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Miljø- og Fødevareudvalget 2021-22
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UNIVERSITY OF COPENHAGEN

FACULTY OF SCIENCE

CENTRE FOR MACROECOLOGY,

EVOLUTION AND CLIMATE

PhD Thesis

Erik Buchwald

Analysis and prioritization of future

efforts for Danish biodiversity

– with particular regard to Nature Agency lands

Academic advisors:

Associate Professor Jacob Heilmann-Clausen

Professor Carsten Rahbek

Submitted: February 2018

UNIVERSITY OF COPENHAGEN

FACULTY OF SCIENCE

CENTRE FOR MACROECOLOGY,

EVOLUTION AND CLIMATE

PhD Thesis

Erik Buchwald

Analysis and prioritization of future

efforts for Danish biodiversity

– with particular regard to Nature Agency lands

Academic advisors:

Associate Professor Jacob Heilmann-Clausen

Professor Carsten Rahbek

Submitted:

February 2018

Department:

Natural History Museum of Denmark,

University of Copenhagen,

Centre for Macroecology, Evolution & Climate (CMEC)

Erik Buchwald

Analysis and prioritization of future efforts for Danish

biodiversity

– with particular regard to Nature Agency lands

Industrial PhD-project undertaken from March 2015 to

February 2018 with the aim to study and analyze where and

how Denmark can make an efficient effort to stop the decline

in biodiversity – particularly on the c. 2000 km

of Denmark

managed by the Danish Nature Agency.

Associate Professor Jacob Heilmann-Clausen

Professor Carsten Rahbek

Head of Division Mads Jensen

Regional Forest Director Jens Bjerregaard Christensen

28. February 2018

Photos ©: Black woodpecker

Dryocopus martius

(Lassi

Rautiainen), silver-washed fritillary

Argynnis paphia

(Erik

Buchwald), the fungus

Pycnoporellus fulgens

(Jacob

Heilmann-Clausen) and lying dead beech-tree (Erik

Buchwald).

Author:

Title:

Topic description:

Academic advisors:

Agency advisors:

Submitted on:

Front cover:

Preface

This thesis is the culmination of a three-year Industrial PhD project conducted in co-

operation between the Danish Nature Agency (Ministry of Environment and Food of

Denmark) and the Centre for Macroecology, Evolution and Climate (CMEC, Natural

History Museum of Denmark, University of Copenhagen). The project has been

supervised by Associate Professor Jacob Heilmann-Clausen and Professor Carsten

Rahbek. From the Danish Nature Agency, Head of Division Mads Jensen and Regional

Forest Director Jens Bjerregaard Christensen have acted as agency advisors.

The thesis consists of three parts. First a synopsis with background and overview of the

work. The second part consists of 4 chapters with the primary written products of the PhD

project - two manuscripts for peer-review and two published reports for the agency and

Danish stakeholders. These chapters form the core part of the PhD thesis. Finally, the

third part consists of three appendices with other articles published or in peer-review

during the project period, but not part of the original Industrial PhD plan.

In addition to the research output presented in this thesis, up to half of my time in the

project period has in line with the Industrial PhD obligations been used for consultations,

meetings, excursions, workshops and other dissemination of preliminary and final results

of the work to the Danish Nature Agency in order to make quickest possible use of the

results, so they could benefit management and conservation actions already during the

project period.

At the same time I have also been heavily involved with fieldwork for the third Danish

Atlas of Breeding Birds, been member of the board of the national Danish Botanical

Society, and undertaken quality assurance of a number of taxon groups for the citizen

science project www.fugleognatur.dk.

Erik Buchwald

Copenhagen, 28th February, 2018

MOF, Alm.del - 2021-22 - Endeligt svar på spørgsmål 532: MFU spm. om de 626 truede arter inkluderer arter, som lever i skoven, fordi deres foretrukne levested ikke findes, eller om ministeren anser, at alle de 626 arter er obligate skovarter, til miljøministeren MOF, Alm.del - 2021-22 - Endeligt svar på spørgsmål 532: MFU spm. om de 626 truede arter inkluderer arter, som lever i skoven, fordi deres foretrukne levested ikke findes, eller om ministeren anser, at alle de 626 arter er obligate skovarter, til miljøministeren

Contents

Summary ............................................................................................................... i

Dansk Resumé ......................................................................................................ii

Acknowledgements ............................................................................................... iii

SYNOPSIS ........................................................................................................... 3

CHAPTER 1 - How well do conservation sites based on annex II species of the

EU habitats directive cover other threatened species relevant for the 2020

biodiversity targets? ............................................................................................ 23

CHAPTER 2 - Marxan with zones applied to prioritize management for

threatened forest species in Denmark to improve reaching 2020 biodiversity

targets ................................................................................................................. 69

CHAPTER 3 - Identification of species and habitats in the 2020 biodiversity

targets ............................................................................................................... 129

CHAPTER 4 - Possibilities for better achievement of 2020-targets for threatened

species on Nature Agency lands ...................................................................... 161

APPENDIX I - Where are Europe´s last primary forests? ................................. 277

APPENDIX II - Development in the number of breeding bird species in Denmark

1800-2012 ......................................................................................................... 367

APPENDIX III - Where should we do what in State Forests? ........................... 375

Summary

It has been widely documented that biodiversity both globally and in Denmark is in

decline and that many species are threatened with extinction. Analyses in 2011-2013 of

the status of Danish biodiversity and of the recent efforts to help it pointed out the need

for a more focused approach to management and conservation, targeted more at

threatened species, and recognizing that forests are of major importance for a very large

proportion of the threatened species. A large part of the analyses were based on 10 x 10

km grid cell data for Denmark and thus difficult to apply in practical management.

The aim of this Industrial-PhD-project was to investigate and analyze, where and how a

more specific and effective effort can be done to achieve the political 2020-targets about

halting the decline in biodiversity – especially on the c. 5% of Denmark managed by the

Nature Agency. First of all I delimited the more than one thousand species of animals,

plants and fungi that influence whether Denmark can reach the EU and UN biodiversity

targets for 2020 (chapter 3). I then compiled and quality checked data on the occurrences

of these species 1991-2015 and analyzed to which extent each species occurred in Special

Areas of Conservation (SACs) or in other types of nature protected areas (chapter 1).

Using the optimization program

Marxan with zones

I studied how species could be

protected better in a cost-effective way, and related the results to the government´s

"Nature-package" decision from May 2016. It determined that 10,000 ha of new

untouched forests and 3,300 ha of new forests protected as "other biodiversity forests"

shall be designated on Danish Nature Agency lands (chapter 2 and 4). Results and data

were supplied to the Nature Agency as soon as available in the project. The analyses in

chapter 4 thereby became a significant part of the basis for the proposal including more

than 13,300 ha of new protected biodiversity forests, which the Nature Agency publicly

announced on February 2nd 2018 as implementation of the "Nature-package" decision.

Unrelated to the original PhD plan, I was invited to contribute to a study "Where are

Europe´s last primary forests". To my joy the authors had chosen definitions and

terminology published by me in 2005 as the most appropriate for the study (Appendix I).

Similarly I co-authored an article on the development in number of breeding bird species

in Denmark since 1800 (Appendix II) and was invited to write a piece on my PhD project

to a thematic journal issue on Biodiversity and Forest (Appendix III).

Dansk resumé

Det er veldokumenteret, at biodiversiteten både globalt og i Danmark er i tilbagegang, og

at mange arter er truet af udryddelse. Analyser i 2011-2013 af den hidtidige status og

indsats for dansk biodiversitet pegede bl.a. på behov for en tilgang til forvaltning og

beskyttelse, som er mere målrettet truede arter, men også på at skovene er af afgørende

betydning for en meget stor andel af de truede arter. Analyserne var i høj grad baseret på

data for Danmark opdelt i 10 x 10 km kvadrater og derfor vanskelige at omsætte i praksis.

Dette Erhvervs-PhD projekts formål var at undersøge og analysere, hvor og hvordan man

mere konkret kan gøre en effektiv indsats for at nå de politiske 2020-mål om at hindre tab

af biodiversitet – særligt på de ca. 5 % af Danmark, som forvaltes af Naturstyrelsen. I

første omgang identificerede jeg de over tusinde arter af dyr, planter og svampe, der er af

betydning for, om Danmark kan nå de i FN og EU politisk vedtagne 2020-mål om

biodiversitet (chapter 3). Jeg samlede og kvalitetssikrede derpå data om arternes

forekomst 1991-2015 og analyserede i hvor høj grad hver art forekom i habitatområder

eller i andre typer beskyttet natur (chapter 1).

Ved hjælp af optimeringsprogrammet

Marxan with zones

undersøgte jeg, hvordan arterne

på omkostningseffektiv vis kunne beskyttes bedre, og satte det i relation til regeringens

Naturpakke fra maj 2016, som fastlagde, at der på Naturstyrelsens arealer skal beskyttes

ekstra 10.000 ha urørt skov og 3.300 ha ekstra anden biodiversitetsskov (chapter 2 og 4).

Delresultater og data blev løbende stillet til rådighed for Naturstyrelsen. Analyserne i

chapter 4 blev dermed en væsentlig del af grundlaget for de forslag til mere end 13.300 ha

ny beskyttede biodiversitetsskove, som Naturstyrelsen sendte i offentlig høring den 2.

februar 2018 som udmøntning af Naturpakken.

Udenfor den oprindelige PhD plan blev jeg i forløbet inviteret til at bidrage til et

europæisk studie om længe urørte naturskove, som til min glæde baserede sig på

definitioner og terminologi, som jeg havde publiceret i 2005 (Appendix I). Tilsvarende

medvirkede jeg til en opdateret artikel om udviklingen i antallet af danske ynglefugle

siden 1800 (Appendix II), og jeg blev inviteret til at skrive om PhD projektet i et

temanummer om biodiversitet og skov (Appendix III).

Acknowledgements

This study has only been possible due to support and help from a large number of people,

who I am greatly indebted and grateful to. First of all I want to thank my main supervisor

Jacob Heilmann-Clausen,

who has helped me in all sorts of ways, has improved the

quality of my work, and allowed me to share office and ups and downs for the past years.

Thanks also to co-advisors

Carsten Rahbek, Mads Jensen

and

Jens Bjerregaard

Christensen

for support, good discussions and for believing in me and my work.

Right since my first ideas in 2014 fostering the project I am hugely grateful for non-

ending support from my superiors

Mads Jensen

and

Peter Ilsøe

at the Danish Nature

Agency who allowed me to apply for the Industrial PhD grant in order to improve the

scientific basis for nature conservation in the agency.

The Danish Nature Agency

and

Innovation Fund Denmark

(industrial Ph.D. grant no. 4135-00145B) are sincerely

thanked for granting the economic support. Also in the implementation period all

colleagues and superiors in the agency deserve my sincere thanks for total commitment to

support me in all ways necessary to make the project a success for me. It has been a

particular delight to discuss issues and receive help from my close colleagues

Erling

Krabbe, Rune Hauskov Kristiansen, Karsten Wandall, Mogens Krog, Per Lynge

Jensen, Erik Kristensen

and

Bjørn Ole Ejlersen,

besides enjoying a beer, snack and

laugh with them when occasion gave the possibility.

Peter Ilsøe, Signe Nepper Larsen,

Mads Jensen

and

Jens Bjerregaard Christensen

finally deserve great respect and

thanks for whole-hearted leadership supporting the use of results from scientific

conservation planning.

No less gratitude goes to my major workplace for the last three years - Professor

Carsten

Rahbeks

wonderful CMEC,

Centre for Macroecology, Evolution and Climate.

Carsten

has succeeded in building a unique and inspiring center with some of the world´s leading

researchers in these fields, which I am grateful to have been a part of. Thanks, Carsten,

for giving me the chance to try to learn from you, and to translate science into

recommendations for applied conservation actions.

At CMEC I have received invaluable help and support from many colleagues who are all

thanked, but special thanks must go to

Anders H. Petersen

(Worldmap software, spatial

conservation planning and numerous fruitful discussions),

Bjørn Hermansen

(GIS and

IT wizard),

Louis A. Hansen

(Worldmap software),

Lotte Nymark Busch Jensen

iii

(Indesign and layout),

Lykke Pedersen

(PhD practicalities and timing) and

Jonathan

Kennedy

(R programming help). Erik Buchwald acknowledges the

Danish National

Research Foundation

for funding for the Center for Macroecology, Evolution and

Climate (grant-number DNRF96). For help with statistical problems I thank

Marcussen

at the Laboratory for Applied Statistics, University of Copenhagen for helpful

discussions and ideas at short notice.

This study would not have been possible without the immense data collection efforts of

numerous persons active in

Birdlife Denmark, Lepidopterological Society, Danish

Mycological Society, Danish Botanical Society, Entomological Society of Fünen,

Spiders of Denmark

(online database), the

Natural History Museum of Denmark,

the

Danish Nature Agency

and Birds & Nature web-database (Fugle

& Natur

license

B05/2014). All persons and organizations involved deserve sincere thanks for their efforts

and for permitting use of data.

For help with extracting data from major data collections I would like to specifically

thank

Thomas Vikstrøm

and

Timme Nygaard

at Birdlife Denmark and

Jesper

Fredshavn, Jesper Bladt, Jesper Moeslund

and

Rasmus Ejrnæs

at DCE (Danish

Centre For Environment And Energy). Species experts

Jan Pedersen

(Coleoptera),

Nikolaj Scharff

(Arachnida) and

Ulrik Søchting

(Lichens) at the Natural History

Museum of Denmark are thanked for help and expert consultations.

Isabel Calabuig,

your great help when uploading my data to GBIF is much appreciated. Thanks also to

P.H. Williams

for kindly providing the WORLDMAP software and to

H. Possingham

and

M. Watts

for providing the Marxan with zones software.

Last but not least my deep-felt thanks and devotion go to my family.

Hanne

you are a

constant help, inspiration and wonderful companion and support. It is a great joy for me

when we together set out to explore the many fascinating nature sites and species of

Denmark (and Sweden and India etc.). You brighten up all my days with your smile.

Morten,

thanks for nice father-and-son talks over lunch here at Campus, while we both

were working on our PhD-studies on either side of the Campus lawn. You proudly earned

your PhD-title in December 2017 just a few months before my deadline.

Lasse,

you have

also made me proud by great results in your education and career, and by overcoming the

obstacles which come in your way - even when they seem impossible. Finally I am so

grateful to my parents

Vagn

Kirsti

for their love and support in so many ways, and for

all the many wonderful dinners you have invited to,

Kirsti.

SYNOPSIS

Analysis and prioritization of future efforts

for Danish biodiversity

– with particular regard to Nature Agency lands

Erik Buchwald

Previous page: The author at fieldwork (Photo by Hanne Schüssler).

Synopsis

Introduction

It has been addressed widely in the literature that decline of global biodiversity including

extinction of threatened species is a pressing issue (e.g. Dirzo et al. 2014; Newbold et al.

2015; Pimm et al. 2014; Steffen et al. 2015). This has lead to political agreements of

which the United Nations (UN) Convention on Biological Diversity (CBD) 1992 and its

follow-up decisions are of major importance (Venter et al. 2014, 2017; Watson et al.

2016). The COP 10 meeting (UN-CBD conference of the parties) at Nagoya 2010

adopted 20 so-called Aichi targets among which target 12 is especially important for the

present PhD project: "By 2020 the extinction of known threatened species has been

prevented and their conservation status, particularly of those most in decline, has been

improved and sustained" (CBD 2010).

It has been shown that biodiversity represents a very large value for society which can be

measured by the billions of Danish kroner (Petersen et al. 2012). Loss of species similarly

represents an economic loss for society. Therefore prevention of loss is highly relevant

both for economic benefits, but not least because of the ethical and moral aspects of

nature protection, and giving opportunities for people to enjoy a rich and diverse nature.

For Denmark and the European Union (EU) implementation of the 'Habitats Directive'

(EU 1992) is one major effort to deliver on the CBD and Aichi targets together with the

'Birds Directive' (EU 1979). The EU has specified six biodiversity targets for 2020 which

to a high degree relate to implementation of those two directives (EU 2011). A major part

of the efforts from the Habitats Directive targets management and conservation of Special

Areas of Conservation (SACs) as part of the Natura 2000 network (Evans 2012;

Maiorano et al. 2015). The SACs were selected as sites with occurrence of species listed

on Annex II of the directive or on the basis of presence of threatened or characteristic

habitats listed on Annex I (Battisti & Fanelli 2015; van der Sluis et al. 2016).

But it was an open question how well SACs and other protected areas of Denmark cover

the threatened species relevant for Aichi target 12, and similarly it was not known if the

ecological requirements and preferences of the threatened species matched the

Synopsis

conservation regime at their occurrence sites. In short there was no certainty that the

protection regime would deliver. In the light of recommendations from previous studies

(Ejrnæs et al. 2011; Johanssen et al. 2013; Petersen et al. 2012), it was decided in this

PhD project to focus on terrestrial and amphibious species, on forests and on the lands of

the Danish Nature Agency, and study the possibilities for improved evidence-based

conservation.

Thesis outline - research problem

Solving the problem of selecting the most appropriate and cost-effective network of sites

for protection and conservation of threatened animals, plants, fungi, and other segments

of biodiversity is an issue for Systematic Conservation Planning (Margules & Pressey

2000; Moilanen et al. 2009;Venter et al. 2014, 2017; Watson et al. 2016). It requires a lot

of data and understanding of the distributions, ecology, and sites of the threatened

species. How many and how large sites are necessary? Which type of management and

protection is necessary? Are a few large sites better than many small sites?

The first studies on systematic conservation planning in Denmark are from around the

turn of the century and were undertaken at a coarse geographical scale and with relatively

few species included (Lund 2002; Lund & Rahbek 2002). A recent similar study

regarding Danish forests recommended 75.000 ha protected as untouched forest (Petersen

et al. 2016). An important aspect in this context is that these and many other planning

studies only discern between "protected" and "unprotected" in the planning algorithms,

which in many cases is too simplistic and may overestimate the economic costs of

conservation (Watts et al. 2009; Wilson et al. 2010). Wilson et al. (2010) found that

accounting only for the contribution of protected areas could overestimate the required

expenditure by 15 times, and the area requiring strict protection by almost 50 times.

The previous studies in Denmark were only able to analyze species occurrences at a

coarse scale of 10 x 10 kilometers and the latest study (Petersen et al. 2016) did not

include data on existing protection. Moreover these studies only included a relatively

small proportion of the more than 1500 threatened species present in Denmark. For all

these reasons the results of the previous studies were not applicable in practical nature

management or conservation which was already realized and highlighted in the studies.

Synopsis

Objectives

The main aim of the PhD project was to take Danish systematic conservation planning to

a more detailed level in order to make it possible for the Danish Nature Agency to

implement evidence-based actions in a cost-effective and prioritized way to help halt the

decline in biodiversity as stated in the political 2020-targets of UN, EU, and Denmark.

Recommendations were to be based on systematic conservation planning at a sufficiently

detailed geographical level to match management needs.

Chapter 1 - How well do conservation sites based on annex II species of the

EU Habitats Directive cover other threatened species relevant for the 2020

biodiversity targets?

This manuscript in preparation for submission to Biological Conservation for peer-review

investigated the performance of Habitats Directive Annex II species as indicators of

threatened species in site selection, and evaluated the effectiveness of actual protected

areas (PAs) using complementarity based algorithm-optimization and gap analysis. Even

though the species groups showed different distribution patterns, the Annex II species

performed significantly better than random in identifying areas important for other groups

using complementarity, both for globally threatened species (Table 1), for species

threatened at the EU level, and also for the long list of nationally threatened species.

Table 1.

Globally threatened species occurring in Denmark 1991-2015 (excluding fully

aquatic species). Species marked * are also on annex II of the Habitats Directive.

Species name (and taxon group)

Podiceps auritus

(bird)

Ampedus hjorti

(beetle)

Dytiscus latissimus

(beetle)*

Graphoderus bilineatus

(beetle)*

Formicoxenus nitidulus

(ant)

Phyllodesma ilicifolia

(moth)

Dolomedes plantarius

(spider)

Vertigo moulinsiana

(snail)*

Hygrocybe citrinovirens

(fungus)

Hygrocybe ingrata

(fungus)

Tricholoma acerbum

(fungus)

Number of 10km grid

cells with occurrence

% of presence records

in SAC polygons

Synopsis

The Danish Special Areas of Conservation (SACs) which were selected based on a

limited number (n=27) of EU Habitats Directive Annex II species had a much higher

coverage (42 – 64%) of all known occurrences of 1344 threatened species than expected

based on the SAC coverage of Denmark (8.6% of land area). This applied for both 137

internationally and 1207 nationally threatened species including less studied groups like

fungi, mosses, and spiders. With generally three to eight times higher than 'baseline'

coverage these figures are promising and underline that the designation of Danish SACs

has been rather successful also for less well-known taxon groups and for species of global

concern which were not direct targets. In total, 88% of all threatened species occurred in

at least one SAC, and 42% of the 156 species missed by SAC polygons had occurrence

instead in nationally protected areas of IUCN category I – IV (Dudley 2008).

Besides the indicator value of Annex II species there can be several reasons why the

coverage of threatened species in the SAC network is so high. Importantly, Denmark is so

heavily impacted by agriculture, urban areas, and infrastructure that SAC designation was

more or less restricted to the c. 30% of the land with a more natural character. Hence, the

random sets inevitably included an average of 70% land lacking natural habitats.

However, the higher coverage of threatened species in the actual SAC set compared to the

optimal set based on Annex II species suggests that the Danish designation process was of

high quality targeting areas with high value for Danish nature in general, with low

intrusion from economic interests.

Excluding Brexit UK, all other EU countries have designated more of their country than

Denmark as Natura 2000 (including SACs). As the countries have had to follow the same

principles of designation laid down in the Habitats Directive, a similar or higher coverage

of threatened species is expected in those countries. Designation of PAs on the basis of

multi-taxon species proxies is thus recommended as being efficient. This is in line with

other studies, even though few have documented as high efficiency as here.

Synopsis

Chapter 2 - Marxan with Zones applied to prioritize management for

threatened forest species in Denmark to improve reaching 2020 biodiversity

targets

This manuscript in preparation for submission to PLOS ONE for peer-review has focus

on selection of a cost-effective set of protected forests using a zoning approach. Refining

"protection" to different categories or management "zones" relevant to the ecological

requirements of different threatened species is highly relevant in systematic conservation

planning, since some species require less than strict untouched protection (e.g. Bernes et

al. 2015; Brunet et al. 2010; Lindenmayer & Franklin 2002; Lundström et al. 2016;

Peterken 1996). Several available methods and software systems for Systematic

Conservation Planning were screened including Zonation, C-Plan, Marxan, and integer

programming methods (Moilanen et al. 2009). This resulted in the choice of Zonae

Cogito (Segan et al. 2011) incorporating Marxan with Zones (Watts et al. 2009) as

analysis platform, due to 1) their good documentation, 2) widespread use, 3) ability to

work efficiently with several different types of protection / management at the same time

(zones), 4) ability to simultaneously solve the planning problem for many hundred

species distributed at many hundred different sites, and 5) incorporation with GIS to

support an iterative interactive planning process (Segan et al. 2011; Watts et al. 2008;

Wilson et al. 2010).

As an input to optimize cost-effective selection of new forest reserves we used presence

data 1991-2015 for 626 forest species threatened at global, EU or national level. The

species represented three biological kingdoms and presences were extracted from data

sources comprising a total of 22 million species records. Species were grouped by

ecological requirements and preferences based on data in the Danish official red list

database and adjusted by expert consultation. Four Marxan zones were then defined to

represent different protection regimes relevant for the different species groups and with

various cost assumptions: 1) Normal, 2) Conifer woods with extra protection, 3) Active

protection of semi-open deciduous woods, and 4) Untouched (minimum-intervention)

deciduous woods. Targets were set to at least 3 or 5 representations in zones matching

ecological preferences for each species.

26 scenarios were run in Marxan with zones to inspect sensitivity to input assumptions

varying 1) cost as area or money, 2) cost of zone 2 and 3 compared to normal, 3)

Synopsis

exclusion of conifer affiliated species or of species not in decline, and finally 4) locking

already fully protected forests a priori to the relevant zone 4. In addition, three control

scenarios were run to test effects of including poorly known species and to evaluate the

potential cost reductions achieved by working with protection zones. Success rate for

scenarios was assessed as 1) cost (net income foregone) and 2) target achievement for 304

threatened species in decline and therefore top priority.

Results showed that, if carefully planned, the government decision of designating 13,300

ha of new protected forests (10,000 ha untouched and 3,300 ha other protection types) in

the Danish State Forests should be able to provide most threatened species with relevant

protection in all known sites for rare species (less than five occurrences in state forests),

and in at least 5 sites for less rare species. Depending on cost assumptions, this implies a

38-46% loss of annual net income from wood harvest in the State Forests. Marxan with

zones proved to be very effective and is highly recommended for solving this type of

conservation problem.

It would have been optimal to analyze private and State forests together, and this has been

done by Petersen et al. (2016) who analyzed priority forest areas countrywide for

Denmark at 10 km grid cell scale. The present study used more detailed input data

available for State Forests but not available for private forests.

Chapter 3 - Identification of species and habitats in the 2020 biodiversity

targets.

This chapter was published as an industrial PhD-report in 2016. It analyzed the UN and

EU biodiversity targets for 2020 in order to identify which species and habitats would

influence the possibilities to reach the targets in Denmark with special focus on

possibilities on Danish Nature Agency lands. The identified species delimited the study

species used in other parts of the PhD-project.

At UN level Aichi target 12 was of primary importance for identification of focal species:

"By 2020 the extinction of known threatened species has been prevented and their

conservation status, particularly of those most in decline, has been improved and

sustained" (CBD 2010). At EU level six biodiversity targets have been specified for 2020

of which target 1 was primarily important for identification of focal species: "To halt the

Synopsis

deterioration in the status of all species and habitats covered by EU nature legislation and

achieve a significant and measurable improvement in their status so that, by 2020,

compared to current assessments: (i) 100% more habitat assessments and 50% more

species assessments under the Habitats Directive show an improved conservation status;

and (ii) 50% more species assessments under the Birds Directive show a secure or

improved status." (EU 2011).

Danish Natura 2000 plans have been adopted for SACs covering the habitats relevant for

EU target 1 and management has already been ongoing for several years to improve their

conservation status based on previous work and assessments (Larsen & Lentz 2016). For

the PhD project it was therefore decided to focus on species, as only EU annex II species

are directly covered by the Natura 2000 plans. Because species are linked to certain

habitats, habitats were included indirectly via species.

Table 2 gives an overview of the 1932 Danish species which are listed as threatened on

global or national red lists or listed on annexes to the EU nature directives: "2020-target-

species". These species were the potential study species. If records were known /

available from Denmark in the period 1991 - 2015 they were realized study species.

Table 2.

Overview of the 1932 species identified * (from chapter 4, tab. 4.)

Taxon \ # species

Mammal

Bird

Reptile

Amphibian

Insect

Other invertebrate

Fungi

Lichen

Moss

Plant

Total

DK_RE

142

287

DK_threatened

164

Global_threatened

Total

654

552

396

144

1932

498

528

293

108

1467

* A number of species are threatened at more than one geographic level. They are only listed in

the table at the most global level. DK = Danish red list. RE = regionally extinct. Threatened

comprises the categories critically endangered (CR), endangered (EN) and vulnerable (VU). EU

species comprise annex 1 of the Birds Directive and annex II, IV and V of the Habitats Directive.

Synopsis

The number of realized study species was 1371 in chapter 1, but 1378 in chapter 4 due to

seven species of fungi, which were not identified as threatened from the start because of

initially overlooked name mismatches between the database with red list status and the

database with taxonomy. Of the 1378 realized study species 1066 occurred on Nature

Agency lands, comprising 626 forest species (study species in chapter 2) and 440 non-

forest species.

Chapter 4 - Possibilities for better achievement of 2020-targets for

threatened species on Nature Agency lands.

This is the main industrial product of the PhD together with electronic data and GIS files

delivered to the Nature Agency. The report (published 14th February 2018) includes for

both forest and non-forest study species the background, data, methods, results,

discussion, biases, recommendations, and conclusions of the work dealing with how to

better achieve the 2020-targets for threatened species on Nature Agency lands. Most

substance from chapters 1 and 3 is included more or less summarily, while the substance

of chapter 2 is included in detail and folded out. A number of other issues relevant for

practical conservation are discussed and recommendations given.

Species were split in priority 1 - 5 based on their reported status and trend, with priority 1

being globally threatened species, priority 2 and 3 threatened species reported in decline,

and priority 4 and 5 being species with no reported decline and thus lower priority in

relation to Aichi target 12. Priority 4 and 5 were grouped as priority class B (lower

priority) while 1, 2 and 3 were taken as priority A (high priority).

One result from chapter 2 was that it was impossible in the Marxan analyses based on

whole forests to reach target achievement for all species or for all priority A species.

Chapter 4 therefore included further detailed GIS-analysis of species records and

preferences at internal forest scale in order to further optimize target achievement. This

showed that by zoning internally in less than 50 forests it is possible to provide all priority

A species with relevant protection in all known sites for rare species (less than five sites

in state forests), and in at least 5 sites for less rare species.

For non-forest species the analyses identified 370 sites on Nature Agency lands with

priority A species which had missing or low protection by IUCN category 1-2 and the

Synopsis

Natura 2000 network in spite of hosting more than 1% of the national total of records

1991 - 2015 for the said species. These sites were listed and delivered with details to the

agency in May 2017 in order to be included in planning the protection and conservation

management for the sites.

Finally recommendations were given for practical conservation and management for

forest and for open habitats separately based on the authors´ synthesis of cited literature,

supplemented with unpublished experience from the implementation of the "Strategy for

Natural Forests" (Anon. 1994), and with the analyses and results of the total work in all

chapters. The recommendations for forests were related to the 2016 government decision

to designate 10,000 ha of new untouched forests and 3,300 ha of "other biodiversity"

forest reserves (Anon. 2016). The decision included a transitional period of 10 and 50

years respectively for deciduous and coniferous forest regions, so recommendations were

divided in before and after the commencement of untouched (minimum-intervention)

status.

A number of the recommendations relate to counteracting human-induced detrimental

changes to ecosystem functions and structures over time, i.e. some of the reasons behind

declines of threatened species are too many nutrients, too few large herbivores, too little

deadwood, and much lower levels of fire and water in most of the landscape compared to

more natural former time periods (figure 1).

Figure 1.

An overview of some ecosystem functions and structures which have been

changed by humans over time to the detriment of many now threatened species.

Synopsis

It was concluded that Marxan with zones is a good but very data demanding tool to help

cost-efficient selection of sites for biodiversity protection. The most cost-efficient

solutions of the Marxan scenario runs selected an area of forest for untouched protection

similar to the ambition level of new 10,000 ha in the government decision. On the other

hand the area selected for other types of forest protection was higher than the 3,300 ha

government target for "other biodiversity forest". A large proportion of the forest areas

selected by Marxan for other types of forest protection were already protected in IUCN

categories 1-4 (Dudley 2008) or based on species needing conifers, which should be able

to be provided for in State Forests without necessarily being designated as "other

biodiversity forest".

Depending on assumptions the economic costs of the most cost-efficient solutions varied

between 38 and 46% of net annual income from wood harvest for the State Forests. This

cost can be lowered and target achievement improved by zoning internally in forests with

species showing opposing habitat preferences.

For open habitats it was concluded that a continued but more targeted approach to

conservation management on Nature Agency lands should be done for a list of identified

sites with priority species enjoying only low levels of protection. Also other sites for the

same 33 listed species and for 277 nationally threatened species in decline should be

given high priority. This large number of species can be difficult to cope with at national

level, but at forest level the species number is always low (ten or less), so management

can go into more detail for each species and target management.

Appendix I - Where are Europe´s last primary forests?

This article was the result of an invitation from the lead author Francesco Sabatini to

contribute to his work on building a database and overview of primary forests in Europe.

It has been in peer-review for Diversity and Distributions, and has been invited for

resubmission after revisions.

Primary forests, in short being naturally regenerated forests of native species where there

are no clearly visible indications of human activities and the ecological processes are not

significantly disturbed, are becoming rare as forestland globally is cleared for agriculture

or put under active management (Mackey et al. 2015; Potapov et al. 2017). Given their

Synopsis

irreplaceability and unique qualities, protecting primary forests is a global concern

(Mackey et al. 2015). Not only are they cherished for their wild nature (Navarro &

Pereira 2012) and represent a social perception of untouched nature (Schnitzler 2014),

they are also ecologically important in regions where forests are highly fragmented

(Vandekerkhove et al. 2009).

Several more or less concise definitions exist for primary forest. For the purpose of

getting an objective overview and database for Europe, it was decided to use the

hierarchical terminology on more or less natural forests developed by Buchwald (2005).

The aim was to i) compile the first European-scale map of known primary forests, ii)

analyze the spatial determinants characterizing their location, and iii) locate areas where

as yet unmapped primary forests are likely to occur. We aggregated data from a literature

review, online questionnaires, and 32 datasets of primary forests. Boosted regression trees

were used to explore which biophysical, socioeconomic and forest-related variables could

explain the current distribution of primary forests. Finally, the relative likelihood of

primary forest occurrence at a 1-km resolution across all of Europe was predicted and

mapped.

Data on primary forests were frequently incomplete or inconsistent among countries.

Known primary forests covered 1.4 Mha in 32 countries (0.7% of Europe’s forest area).

Most of these forests were protected (89%), but only 46% of them strictly. Primary

forests mostly occurred in mountain and boreal areas, and were unevenly distributed

across countries, biogeographical regions, and forest types. Unmapped primary forests

likely occur in the least accessible and populated areas, where forests cover a greater

share of land but wood demand historically has been low.

The current distribution of primary forests is the result of centuries of land-use dynamics

and forest management. The conservation outlook for these forests is uncertain, however,

since their small and fragmented patch structure makes them prone to extinction debt and

human disturbance. Predicting where unmapped primary forests likely occur could aid

protection efforts and prevent further loss.

Synopsis

Appendix II - Development in the number of breeding bird species in

Denmark 1800-2012

This article resulted from a contact from me to the three other authors who in 2013

presented a paper in the Birdlife Denmark journal (Romdal et al. 2013) on development

in the numbers of species of Danish breeding birds 1800-2012. I had noted problems in

their data which made re-analysis of an updated extended dataset relevant. The authors

and the journal editor agreed this was necessary and I helped getting the data-set, article,

and conclusions right and up-to-date, which resulted in new categorization for eight

species.

The updated results confirmed that numbers of breeding bird species in Denmark

increased significantly from 149 in 1800 to 178 in 2012. Split into habitat types, the new

results showed a continued increase in numbers of species in forest and wetland habitats

in recent years in contrast to results in the first article (Romdal et al. 2013).

The largest decline in species numbers occurred within the group heathland /meadow

/commons. The general stagnation in species richness reported in the last 20 years by

Romdal et al. (2013) was not supported. The number of non-persecuted species, however,

did show a statistically significant decline in the last 20 years. By contrast, the species

categorized as (previously) persecuted increased significantly in the revised analysis over

the last 20 years – indicating a comeback from an all-time low around 1900.

Enhanced legal protection, landscape change, nature management, and a change in

mindset among the general public are believed to be some of the major drivers

contributing to the long term increase in Danish bird species richness.

Appendix III - Where should we do what in State Forests?

As a kick-off presentation of the industrial PhD-project this article was published in 2015

in a thematic issue of moMentum+ on Biodiversity and Forest. The idea and objectives of

the PhD-project were presented and some preliminary analyses; among others that forest

species comprise an absolute majority of threatened species, but that relatively less forest

species are threatened compared to species of more open habitats like grasslands,

wetlands, and heaths etc. (Table 3).

Synopsis

Table 3.

Overview of species with habitat information in the Danish red list database

(Wind & Pihl 2010).

# species per habitat

Regionally

extinct

(RE)

137

152

289

2.7

5.1

Threatened

(CR, EN,

VU)

881

645

1526

17.5

21.5

Near

threatened

(NT)

240

192

432

4.8

6.4

Least

concern

(LC)

3322

1842

5164

66.1

61.3

Data

deficient

(DD)

446

175

621

8.9

5.8

Total

Forest species

Non-forest species

Total

Percentages

% of forest species

% of non-forest species

5026

3006

8032

100.0

100.1

Perspectives

The analyses and results of this study became a significant part of the basis for the new

State Forests proposed for protection as implementation of the 2016 "Nature-package"

decision. The proposal was publicly announced on February 2nd 2018 (Anon. 2018). It

included more than 13,300 ha of new protected biodiversity forests, with a very great

overlap to the most cost-effective forest selections resulting from solutions in chapter 2

and 4. The "Nature Package" also includes actions and subsidies for forest biodiversity in

non-state forests with a target level of 3,300 ha new protected forests.

Table 3 and Appendix II are in line with the general picture (e.g. Ejrnæs et al. 2010;

Petersen et al. 2012) that decline of biodiversity in Denmark is also a major problem in

open semi-natural habitats like grasslands and heaths, and in the agricultural landscape in

general. In view of the major new effort for forest biodiversity the more open habitats

would seem to be a good candidate for a similar evidence-based and systematic

conservation planning effort including non-state lands.

Of the 1378 threatened species recorded 1991-2015 in Denmark, 312 species were only

recorded outside State Forest areas, while 74 species were only recorded in State Forest

areas. The nation-wide quality-assured dataset generated by the project with occurrences

of all threatened species 1991-2015 has been published on GBIF, so it can be

incorporated in other types of studies. The occurrence dataset could e.g. be used by

authorities or nature protection organizations for more evidence-based planning and

prioritization of actions all over Denmark for both forest and non-forest species. For a

Synopsis

successful national protection regime conservation action is necessary across ownership

categories.

Fortunately not all is decline for biodiversity. Actions to reverse trends do help, as e.g.

shown in Appendix II for previously persecuted birds, for birds in general and most

significantly for forest birds. A very recent positive sign that conservation actions in

Danish forests have in the last 25 years also helped other threatened species, regards fungi

dependent on dead wood, as several of them are noted to have increased significantly in

that period attributed to higher amounts of dead wood in some forests (Heilmann-Clausen

et al. 2018). With the decided major increase in protected State Forest areas based on data

for all threatened species in Denmark I am convinced that forest biodiversity in Denmark

will thrive even more!

The Nature Agency has offered me to continue work on these issues after my PhD

dissertation by using the results and detailed knowledge from the PhD project in the

actual planning and implementation of conservation and protection of the new designated

biodiversity forests. This makes it possible to make best use of the large amount of data

generated in the project to the benefit of protection of our threatened species. I feel

grateful having been given this chance to make a difference and improve chances of

halting the decline in biodiversity.

Synopsis

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CHAPTER 1

How well do conservation sites based on

annex II species of the EU Habitats Directive

cover other threatened species relevant for

the 2020 biodiversity targets?

Erik Buchwald & Jacob Heilmann-Clausen

(In preparation for Biological Conservation)

Previous page: Aporia crataegi and Adscita statices (photo by Erik Buchwald)

Chapter 1

How well do conservation sites based on annex II species of the EU

Habitats Directive cover other threatened species relevant for the

2020 biodiversity targets?

Erik Buchwald

a, b,

*, Jacob Heilmann-Clausen

Center for Macroecology, Evolution and Climate, Natural History Museum of Denmark, University of

Copenhagen, Universitetsparken 15, DK-2100 Copenhagen, Denmark

The Danish Nature Agency, Ministry of Environment and Food, Gjøddinggård, Førstballevej 2, 7183 Randbøl,

Denmark

* Corresponding author:

E-mail addresses:

[email protected], [email protected], Phone: +45 25 85 35 44

Abstract

Political targets for protection of biodiversity have been accepted at global and EU (European

Union) levels for the year 2020. Networks of protected areas (PAs) have been designated

which are supposed to deliver on the targets. PAs are often designated based on proxy data

surrogating total biodiversity, e.g. data on abiotic environmental or on a set of pre-defined

indicator species. In many cases it remains an open question if such PA networks cover more

than the proxies they were based on. This is not least the case for subtle and less well-known

parts of threatened biodiversity. In this study we investigated the performance of EU Habitats

Directive Annex II species as indicators of threatened species in site selection, and evaluated

the effectiveness of actual PAs using complementarity based algorithm-optimization and gap

analysis. We found that the Danish Special Areas of Conservation (SACs) which were

selected based on a limited number (n=27) of EU Habitats Directive Annex II species had a

much higher coverage (42 – 64%) of all known occurrences of threatened species (n=1344)

than expected based on the SAC coverage of Denmark (8.6% of land area). This applied for

both internationally (n=137) and nationally (n=1207) threatened species including less studied

groups like fungi (n=513), mosses (n=35) and spiders (n=17). In total, 88% of all threatened

species occurred in at least one SAC. All other EU countries have designated more of their

country than Denmark as Natura 2000 (including SACs). As the countries have had to follow

the same principles of designation laid down in the Habitats Directive, a similar or higher

coverage of threatened species is expected in those countries. Designation of PAs on the basis

of multi-taxon species proxies is thus recommended as being efficient. This is in line with

other studies, even though few have documented as high efficiency as here.

Keywords

Denmark, Natura 2000, gap analysis, protected areas, IUCN, indicator species, red list, SAC.

Chapter 1

Introduction

The 1992 United Nations Convention on Biological Diversity (CBD) set ambitious goals to

protect life on Earth. Its Strategic Plan for Biodiversity 2011–2020 further details 20 so-called

Aichi Biodiversity Targets including target 12 aiming at preventing extinction of threatened

species (CBD 2014). However a review of progress (Tittensor et al. 2014) argued that target

12 and many other targets are unlikely to be met unless efforts to meet targets increase.

At the European Union (EU) level the implementation of the 'Habitats Directive' (directive

92/43/EEC) adopted in 1992 is one major effort to deliver on the CBD and Aichi targets,

together with the 'Birds Directive' 79/409/EEC from 1979. The EU has specified 6

biodiversity targets which to a high degree relate to implementation of those two directives.

The EU targets have been evaluated mid-term by the European Environment Agency (EEA

2015) similarly showing that more effort is needed because a high proportion of species and

habitats have an unfavorable conservation status.

A major part of the efforts from the Habitats Directive targets management and conservation

of Special Areas of Conservation (SACs) as part of the Natura 2000 network (Evans 2012;

Maiorano et al. 2015). The SACs were selected as sites with occurrence of species listed on

Annex II of the directive or on the basis of presence of threatened or characteristic habitats

listed on Annex I (Battisti & Fanelli 2015; van der Sluis et al. 2016). The Annex II species

were chosen when drafting the directive annexes as a subset of red listed species assessed to

benefit from site protection in contrast to species protection (Cardoso 2012; Lund 2002). But

it remains an open question if sites designated based on a limited number of focal species also

cover other taxon groups and biodiversity in general, and more specifically if they cover

threatened species relevant for the 2020 biodiversity targets (Cardoso 2012; Maiorano et al.

2015; Petersen et al. 2016; Rodrigues & Brooks 2007).

The question has been studied at EU level, based on analysis of Natura 2000 site overlap with

distribution maps of species (Sluis et al. 2016; Trochet and Schmeller 2013) showing that

several countries and taxon groups had significantly higher effectiveness than if a similar

Natura 2000 area had been chosen at random. Surprisingly other countries and taxon groups

had lower coverage than expected by chance. This is surprising because Natura 2000 sites

were chosen to cover the largest and best nature areas representing a large number of habitats

and species, while areas outside Natura 2000 generally have more intensive land-use and

infrastructure less suited for natural habitats and species (Evans 2012; Petersen et al. 2016).

Chapter 1

It is concerning if the designated Natura 2000 sites do not cover a significantly higher than

random proportion of globally / nationally threatened species, because nature management

resources are highly directed towards Natura 2000 sites in much of the EU.

Lund (2002), in an analysis of species representation (instead of area coverage) showed that

species listed in Annex II of the Habitats Directive performed well as indicators of other

species in site selection based on complementarity – at least in Denmark. Indeed the

effectiveness of the then proposed Sites of Conservation Interest (pSCIs) based on Annex II

species at representing other Danish species was found to be "equivalent to the effectiveness

of the near-optimal solution" and significantly better than random. The study of Lund (2002)

was based on 761 species (including 262 nationally red listed species) of orchids, club

mosses, bats, birds, amphibians, reptiles and five groups of insects. Due to data limitations

several questions remained unresolved, e.g. would the conclusion hold true also for threatened

species in general and for taxon groups not included, like fungi and lichens, and would the

relatively weak representation of orchids be representative of other plants?

Since 2002 the then proposed 194 Danish pSCI sites have been finalized and supplemented

with more sites so that a total of 261 SACs are now designated. The fact that Danish and EU

funding for nature protection has very high focus on Natura 2000 including SAC, might

potentially lead to less funding for globally or nationally threatened species if these are not in

the SAC areas. Much more detailed species occurrence data has been obtained in Denmark

since Lund (2002), so that some of her unresolved questions, e.g. effect of inclusion of other

taxonomic species groups, of data on more plant species or effect of better site-scale data may

now be addressed.

In this paper we re-used the tests and methods recommended by Lund (2002) and several

other authors (e.g. Petersen et al. 2016; Rodrigues & Brooks 2007) to assess how well EU

Annex II species (listed in the Habitats Directive and used for designation of SACs) represent

nationally and globally threatened species based on principles of complementarity.

In addition we performed a gap-analysis to study how well the network of SACs in Denmark

covers species threatened at global, EU and national level respectively. In order to be

comparable to previous studies (Sluis et al. 2016; Trochet and Schmeller 2013) the null-

hypothesis was chosen to be that Annex II species are no better than random at representing

other species groups and that their coverage is therefore no larger than the percentage of

Danish area designated. It should be noted that this null-hypothesis is not optimal because it is

Chapter 1

unlikely to be met in countries with a large agricultural area (like Denmark) if SAC

designation as expected primarily occurs in less heavily influenced areas of the country.

Finally we compared grid-cell-based versus polygon-based analyses of species coverage to

assess the bias introduced by using coarse spatial data, and assessed the proportion of

threatened species not covered by SACS covered by other types of nature protection in

Denmark.

The present study is novel by using comprehensive detailed presence data instead of assumed

presence extrapolated from largescale range maps often at 50 x 50 or 10 x 10 km scale, and by

including all species and taxonomic groups threatened at either global, EU or national level in

Denmark, and finally by being an integral part of the governmental designation process of

new reserves. The government has decided to designate 10 000 ha of new minimum-

intervention forest reserves and more than 3000 ha of other forest reserves to better preserve

Danish forest biodiversity (Anon. 2016a). The findings of this and other studies were included

to direct which areas to prioritize in the new selection process.

Methods

Study area

Denmark covers 43 100 km

in NW Europe and is highly influenced by agriculture and other

human activities. More than 62% of the area is used for agriculture while forests cover 14%,

cities and infrastructure 10%, and semi-natural biotopes like grassland and heaths 9% (Anon.

2016b).

Denmark has designated 261 marine and non-marine SACs based on occurrences of Annex II

species and Annex I habitats. The non-marine parts cover 8.61% of Denmark (n=163). The

selection process was undertaken in 1994-2006 using analogue traditional data on occurrences

of Annex II species and Annex I habitats and without access to the much more complete,

detailed and updated data collated in the present study. The selection process did not include

consideration of other threatened species than those listed on Annex II (Lars Rudfeld pers.

comm.).

Using a not totally precise method to distinguish terrestrial from marine sites, EEA (2016) has

listed Denmark as the EU country with the lowest proportion of terrestrial area (8.3%)

covered by Natura 2000 sites. According to EEA (2016) the terrestrial Natura 2000 cover in

Chapter 1

other EU countries (excluding Brexit UK) range from 11.5 (Latvia) to 37.9% (Slovenia).

These figures include both SACs and SPAs (Special Protection Areas for birds).

Data

Study species (N = 1371) were delimited as being terrestrial or amphibious species known

from Denmark, and being globally (IUCN 2015) or nationally (Wind & Pihl 2010) red-listed

in the high threat categories (RE, CR, EN, VU), plus birds listed on Annex I of the EU Birds

Directive and species listed on annexes II, IV or V of the EU Habitats Directive. These

species are collectively referred to as threatened. Taxonomy and naming was updated to

match the standard species checklist of Denmark maintained by the Danish Biodiversity

Information Facility (DANBIF 2016).

Species occurrence records were collected and compiled from all relevant sources available in

digital formats (more than 22 million records from e.g. ministry databases, NGO citizen

science databases and university & museum databases) supplemented by a few detailed

inventory studies on beetles, spiders or lichens which were digitized as part of the project

(Table A1, electronic suppl. mat.). Only records regarding the study species were used and

only if data on at least year and detailed locality were recorded or could easily be extracted

from supplementary information.

In order to use as up-to-date occurrences as possible without leaving out too many overlooked

extant occurrences, records from the last 25 years were used (1991-2015). For birds only

certain and probable breeding records were used, delimited by the methods applied in the

Danish Breeding Bird Atlas I, II & III regarding activity type and dates of breeding season

(Anon. 2015; Dybbro 1976; Grell 1998).

Records living up the above criteria were stratified according to how precise the locality was

delimited and identified. UTM coordinates of the center of observation were used for precise

GPS records and based on locality name for other records. Locality names and coordinates

were cross-checked and corrected for records with more than one type of locality information.

Locality names were standardized for typing errors etc. and extended with municipality name

in order to separate synonymous names. Up to 15 different sites had the same name, e.g.

Nørreskov (Northwood), which in some cases had led to wrong automatic coordinates being

applied by some data providers.

The data providers had varying levels of quality assurance of their data, so records were

scrutinized as an extra quality control with focus on regionally extinct and very rare species

Chapter 1

without proper documentation, and on uncertain or inaccurate localities. More than 1500

records for study species were thus excluded and a similar number corrected. Examples

include an almost extinct lake-bottom plant noted from forest floor, and long extinct species

recorded outside potential range/habitats and without having comments proving their validity

or being published in other ways. The errors were documented in the files with comments on

excluded records.

Finally the dataset (N= 267 064) was uploaded to GBIF (Buchwald 2018). Other GBIF

species records for Denmark (n=10 245 635, Table A2, suppl. mat.) were checked, but not

used due to being either redundant to more detailed data acquired from the original sources

(87%), having locality precision issues (11%), being datasets for non-study-species (1%) or

lacking year (1%).

Using GIS ArcView the resulting data records were assigned to the 261 SACs and 633 UTM

10 x 10 km grid cells of Denmark (UTM ETRS 1989 zone 32/33N) and to other nationally

designated protected areas matching IUCN protection categories I-IV. GIS-data for SACs

were from the European Environmental Agency (EEA 2016), while nationally protected areas

were 2016 versions of GIS-files of the Danish Nature Agency.

Analysis methods

A rarity-based heuristic algorithm was used to select priority sets of complementary areas in

order to maximize representation of different species given e.g. a certain amount of grid cells

or a given total area to be protected (WORLDMAP software, Williams 1999). Similarly areas

can be selected for a certain minimum number of representations of each species.

A "near-minimum" set was assigned as a minimum set of 10 x 10 km grid cells needed to

represent all EU Annex II species 5 times. This "near-minimum" set was then checked for

number of representations of internationally / nationally threatened species and compared

with the number of representations which could have been included with the same amount of

grid cells chosen optimally ("Near-maximum-coverage" analysis, Williams 1998). Annex II

species were excluded in the lists of internationally / nationally threatened species to avoid

them being both indicator and target (Rodrigues & Brooks 2007). Internationally threatened

species were only included as such, even though many of them are also on the national red-list

as threatened.

By running 1000 random selections (without replacement) of a fixed amount of grid cells,

minimum acceptable effectiveness was assessed. For a focal group to be considered a reliable

Chapter 1

indicator its performance was required to be better than delimited by the upper 95%

confidence limit achieved by 1000 random area selection runs (Araujo 2004; Lund 2002;

Lund & Rahbek 2002; Rodrigues & Brooks 2007).

Efficiency of the Danish SAC designation and coverage of species was assessed by Gap

Analysis (Oldfield et al. 2004; Powell et al. 2000; Scott et. al. 1993) of the 163 non-marine

SACs. A 'SAC set' of grid cells was developed for this purpose: For SACs totally included in

one grid cell, that grid cell was included in the 'SAC set'. SACs crossing grid cell borders

included only one grid cell in the 'SAC set' if that grid cell included all of the Annex II species

present in the SAC. When necessary to represent all Annex II species occurring in a SAC, two

or three grid cells were included (Lund 2002). Totally 172 grid cells of 10 x 10 km were thus

included in the 'SAC set'. Near-maximum-coverage and random analyses were re-run as

described above.

For comparison with the 'SAC set' an "optimal location solution" for a similar number of grid

cells was found using Near-maximum-coverage analysis that maximizes the number of

representations of Annex II species. Lund (2002) described the above methods in detail.

Coverage of species by protected areas was finally assessed at precise scale based on GIS-

analysis of the detailed species occurrence data using polygon data for SACs and other

Danish protected areas matching IUCN protection categories I-IV (Dudley 2008).

ANCOVA was used to compare mean coverage of the taxon- and threat-groups and to

compare with the national percentage of designated SACs. ANCOVA was used instead of

ANOVA because coverage was significantly influenced by the covariate species

commonness. Linear models were thus fitted for each species group to the relationship

between coverage (% of occurrences in SAC polygons) and number of occupied grid cells

(log-transformed). Covariate slopes were also compared using ANCOVA. Reptiles and

amphibians were pooled with mammals to "other vertebrates" in the statistical analyses

because they had small species numbers (N = 1 and 11). Statistical analyses were performed

using R (R Core Team 2015) including package ggplot2 for graphics (Wickham 2009).

Standard significance level of 95% was used.

Chapter 1

Results

The 27 Annex II study species were distributed widely and rather evenly across Denmark,

with somewhat lower density in the sandy outwash plains of western Denmark (Fig. 1a). Only

18 of the 633 Danish 10 km grid cells were without any records of target species while eight

grid cells were hotspots with seven to eight of the 27 species. The distributions of

internationally (N = 137, Fig. 1b) and nationally threatened (N = 1207, Fig.1c) species

showed other patterns with relatively more records from islands and coasts, and less from the

central southern part of Denmark. The most species rich grid cells for the three groups

showed only limited overlap, e.g. only one grid cell from the Annex II top-ten overlapped

with top-ten for the 1207 nationally threatened species, and only two with top-ten for the 137

internationally threatened species (Fig.1a,b,c).

a) 27 Annex II species

b) 137 internationally threatened species

c) 1207 nationally threatened species

Fig. 1. Species richness patterns of a) Annex II species, n=27, b) internationally threatened species not

on Annex II, n=137 and c) nationally threatened species not included in a) or b), n=1207. Red color

indicates maximum richness [a) 8 species, b) 55 species, c) 248 species] while light blue is minimum

[1, 2 and 8 respectively]. White is sea or UTM zone border in c) but includes zero species in a) and b).

Chapter 1

Annex II species as indicators

In spite of the differing distribution patterns, the 49 grid cells selected based on

complementarity for Annex II species ('Annex II set') represented the other groups

significantly better than random with 86.1% of the internationally and 65.8% of the nationally

threatened species represented (Fig. 2a - b). The median random solution would only have

represented 77.5% and 44.0% respectively, so the difference is not just statistically

significant, but also quite large, meaning that the Annex II species were effective as indicators

of grid cells with other threatened species. Six of the globally threatened species were

represented four to five times in the 49 grid cells, while the two most rare species were

unrepresented (Table 1).

Table 1. Representations (reps) of globally threatened species which are not also EU Annex II-species.

Species name

Total number

Reps in 49

Reps in 172 Presence records

of 10km grid

grid cells

in-/outside SAC

cell presences 'Annex II set' 'SAC set'

polygons

Ampedus hjorti

(beetle)

33 / 32

Formicoxenus nitidulus

(ant)

18 / 8

Phyllodesma ilicifolia

(moth)

80 / 151

Dolomedes plantarius

(spider)

37 / 2

Hygrocybe citrinovirens

(fungus)

29 / 38

Hygrocybe ingrata

(fungus)

56 / 75

Tricholoma acerbum

(fungus)

0 / 21

Podiceps auritus

(bird)

4/1

Fig. 2. Percentage of internationally (a, n=137) and nationally (b, n=1207) threatened species

represented by 49 grid cells optimized for five representations of Annex II species ('Annex_II_set'),

compared to the 'near-maximum' solution (Max) and to 49 grid cells chosen at random (random

median with upper and lower 95% confidence limits from 1000 draws). 49 grid cells correspond to

7.7% of 633 Danish grid cells (10 x 10 km).

Chapter 1

Performance of the realized SACs at grid cell scale

All Annex II species (n=27) were represented at least once in the 'SAC set' (Table A3,

supplementary material). Assuming a conservation target of at least one representation of all

other threatened species (n=1344), the 'SAC set' performed significantly better than the

random selection, (representing 88.1% and 75.7% of the species respectively) but also better

than the solution optimized for maximum representation of Annex II species which

represented 85.7% of target species - the 'optimal set' (Fig. 3).

Fig. 3. Percentage of all threatened species except Annex II (n=1344) represented by 172 grid cells

covering the 163 non-marine SACs (Sac_set), compared to the 'near-maximum' solution (Max), to the

'Optimal set' based on Annex II species, and to 172 grid cells chosen at random (random median with

upper and lower 95% confidence limits from 1000 draws). 172 grid cells correspond to 27.2% of 633

Danish grid cells (10 x 10 km).

Most study species (70.2%) had multiple representations in the 'SAC set' (Fig. 4; Table A3,

supplementary material) while zero representation ("gap species") was the case for 160

species (11.9%) including 112 'one-grid-cell' species. In total 15.6% of the 1344 threatened

species were recorded from only one grid cell. The gap species included some which have had

an ephemeral occurrence, e.g. only showing breeding behavior for one or a few years despite

being easily recorded, and other species being only recorded once due to detection problems

and/or extreme rarity. In all respects the 'SAC set' performed better than the 'optimal set' by

having more species represented multiple times and less species represented only once or not

at all (Fig. 4).

Chapter 1

Fig. 4. Efficiency of the 'SAC set' and the 'Optimal set' in representing threatened species (n=1344) in

172 grid cells compared to the total number of representations per species in all 633 grid cells.

Intervals are chosen to match the ones used by Lund (2002).

Taxon group coverage at grid cell scale

The 'SAC set' covered 27% of the 633 Danish grid cells, while its species coverage

(proportion of occupied grid cells covered for each species) varied for Annex II species from

39% (amphibians) to 70% (butterflies), and for globally threatened species from 0% (birds) to

83% (other invertebrates) (Table 2). Other species groups varied in coverage from 31%

(nationally threatened mammals) to 60% (nationally threatened other invertebrates). The

coverage of globally threatened species was higher than 40% for all taxon groups and species,

except for one fungus and the single bird both having a coverage of 0 % (Table A3,

supplementary material). For the vertebrate groups and mosses no significant difference could

be found compared to the null-hypothesis of random coverage (27%), while coverage was

significantly (99.9%) higher for all other taxon groups and for the total species pool (Table 2).

Globally threatened species did not differ significantly in coverage from Annex II species,

while nationally threatened and non-annex II species had significantly lower coverage than

these, but still significantly higher than the random level of 27% (Table 2).

Chapter 1

Table 2. Grid-cell-scale coverage % of threatened species in 172 grid cells of the 'SAC set'. Average

(minimum - maximum) percentage of occupied grid cells covered for each species group. The 172 grid

cells cover 27% of Denmark. They are based on 163 non-marine SACs.

Threat level

Annex II

X % (min - max)

39.4 (33 - 46)

70.0 (40 - 100)

Global

X % (min-max)

0 (0 - 0)

62.0 (41 - 79)

83.3 (83 - 83)

35.4 (0 - 58)

45.2 (0 - 83)

Non-annex II

X % (min-max)

42.3 (22 - 67)

37.1 (0 - 100)

35.4 (35 - 35)

31.0 (24 - 34)

31.6 (31 - 32)

58.8 (32 - 86)

36.5 (0 - 50)

47.2 (0 - 75)

31.6 (0 - 45)

39.1 (0 - 100)

National

X % (min-max)

30.7 (28 - 33)

39.8 (17 - 63)

47.2 (0 - 100)

43.0 (0 - 100)

60.1 (17 - 100)

51.8 (0 - 100)

51.0 (0 - 100)

327

510

190

49.0 (0 - 100)

46.6 (0 - 100)

Total

337

513

197

122

1371

Mammals

Birds

Reptiles

Amphibians

Butterflies

44.7 (27 - 67)

Other insects

66.4 (29 - 100)

61.5 (49 - 73)

Other invertebrates

Fungi

Lichens

Mosses

54.5 (54 - 55)

129

Vascular plants

Average

66.9 (50 - 94)

57.6 (27 - 100)

110

1207

Species threatened at more than one geographical level are only included once (in the leftmost column it would occur).

Annex II refers to the EU Habitats Directive Annex II while Non-annex II refers to other annexes of that directive plus

Annex I of the EU Birds Directive. "Other invertebrates" include spiders (n=17), snails (n=4), leeches (n=1), moss scorpions

(n=1) and crustaceans (n=1).

For these taxon groups (excluding Annex II which was the basis for SAC designation) the mean coverage differed

significantly (99.9%) from the 27% 'SAC set' coverage of Denmark. For other groups the difference was non-significant.

The Annex II group differed significantly from Global (99%), Non-annex II (99%) and National (95%). Covariate slopes in

the linear model were significantly (95%) different from Annex II except for group National. For Global group the slope was

positive while the slope was negative for the other groups.

The linear model fitted to the coverage by SACs of each taxon group showed that the overall

covariate (amount of occupied grid cells at national level for each species) was significant

(95%) with a negative slope, i.e. rare species had significantly higher coverage by the 'SAC

set' than less rare species. Globally threatened and non-annex II species had significantly

different slopes of their covariate compared to Annex II species. For globally threatened

species the slope was positive instead of the expected negative (Table 2) because some of the

rarest species were not covered.

Chapter 1

Polygon-based compared to grid cell based results

The coverage computed using detailed SAC polygon data instead of grid cells (Table 3)

showed larger variation around a higher average coverage of 42% (EU non-annex II species)

to 64% (globally threatened species). The coverage for all taxon groups was significantly

(99.9%) higher than the null-hypothesis supposing species coverage to equal the percentage of

Denmark covered by SACs (8.6%). The globally threatened species did not differ

significantly in coverage from Annex II species, while nationally threatened and non-annex II

species had significantly lower coverage, but still significantly (99.9%) higher than 8.6%

(Table 3).

Table 3. Polygon-scale coverage % of threatened species in non-marine Danish SAC polygons.

Average (minimum - maximum) percentage of all presence records 1991-2015 being inside the SAC

polygons which cover 8.6% of Denmark. See table 2 for other explanations.

Threat level

Annex II

X % (min-max)

Mammals

Birds

Global

80.0 (80 - 80)

X % (min-max)

51.5 (35 - 69)

94.9 (95 - 95)

28.7 (0 - 43)

63.8 (0 - 95)

Non-annex II

X % (min-max)

24.8 (9 - 44)

55.5 (0 - 100)

45.5 (46 - 46)

31.4 (6 - 63)

16.2 (6 - 26)

50.5 (13 - 88)

45.7 (20 - 78)

57.1 (0 - 100)

47.2 (28 - 69)

41.5 (0 - 100)

National

X % (min-max)

15.9 (0 - 32)

48.9 (0 - 100)

41.3 (0 - 100)

39.8 (0 - 100)

56.8 (0 - 100)

47.0 (0 - 100)

59.5 (0 - 100)

327

510

190

60.0 (0 - 100)

46.2 (0 - 100)

Total

337

513

197

122

1371

49.2 (6 - 83)

Reptiles

Amphibians

Butterflies

44.2 (17 - 71)

79.1 (58 - 100)

55.6 (37 - 86)

53.8 (33 - 66)

Other insects

Other invertebrate

Fungi

Lichens

Mosses

78.7 (75 - 83)

129

Vascular plants

Average

82.3 (66 - 97)

63.3 (6 - 100)

110

1207

For these taxon groups (excluding Annex II which was the basis for SAC designation) the mean coverage differed

significantly (99.9%) from the 8.6% SAC coverage of Denmark. When the three non-significant groups were pooled into

"other vertebrates" that group also became significant.

The total Annex II and Global groups did not differ significantly, while Non-annex II (99%) and National (95%) were

significantly different from Annex II.

Also for the polygon-based data, the linear model showed the overall covariate (amount of

occupied grid cells at national level for each species) to be significant (95%) with a negative

slope, i.e. rare species had significantly higher coverage by SAC polygons than less rare

species. But in contrast to the grid-cell-based data, the different taxon groups and threat

Chapter 1

groups did not differ in this respect as the slopes of the covariate were not significantly

different (Table 3).

Polygon-scale gap species at 0% coverage comprised 156 species (11.4%), of which only 77

species overlapped with the 160 gap species identified at grid-cell-scale. Some (41.7%) of the

156 species missed by SAC polygons had occurrence instead in nationally designated

protected areas of IUCN category I – IV (Table A3, supplementary material).

Discussion

Even though the target species groups showed different distribution patterns, the Annex II

species performed significantly better than random in identifying areas important for other

groups using complementarity, both for globally threatened species, for species threatened at

the EU level and also for the long list of nationally threatened species.

Both at grid-cell- and polygon-scale the SAC analyses similarly confirmed that the area

selection based on Annex II species presence has been successful in capturing occurrences of

other threatened species, by showing significantly higher coverage and representation than

could be expected by chance and thereby rejecting the null-hypothesis. This is in contrast to

many previous studies pointing out that it is unusual to find better than random coverage by

protected areas often because of e.g. economic constraints in the designation process (e.g.

Beier & de Albuquerque 2015; Gaston et al. 2008; Maiorano et al. 2006, 2007; Oldfield et al.

2004; Powell et al. 2000; Rodrigues & Brooks 2007).

The two taxon groups with only twice as high coverage as the 8.6% SAC coverage of

Denmark were 1) nationally threatened mammals including only European hare

Lepus

europeaus

(32%, numerous with a very wide distribution, but recently declining) and black

rat

Rattus rattus

(0%, only known from three industrial facilities and non-native pest) and 2)

other non-annex II insects including only the moth

Proserpinus proserpina

(6%, newly

colonizing species especially on railroad gravel fields) and the dragonfly

Aeschna viridis

(26%, fairly numerous with a very wide distribution). If the non-native pest and the newly

immigrant (after SAC designations) species are ignored, all species groups had at least three

times as high coverage as the 8.6% SAC coverage of Denmark.

Much higher coverage of species than 8.6% by SAC polygons was indeed found for both

internationally and nationally threatened species, and also for taxon groups separately

including the seldom tested taxon groups of fungi, lichens and miscellaneous invertebrates

including spiders. With generally three to eight times higher than 'baseline' coverage this is

Chapter 1

promising and underlines that the designation of Danish SACs has been rather successful also

for less well-known taxon groups and for species of global concern which were not targets.

This answers the questions left unresolved by the similar SAC-based study by Lund (2002)

based on more limited data. Indeed her conclusion that species listed in Annex II of the

Habitats Directive perform well as indicators of other species in site selection holds true also

for threatened species in general and for taxon groups she could not include, like fungi and

lichens, and also for other plants than orchids.

Besides the indicator value of Annex II species, there can be several reasons why the

coverage of threatened species in the SAC network is so high. Importantly, Denmark is so

heavily impacted by agriculture, urban areas and infrastructure that SAC designation was

more or less restricted to the c. 30% of the land with a more natural character. Hence, the

random sets inevitably included an average of 70% land lacking natural habitats. However,

the higher coverage of threatened species in the actual SAC set compared to the optimal set

based on Annex II species suggests that the Danish designation process was of high quality

targeting areas with high value for Danish nature in general, with low intrusion from

economic interests.

Denmark is the EU country with the lowest area proportion covered by Natura 2000 sites, so

there is reason to expect that coverage of threatened species in other EU countries could be

higher than the figures documented for Denmark. In our opinion the good coverage should

hold true also for other countries, assessed by the Danish coverage not being particularly high

when seen in comparison with other EU countries (Sluis et al. 2016; Trochet and Schmeller

2013). At least if the countries have followed the Natura 2000 designation guidelines and

made best possible use of existing data.

It should also be acknowledged that the thorough EU designation process including scientific

and NGO scrutiny of proposed Natura 2000 sites at "biogeographic" seminars (Evans 2012),

and including habitat types as designation reasons, can have helped to enhance the quality of

the site network. On the other hand the selection of species for Annex II has been seen as

biased and based on non-transparent criteria, especially in some Mediterranean countries

(Cardoso 2012; Maiorano et al. 2007, 2015). The original process for selecting Annex II

species is unfortunately only vaguely described (Evans 2012; Lund 2002). This might imply

that even higher efficiency could be possible if indicator species or sites were selected in a

more systematic way.

Chapter 1

One representation (or five) of a species is no guarantee for long-term viability or for

favorable conservation prospects (e.g. Kujala et al. 2011), so it is fortunate that more than

70% of the species had multiple representations in the 'SAC set'. Even if the overall coverage

was good, 11.9% of study species were missed by the 'SAC set' grid cells while 11.4% were

missed by the SAC polygons. These gap species call for supplementary conservation actions

targeting specific sites where they occur. Many of the gap species have very specific habitat

requirements, while others are irregular and unpredictable in their occurrence, e.g. being

geographically marginal to Denmark. Fortunately 41.7% of the 156 species missed by SAC

polygons occur in nationally designated protected areas (Table A3, supplementary material)

similar to the results of Maiorano et al. (2015) at European Union scale.

Both the grid-scale and polygon-scale analyses were based on the same detailed occurrence

data, so the differences found between grid- versus polygon-based analyses illustrate bias

introduced by fitting polygons into a fixed grid. Most Danish terrestrial SACs are smaller than

a grid cell but may still intersect several grid cells. Only one grid cell was selected for each

SAC except where more grid cells were necessary to represent the Annex II species used for

designation of the SAC. In this process parts of SACs in other grid cells were left out, while

species from large non-SAC areas surrounding the SAC entered the 'SAC set'. The bias shows

up clearly in the large turnover of gap species, with only 77 of the 156 'real' (polygon-based)

gap species being among the total of 160 gap species produced at grid-scale, meaning that

more than half of the grid-scale gap species were in reality an artefact of the coarse grid-scale

method. This is relevant e.g. for the globally threatened bird

Podiceps auritus.

The bias from using coarse data also results in less statistical significance in the grid-based

analysis, and in poorer fit of the linear models, including positive slope of the covariate in

some cases. A negative slope reflecting rare species having higher coverage than less rare

species is the expected result and was found for all polygon-based models, but not for all grid-

cell-based models.

Sluis et al. (2016) analyzed the performance of Natura 2000 at the EU scale based on

modelled and coarse distribution maps, and found much lower coverage figures for Denmark

than in our study (e.g. for reptiles & amphibians their coverage was 10% against ours at 30-

45%; mammals 10% against 15-50%; butterflies 25% against 40-80%; birds 15% against 48-

80%). There are several possible explanations for this considerable difference. First of all

Sluis et al. (2016) included common species besides threatened ones, which may lower

Chapter 1

figures, and they acknowledged limitations of their modelling method. But still the

differences are dramatic and could be caused, to a large extent, by the use of too coarse

species data. On the other hand most countries and regions do not have access to such detailed

occurrence data as used in our study, so using coarse maps and treating resulting estimates

conservatively as minimum coverage estimates would still be relevant.

Also Trochet & Schmeller (2013) in their study of Natura 2000 coverage of globally

threatened species used coarse data (IUCN distribution maps for 145 species & 10 x 10 km

grid cells for 155 others). They reported Natura 2000 area coverage of 11% in Denmark for

globally threatened species – very different from the 76% SAC coverage of presence records

that comes out of our study for the same three species which are all also on Annex II

(Dytiscus

latissimus, Graphoderus bilineatus & Margaritifera margaritifera).

The main

reason for the discrepancy seems to be that Trochet & Schmeller (2013) computed their

coverage as if each species was present everywhere inside the used distribution maps. This is

not the case in general and certainly not for the three study species, which have a very patchy

occurrence in specific small habitats. Even if a Natura 2000 site is designated to include the

full potential habitat of a species, it will in many cases only cover a small percentage of the

grid cell or region when the actual habitat is small, which is particularly often the case for

threatened species. Several authors have warned against these kinds of scale problems (e.g.

Alagador et al. 2011; Araujo 2004; Battisti & Fanelli 2015), even if it is also relevant to

emphasize the need to increase the area of suitable habitat for threatened species to halt

biodiversity loss.

Regardless of the huge effort used in our quality control, there will inevitably also be biases of

several sorts in the data used for our study, e.g. SACs may have been surveyed more

intensively than other areas, "false-presences" caused by mis-identification, "false-absences"

caused by overlooked species, besides other error types (Geldmann et al. 2016).

For conservation purposes it is comforting, that the SACs cover 88% of other threatened

species, and not just the EU Annex II species used for designation. The Natura 2000 network

in Denmark is in the second round of implementation of 6-year management plans designed

to ensure that SPAs and SACs are managed properly to reach favorable conservation status as

soon as possible (Anon. 2016a; Larsen & Lentz 2016). Given the high efficiency of Annex II

species in representing other threatened species at 10 x 10 km and SAC scale, it seems

reasonable to assume that there is also a significant overlap in habitat requirements at local

Chapter 1

scale and in relation to management requirements. If so, many other threatened species should

benefit from conservation actions targeted at habitats and Annex II species in each SAC, but

this needs to be investigated further by analyzing relevant ecological traits across species

groups. In addition more work and protection is needed if the 12% gap species shall be

covered. Some of the populations in the matrix landscape between protected areas or in

nationally protected sites can perhaps be viable (Watts et al. 2009), but this needs further

testing. Also global change dynamics in species occurrence are necessary to take into account

if future viability is to be secured (e.g. Kujala et al. 2011).

For successful conservation of biodiversity it is essential that the management of protected

sites is implemented with sufficient resources to ensure long-term viability of the species (e.g.

Kati et al. 2015). The Danish Natura 2000 network has been criticized for not including

sufficient areas of forest (Petersen et al. 2016). It is the aim of the Danish government to

address these issues by continuing and enhancing the already ongoing work in the Natura

2000 network with an annual management budget of 40 million Euro from 2016 to 2021

increasing the active conservation management from 60 000 to 90 000 hectares of habitats

(Larsen & Lentz 2016), and by designating more than 13 000 hectares of new forest reserves

(Anon. 2016a; Naturstyrelsen 2018). The new reserves will be targeted at species and sites

identified by this and other studies to find the most relevant and cost-efficient sites to include.

Conclusion

The EU site designation process defined in the Habitats Directive using occurrence of a multi-

taxon set of indicator species combined with presence of characteristic or threatened habitats

has resulted in the protected areas having a significantly higher representation and coverage of

non-target threatened species than expected and previously reported. The studied site network

covers only 8.6% of Denmark but on average 42 - 64% of all known occurrences of both

globally and nationally threatened species. These figures even include less well-known

taxonomic groups like fungi, lichens, mosses and miscellaneous invertebrates including

spiders.

These results support previous studies which have concluded that site selection using multi-

taxon sets of species as proxies for threatened biodiversity is efficient. This applies even

though the selection process of Annex II indicator species has been criticized as being biased

and based on non-transparent criteria. Enlargements of protected area networks on other

Chapter 1

continents could thus be made more efficient by using a similar designation approach as the

one used for developing the European Natura 2000 network.

Acknowledgements

This study would not have been possible without the immense data collection efforts of

numerous persons active in Birdlife Denmark, Lepidopterological Society, Danish

Mycological Society, Danish Botanical Society, Entomological Society of Fünen, Spiders of

Denmark (online database), the Natural History Museum of Denmark, the Danish Nature

Agency and Birds & Nature web-database (Fugle & Natur license B05/2014). All persons and

organizations involved deserve sincere thanks for their efforts and for permitting use of data.

C. Rahbek, B. Hermansen, A. H. Petersen, L. A. Hansen and L. N. B. Jensen at the Centre for

Macroecology, Evolution and Climate (CMEC), University of Copenhagen, are thanked for

help and advice during the project. Thanks also to P.H. Williams for kindly providing the

WORLDMAP software.

Erik Buchwald and Jacob Heilmann-Clausen acknowledge the Danish National Research

Foundation for funding for the Center for Macroecology, Evolution and Climate (grant-

number DNRF96). The Danish Nature Agency and Innovation Fund Denmark (industrial

Ph.D. grant no. 4135-00145B) are thanked for economic support to Erik Buchwald.

Appendix A. Supplementary material

in the online version.

Table A1 with overview and references for data sources.

Table A2 GBIF records.

Table A3 with details on all 1371 species included in the study.

Chapter 1

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Chapter 1

Appendix A, supplementary electronic materials

Table A1 Overview of data sources and records.

Used records include only study species and the years 1991-2015.

Records

Data provider

Atlas- & observation-database (Birdlife

Denmark, DOFbasen)

Birds & Nature web-database (license

B05/2014, Fugle & Natur)

Bugbase (Lepidopterological Society, Denmark)

Biodiversity map, HNV-forest update (Danish

Nature Agency)

Biodiversity map, 2014 (Danish Nature Agency)

Danish fungal records database (Danish

Mycological Society)

An Annotated Atlas of the Danish Butterflies

(Zoological Museum)

"Forests of special nature value § 25" (Danish

Nature Agency)

Danish Nature Agency database on things to

protect, "Pas-paa-kort"

Atlas Flora Danica data 1992-2013, red list &

forest extract (Danish Botanical Society)

Entomological Society of Fünen database

(Fynske insekter)

Spiders of Denmark online database

Epiphytic lichens and bryophytes in the forests

of Lille Vildmose in 2013

Roof-top insect study 1992-2009 (Zoological

Museum)

Digital databases of invertebrates (Natural

History Museum of Denmark)

Epiphytic lichens in the forest of Kås in 2002

Epiphytic lichens and bryophytes on oak in the

forest of Tofte 2012-2013

Status books on the forests of Tofte &

Høstemark

Danish Topographical Botanical Survey online,

"TBU collections"

Hepatics at the Herbarium C. (Natural History

Museum of Denmark)

Total

17 961 625

1 958 677

859 969

54 997

23 185

559 159

199 307

49 894

8526

123 334

167 905

31 731

210

44 088

69 436

104

3331

3299

9306

22 128 168

Used

110 506

57 402

23 689

22 764

20 919

19 868

5288

2401

1867

1460

971

158

267 064

Access

date

17/12

2015

2/12

2015

21/12

2015

2/5

2016

9/12

2015

17/11

2015

15/1

2014

1/12

2015

3/11

2015

22/4

2014

20/1

2016

3/11

2015

19/4

2016

14/1

2016

1/12

2015

19/4

2016

19/4

2016

19/4

2016

21/12

2015

3/11

2015

Total

Reference

http://dofbasen.dk/

http://www.fugleognatu

r.dk/

http://www.bugbase.dk/

Johannsen, V. K., 2015 &

digital data 2/5 2016

Ejrnæs et al., 2014 &

digital update

http://www.svampeatlas

.dk/

Stoltze, M., 1994

Naturstyrelsen 2007

Naturstyrelsen 2015

Hartvig, P., 2015

http://www.fynskeinsekt

er.dk/

http://www.danmarks-

edderkopper.dk/

Fritz, Ô., 2014

Thomsen et al., 2015

Stein, M., 2015

Larsen, R.S., 2002

Mouridsen, M.T., 2014

Hald-Mortensen, P.,

2012 & 2002

http://www.daim.snm.k

u.dk/TBU-en

http://www.daim.snm.k

u.dk/hepatics

Chapter 1

References for table A1:

Ejrnæs et al., 2014.

Biodiversitetskort for Danmark.

Rapp 112/2014, DCE.

Fritz, Ô. 2014.

Epiphytic lichens and bryophytes in the forests of Lille Vildmose in 2013.

Naturcentrum

report 2014-03-04, 34 pp.

Hald-Mortensen, P. 2012.

Tofte Skov og Mose - status 2012.

AVJF, 387 pp.

Hartvig, P., 2015.

Atlas Flora Danica.

3 volumes. Gyldendal, København.

Johannsen, V. K., 2015.

Udvikling af et High Nature Value - HNV-skovkort for Danmark.

IGN rapport, nov

2015.

Larsen, R.S., 2002.

Biodiversitet, kvistflora og kvælstofbelastning. Kås Skov, 2002.

KU speciale rapport.

Mouridsen, M.T., 2014.

Epifytiske laver og mosser på eg i Tofte Skov. Status 2012-13.

Specialerapport, KU

Biol. Inst. 2014.

Naturstyrelsen, 2007.

Data from 2007 COWI project "Forests of special nature value § 25".

Unpublished.

Naturstyrelsen, 2015.

Database on things to protect, "Pas-paa-kort", Naturreg.

Unpublished.

Naturstyrelsen, 2016.

Digital biodiversity map 2016,

updatet ArcGis files March 18th 2016. Unpublished.

Stein. M., in litt. 2015.

RE: Forespørgsel på invertebratdata til mit PhD.

Email 1/12 2015 from SNHM.

Stoltze, M., 1994.

An Annotated Atlas of the Danish Butterflies.

Zoological Museum, 134 pp.

Thomsen, P. F., Jørgensen, P. S., Bruun, H. H., Pedersen, J., Riis-Nielsen, T., Jonko, K., Słowińska, I.,

Rahbek, C., Karsholt, O., 2015.

Resource specialists lead local insect community turnover associated with

temperature – analysis of an 18-year full-seasonal record of moths and beetles.

Journal of Animal

Ecology. doi: 10.1111/1365-2656.12452.

Table A2 Overview of GBIF records for Denmark September 2016

GBIF records for DK

Redundant to data in table A1 (updated original data used instead)

Coordinate projection uncertain (and probably also redundant)

With other "known coordinate issues" (and probably also redundant)

No study species

No years

Total

GBIF records for Denmark (5/9 2016)

Records

8 917 771

606 352

490 677

117 289

113 546

10 245 635

Percent

100

Reference:

GBIF 2016. GBIF.org (5th September 2016)

GBIF Occurrence Download

http://doi.org/10.15468/dl.obr3f0

Chapter 1

Page 1 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Species name

annex II

Mammal

Barbastella barbastellus

annex II

Mammal

Canis lupus

annex II

Mammal

Halichoerus grypus

annex II

Mammal

Lutra lutra

annex II

Mammal

Myotis bechsteinii

annex II

Mammal

Myotis dasycneme

annex II

Mammal

Phoca vitulina

annex II

Amphibian

Bombina bombina

annex II

Amphibian

Triturus cristatus

annex II

Butterfly

Euphydryas aurinia

annex II

Butterfly

Maculinea arion

annex II

Other insect

Leucorrhinia pectoralis

annex II

Other insect

Ophiogomphus cecilia

annex II

Other insect

Osmoderma eremita

annex II

Other invertebrateAnthrenochernes stellae

annex II

Other invertebrateVertigo angustior

annex II

Other invertebrateVertigo geyeri

annex II

Moss

Buxbaumia viridis

annex II

Moss

Hamatocaulis vernicosus

annex II

Plant

Botrychium simplex

annex II

Plant

Cypripedium calceolus

annex II

Plant

Liparis loeselii

annex II

Plant

Luronium natans

annex II

Plant

Saxifraga hirculus

annex II & globa Other insect

Dytiscus latissimus

annex II & globa Other insect

Graphoderus bilineatus

annex II & globa Other invertebrateVertigo moulinsiana

global

Bird

Podiceps auritus

global

Other insect

Ampedus hjorti

global

Other insect

Formicoxenus nitidulus

global

Other insect

Phyllodesma ilicifolia

global

Other invertebrateDolomedes plantarius

global

Fungi

Hygrocybe citrinovirens

global

Fungi

Hygrocybe ingrata

global

Fungi

Tricholoma acerbum

EU non-annex II Mammal

Eptesicus nilssonii

EU non-annex II Mammal

Eptesicus serotinus

EU non-annex II Mammal

Martes martes

EU non-annex II Mammal

Muscardinus avellanarius

EU non-annex II Mammal

Mustela putorius

EU non-annex II Mammal

Myotis brandtii

EU non-annex II Mammal

Myotis daubentonii

EU non-annex II Mammal

Myotis mystacinus

EU non-annex II Mammal

Myotis nattereri

EU non-annex II Mammal

Nyctalus noctula

EU non-annex II Mammal

Pipistrellus nathusii

EU non-annex II Mammal

Pipistrellus pipistrellus

EU non-annex II Mammal

Pipistrellus pygmaeus

EU non-annex II Mammal

Plecotus auritus

EU non-annex II Mammal

Sicista betulina

EU non-annex II Mammal

Vespertilio murinus

EU non-annex II Bird

Aegolius funereus

EU non-annex II Bird

Alcedo atthis

EU non-annex II Bird

Anthus campestris

EU non-annex II Bird

Aquila chrysaetos

EU non-annex II Bird

Ardea alba

EU non-annex II Bird

Asio flammeus

EU non-annex II Bird

Botaurus stellaris

EU non-annex II Bird

Branta leucopsis

EU non-annex II Bird

Bubo bubo

EU non-annex II Bird

Calidris alpina schinzii

EU non-annex II Bird

Caprimulgus europaeus

EU non-annex II Bird

Charadrius alexandrinus

EU non-annex II Bird

Chlidonias niger

EU non-annex II Bird

Ciconia ciconia

EU non-annex II Bird

Circus aeruginosus

EU non-annex II Bird

Circus cyaneus

EU non-annex II Bird

Circus pygargus

EU non-annex II Bird

Coracias garrulus

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

335

5.7

6.0 23.9

33.3

83.3 83.3

1519

24.2

68.7 70.6

14355

3.1

21.5 60.1

0.0

66.7 83.3

4.9

29.3 34.2

10595

12.3

69.2 73.5

1082

0.0

71.0 94.4

4950

2.6

17.3 78.3

993

0.5

58.4 82.4

497

91.0

99.8 99.8

596

0.5

56.0 77.0

381

3.4

37.3 54.3

145

16.6

55.9 75.2

8.3

33.3 50.0

244

4.9

66.4 92.2

0.0

56.8 88.6

255

17.7

82.8 85.9

188

0.5

74.5 96.3

111

0.0

97.3 100.0

138

20.3

68.8 69.6

1360

0.0

91.5 98.1

0.0

66.2 78.5

149

0.0

87.9 99.3

0.0

42.9 47.6

0.0

85.7 98.0

361

9.4

58.7 94.7

0.0

80.0 100.0

9.2

50.8 63.1

3.9

69.2 76.9

231

20.4

34.6 64.5

7.7

94.9 94.9

3.0

43.3 77.6

131

3.8

42.8 78.6

0.0

8.7 13.0

179

5.6

21.2 34.6

597

9.2

24.0 34.3

178

0.0

21.9 24.7

904

3.2

21.1 34.1

4.7

21.9 31.3

281

4.6

32.7 53.0

0.0

16.0 28.0

9.1

18.2 35.4

804

1.7

39.4 54.6

100

10.0

41.0 60.0

0.0

15.4 38.5

547

9.9

26.9 42.4

19.4

44.4 61.1

5.0

30.0 50.0

521

0.6

14.0 17.1

4.1

39.8 40.8

1140

1.9

30.4 51.2

34.6

72.7 98.2

88.4

90.7 90.7

25.0

50.0 100.0

3.1

36.7 89.8

6662

13.2

72.8 94.5

160

0.6

48.1 78.8

448

3.1

17.9 21.2

273

0.7

91.6 96.0

2661

2.7

33.2 41.0

312

2.2

87.8 87.8

254

0.4

74.4 94.5

483

0.0

1.0 19.5

5940

5.6

48.7 83.1

1.7

91.4 96.6

774

0.8

23.3 57.6

0.0

Grid cells

10 x 10 km

sac-set

172

cells

Total

126

421

112

298

405

132

173

292

125

114

180

208

161

370

122

Chapter 1

Page 2 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Species name

EU non-annex II Bird

Crex crex

EU non-annex II Bird

Cygnus cygnus

EU non-annex II Bird

Dendrocopos medius

EU non-annex II Bird

Dryocopus martius

EU non-annex II Bird

Falco peregrinus

EU non-annex II Bird

Ficedula parva

EU non-annex II Bird

Gallinago media

EU non-annex II Bird

Gelochelidon nilotica

EU non-annex II Bird

Grus grus

EU non-annex II Bird

Haliaeetus albicilla

EU non-annex II Bird

Himantopus himantopus

EU non-annex II Bird

Hydrocoloeus minutus

EU non-annex II Bird

Hydroprogne caspia

EU non-annex II Bird

Lanius collurio

EU non-annex II Bird

Larus melanocephalus

EU non-annex II Bird

Lullula arborea

EU non-annex II Bird

Luscinia svecica

EU non-annex II Bird

Milvus milvus

EU non-annex II Bird

Pandion haliaetus

EU non-annex II Bird

Pernis apivorus

EU non-annex II Bird

Philomachus pugnax

EU non-annex II Bird

Platalea leucorodia

EU non-annex II Bird

Pluvialis apricaria

EU non-annex II Bird

Porzana parva

EU non-annex II Bird

Porzana porzana

EU non-annex II Bird

Porzana pusilla

EU non-annex II Bird

Recurvirostra avosetta

EU non-annex II Bird

Sterna hirundo

EU non-annex II Bird

Sterna paradisaea

EU non-annex II Bird

Sterna sandvicensis

EU non-annex II Bird

Sternula albifrons

EU non-annex II Bird

Sylvia nisoria

EU non-annex II Bird

Tetrao tetrix

EU non-annex II Bird

Tringa glareola

EU non-annex II Reptile

Lacerta agilis

EU non-annex II Amphibian

Bufotes variabilis

EU non-annex II Amphibian

Epidalea calamita

EU non-annex II Amphibian

Hyla arborea

EU non-annex II Amphibian

Pelobates fuscus

EU non-annex II Amphibian

Pelophylax esculentus

EU non-annex II Amphibian

Pelophylax ridibundus

EU non-annex II Amphibian

Rana arvalis

EU non-annex II Amphibian

Rana dalmatina

EU non-annex II Amphibian

Rana temporaria

EU non-annex II Other insect

Aeshna viridis

EU non-annex II Other insect

Proserpinus proserpina

EU non-annex II Other invertebrateHelix pomatia

EU non-annex II Other invertebrateHirudo medicinalis

EU non-annex II Lichen

Cladonia arbuscula

EU non-annex II Lichen

Cladonia ciliata

EU non-annex II Lichen

Cladonia portentosa

EU non-annex II Lichen

Cladonia pp., subgenus Cladina sp.

EU non-annex II Lichen

Cladonia rangiferina

EU non-annex II Lichen

Cladonia stellaris

EU non-annex II Lichen

Cladonia stygia

EU non-annex II Moss

Leucobryum glaucum

EU non-annex II Moss

Sphagnum affine

EU non-annex II Moss

Sphagnum angustifolium

EU non-annex II Moss

Sphagnum auriculatum

EU non-annex II Moss

Sphagnum austinii

EU non-annex II Moss

Sphagnum balticum

EU non-annex II Moss

Sphagnum capillifolium

EU non-annex II Moss

Sphagnum centrale

EU non-annex II Moss

Sphagnum compactum

EU non-annex II Moss

Sphagnum contortum

EU non-annex II Moss

Sphagnum cuspidatum

EU non-annex II Moss

Sphagnum fallax

EU non-annex II Moss

Sphagnum fimbriatum

EU non-annex II Moss

Sphagnum flexuosum

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

3437

0.6

17.8 43.7

183

3.8

10.9 66.7

0.0

2081

7.5

48.8 58.5

733

17.5

28.4 37.1

588

17.7

43.0 55.6

2.3

98.9 98.9

163

0.6

44.8 78.5

2948

15.6

70.7 86.3

1467

21.2

62.7 75.9

149

45.0

47.7 49.7

0.0 100.0 100.0

3.3

80.0 96.7

5972

6.6

49.4 62.9

898

0.0

12.4 88.4

3498

4.6

34.8 45.4

2643

4.2

51.8 87.6

889

1.4

7.3 16.3

25.7

80.0 85.7

1233

9.9

40.6 55.2

265

1.1

72.5 90.6

0.0

88.3 98.9

5.8

75.6 93.0

0.0 100.0 100.0

1652

17.9

65.1 86.8

211

60.7

97.6 98.6

3554

2.2

70.1 85.2

1121

0.6

54.2 84.6

1804

0.9

73.5 80.9

468

0.2

51.1 87.8

1189

3.4

74.4 80.3

14.3

71.4 90.5

0.0

83.3 83.3

248

6.9

70.6 80.7

2389

11.6

45.5 69.8

1029

0.4

50.3 78.0

1442

14.2

63.4 82.4

4658

0.2

5.8 61.8

1076

1.0

9.8 87.6

3972

3.4

37.4 64.1

0.0

47.2 61.1

5533

4.9

27.1 89.8

2250

3.7

19.4 74.3

3030

4.3

22.2 52.1

849

0.8

26.4 65.0

1.5

5.9 11.8

1675

3.5

12.9 32.1

7.1

88.1 97.6

13.1

41.0 67.2

308

11.0

77.6 94.8

368

14.4

51.9 69.6

148

9.5

32.4 54.1

12.8

46.8 66.0

0.0

50.0 100.0

0.0

20.0 100.0

186

18.8

53.2 75.8

0.0

8.3 83.3

147

12.9

61.2 85.0

105

13.3

64.8 86.7

1 100.0 100.0 100.0

20.0

80.0 100.0

45.5

67.3 81.8

5.0

70.0 90.0

26.1

65.2 87.0

4.6

54.6 86.4

159

11.3

59.8 84.3

377

14.1

57.3 83.8

393

12.7

53.2 84.0

11.4

65.7 91.4

Grid cells

10 x 10 km

sac-set

172

cells

Total

291

170

182

153

433

138

246

123

189

223

171

179

135

163

116

311

110

112

199

104

134

205

430

141

143

375

117

283

143

112

133

Chapter 1

Page 3 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

EU non-annex II Moss

EU non-annex II Plant

Mammal

Bird

Butterfly

Species name

Sphagnum fuscum

Sphagnum girgensohnii

Sphagnum inundatum

Sphagnum lindbergii

Sphagnum magellanicum

Sphagnum majus

Sphagnum molle

Sphagnum obtusum

Sphagnum palustre

Sphagnum papillosum

Sphagnum quinquefarium

Sphagnum riparium

Sphagnum rubellum

Sphagnum russowii

Sphagnum squarrosum

Sphagnum subnitens

Sphagnum tenellum

Sphagnum teres

Sphagnum warnstorfii

Arnica montana

Diphasiastrum complanatum

Diphasiastrum tristachyum

Huperzia selago

Lycopodiella inundata

Lycopodium annotinum

Lycopodium clavatum

Lepus europaeus

Rattus rattus

Acrocephalus arundinaceus

Anas acuta

Anas penelope

Arenaria interpres

Athene noctua

Carpodacus erythrinus

Cinclus cinclus

Falco subbuteo

Galerida cristata

Jynx torquilla

Lanius excubitor

Limosa limosa

Locustella luscinioides

Mergus merganser

Netta rufina

Nucifraga caryocatactes

Oriolus oriolus

Remiz pendulinus

Serinus serinus

Tringa ochropus

Upupa epops

Argynnis adippe

Argynnis aglaja

Argynnis niobe

Argynnis paphia

Aricia artaxerxes

Boloria aquilonaris

Boloria euphrosyne

Brenthis ino

Carterocephalus silvicola

Coenonympha arcania

Coenonympha tullia

Cyaniris semiargus

Erynnis tages

Hesperia comma

Leptidea juvernica

Leptidea sinapis

Lycaena hippothoe

Lycaena tityrus

Maculinea alcon

Melitaea athalia

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

17.2

37.9 96.6

25.0

52.8 77.8

102

8.8

49.0 83.3

0.0

57.1 100.0

158

13.3

60.1 85.4

0.0

0.0 100.0

15.4

69.2 92.3

0.0

75.0 81.3

496

11.9

56.7 83.7

112

12.5

45.5 85.7

0.0

50.0 50.0

9.8

59.8 87.8

155

12.3

55.5 85.2

136

24.3

63.2 88.2

359

9.2

55.2 85.0

197

5.6

60.4 89.9

24.3

67.6 83.8

165

7.3

55.2 87.9

11.8

52.9 88.2

817

3.3

28.4 72.5

0.0

28.1 31.3

130

20.8

63.9 90.0

264

8.3

56.4 67.8

270

5.2

69.3 91.1

763

9.6

38.0 57.8

575

9.2

46.3 60.0

27288

4.5

31.8 53.7

0.0

679

5.2

40.8 91.9

171

0.0

90.6 97.1

1.3

59.0 78.2

0.0 100.0 100.0

0.0

2.2

4.4

2811

13.5

50.7 63.3

0.0

36.1 58.3

172

0.0

27.3 37.8

421

0.0

0.2

3.8

973

11.0

48.2 60.7

0.0

72.5 90.0

947

0.5

73.5 95.0

491

0.2

57.8 88.2

1211

3.3

23.6 44.3

0.0

88.6 97.7

11.5

39.3 47.5

771

23.9

51.2 63.0

859

1.8

31.4 83.9

861

3.1

12.5 34.6

607

4.9

59.5 76.1

0.0

62.5 62.5

1202

1.4

30.5 46.6

1948

3.7

38.9 61.0

1181

8.3

41.7 60.6

5276

8.0

29.4 44.8

255

0.0

42.0 58.4

1459

9.7

53.1 83.9

811

0.5

32.9 43.4

793

7.4

52.0 84.2

324

0.0

79.9 86.4

60.7

60.7 60.7

1247

10.4

42.7 78.2

1434

5.9

29.1 49.5

1169

5.8

48.3 76.0

1061

16.0

55.8 81.5

0.0 100.0 100.0

2265

6.6

29.6 52.9

478

0.0

21.3 69.5

742

8.0

54.5 75.3

1546

0.2

25.1 70.5

Grid cells

10 x 10 km

sac-set

172

cells

Total

134

119

156

158

131

602

169

173

179

163

132

122

214

146

267

122

136

238

219

Chapter 1

Page 4 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Butterfly

Other insect

Species name

Melitaea cinxia

Melitaea diamina

Nymphalis polychloros

Papilio machaon

Plebejus argus

Pyrgus armoricanus

Pyrgus malvae

Satyrium ilicis

Satyrium w-album

Thecla betulae

Abdera bifasciata

Acontia trabealis

Acronicta cinerea

Acronicta cuspis

Acronicta tridens

Adscita statices

Aegomorphus clavipes

Agonum dolens

Agonum ericeti

Agriotes ustulatus

Agrochola nitida

Allandrus undulatus

Allecula morio

Allecula rhenana

Amara ingenua

Ampedus nigerrimus

Ampedus praeustus

Ampedus quercicola

Ampedus sanguineus

Amphimallon ochraceum

Amphipoea lucens

Amphipyra perflua

Anasimyia lunulata

Anaspis ruficollis

Aneurus avenius

Anthicus sellatus

Apamea aquila

Aphodius coenosus

Aphodius fasciatus

Aphodius obliteratus

Aphodius scrofa

Aphodius sordidus

Apion armatum

Apion carduorum

Apion simum

Apion sulcifrons

Apion vorax

Apteropeda orbiculata

Aradus betulae

Arctophila bombiformis

Arctophila superbiens

Asilus crabroniformis

Aulonium trisulcum

Bagous nodulosus

Baris lepidii

Bembidion bipunctatum

Bembidion dentellum

Bembidion ephippium

Bembidion humerale

Bembidion litorale

Bembidion lunatum

Bembidion maritimum

Blaps lethifera

Bombus humilis

Bombus veteranus

Brachinus crepitans

Brachionycha nubeculosa

Brachyopa bicolor

Brachyopa panzeri

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

4588

7.2

38.7 59.4

0.0

7.8 98.0

2.4

9.8 36.6

290

9.7

27.2 39.7

1680

6.3

44.6 72.6

458

0.0

65.1 71.0

1898

6.1

44.4 60.6

0.0

0.0 100.0

1859

2.6

11.3 25.9

1752

9.3

24.0 38.6

33.3

33.3 66.7

230

0.9

10.9 18.3

711

0.3

1.4

3.9

117

41.0

82.1 85.5

174

4.0

20.1 47.7

1697

6.9

33.6 57.6

0.0

0.0 100.0 100.0

50.0

75.0 100.0

0.0

129

25.6

33.3 58.9

0.0 100.0 100.0

0.0

28.6 42.9

0.0

16.7 16.7

50.0 100.0 100.0

0.0

6.3 81.3

0.0

61.1 66.7

0.0

17.4 17.4

20.0

60.0 60.0

141

34.8

49.7 74.5

0.0

5.7

0.0

15.7 27.5

0.0

9.1 36.4

0.0

75.0 75.0

11.9

79.8 86.9

16.7

72.2 88.9

50.0

75.0 75.0

0.0

2 100.0 100.0 100.0

0.0

50.0 50.0

0.0

0.0 12.5

0.0

50.0 100.0

0.0

0.0 28.6

0.0 100.0 100.0

24.1

94.3 97.7

3.3

15.2 31.5

248

12.9

29.8 45.6

108

5.6

40.7 58.3

0.0

5.6 16.7

0.0 100.0 100.0

0.0

0.0 100.0 100.0

0.0

0.0 100.0

0.0

0.0 100.0

0.0

0.0 100.0 100.0

0.0

6.7

0.0 100.0 100.0

0.0

39.1 82.6

0.0

0.0 50.0

0.0

0.0 14.3

0.0

33.3 33.3

0.0

6.3 24.2

Grid cells

10 x 10 km

sac-set

172

cells

Total

345

105

178

278

312

103

144

300

Chapter 1

Page 5 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Other insect

Species name

Brachyopa scutellaris

Brachypalpus laphriformis

Bradybatus kellneri

Bryophila domestica

Bryophila raptricula

Caliprobola speciosa

Calliteara abietis

Calophasia lunula

Calosoma auropunctatum

Calosoma inquisitor

Carabus auratus

Carabus cancellatus

Carabus clathratus

Carabus glabratus

Carabus intricatus

Carabus nitens

Caradrina montana

Caryocolum fischerella

Cassida murraea

Cerambyx scopoli

Ceutorhynchus assimilis

Ceutorhynchus posthumus

Chalcosyrphus piger

Chalcosyrphus valgus

Chamaesyrphus lusitanicus

Cheilosia flavipes

Cheilosia frontalis

Cheilosia illustrata

Chersotis cuprea

Chionodes ignorantella

Chlaenius tristis

Chloantha hyperici

Chorthippus dorsatus

Chorthippus mollis

Chrysolina carnifex

Chrysolina graminis

Chrysotoxum verralli

Clostera anastomosis

Coeliastes lamii

Coenagrion armatum

Colydium elongatum

Conisania leineri

Copris lunaris

Coranarta cordigera

Coriomeris scabricornis

Coryssomerus capucinus

Cosmia affinis

Crepidophorus mutilatus

Criorhina floccosa

Cryptocephalus bilineatus

Cryptocephalus distinguendus

Cucullia artemisiae

Cymindis angularis

Cymindis macularis

Cymindis vaporariorum

Cyrtopogon lateralis

Dasypolia templi

Diarsia dahlii

Dibolia occultans

Dichomeris ustalella

Dictyoptera aurora

Dolichus halensis

Doros profuges

Drymonia obliterata

Dryophthorus corticalis

Dyschirius chalceus

Eilema griseola

Eilema pygmaeola

Elater ferrugineus

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

0.0

14.8 22.2

2.1

22.3 31.9

0.0

16.1

28.6 32.1

532

0.2

4.5

6.0

0.0

7.9 10.5

0.0

45.6 46.7

15.8

13.7 21.1

0.0

8.3 16.7

0.0

88.9 94.4

2.7

16.2 40.5

11.9

61.9 88.1

0.0

52.9 52.9

15.8

52.6 52.6

10.0

83.3 83.3

25.0

25.0 25.0

0.0

0.0 100.0 100.0

3.6

28.6 64.3

0.0

0.0 14.3

0.0

4.5

9.0 25.4

4.2

45.8 54.2

0.0 100.0 100.0

0.0

22.2 33.3

5.1

22.0 33.9

223

2.2

4.5 23.8

0.0

66.7 100.0

130

0.0

0.8

0.0

69.6 73.9

0.0

28.6 57.1

0.0 100.0 100.0

0.0

66.7 66.7

0.0

5.0 15.0

0.0

0.0 100.0

35.5

88.7 98.4

0.0

50.0 100.0

189

0.0

2.7

7.9

0.0 100.0 100.0

48.4

61.1 89.5

16.7

25.0 50.0

0.0 100.0 100.0

1.6

12.5 21.9

13.3

41.7 55.0

3.9

19.2 32.1

0.0

0.0 100.0 100.0

132

0.8

7.6 25.8

0.0

0.0 100.0

0.0

66.7 100.0 100.0

0.0

387

0.3

8.5 18.1

1.4

9.5 14.9

0.0

1 100.0 100.0 100.0

20.0 100.0 100.0

0.0

0.0 50.0

152

25.7

31.6 32.2

0.0

0.0 100.0

1496

1.2

14.6 34.0

292

2.4

10.6 39.7

4.7

62.8 90.7

Grid cells

10 x 10 km

sac-set

172

cells

Total

Chapter 1

Page 6 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Other insect

Species name

Endromis versicolora

Epaphius rivularis

Epilecta linogrisea

Epipsilia grisescens

Epistrophe grossulariae

Epistrophe melanostoma

Eremobia ochroleuca

Eriogaster lanestris

Eriozona syrphoides

Eristalis cryptarum

Eristalis ostracea

Eristalis rupium

Eucnemis capucina

Euheptaulacus villosus

Eumerus ornatus

Eumerus sogdianus

Eurydema dominulus

Eutolmus rufibarbis

Exocentrus lusitanus

Gastropacha quercifolia

Geotrupes stercorarius

Gnorimoschema herbichii

Gnorimus nobilis

Gnorimus variabilis

Gryllotalpa gryllotalpa

Habroloma nanum

Hadena albimacula

Hadena filograna

Hallomenus axillaris

Harpalus calceatus

Harpalus froelichii

Harpalus griseus

Heliothis maritima

Hemaris tityus

Herminia tarsicrinalis

Hoplia graminicola

Hoplia philanthus

Hoplodrina respersa

Hydraecia nordstroemi

Hylobius pinastri

Hymenalia rufipes

Hyphoraia aulica

Hypulus quercinus

Ischnodes sanguinicollis

Ischnomera sanguinicollis

Jalla dumosa

Judolia sexmaculata

Labidostomis longimana

Lacanobia splendens

Lamprotes c-aureum

Laphria ephippium

Lasionhada proxima

Lasiorhynchites cavifrons

Lejogaster tarsata

Lejops vittata

Lemonia dumi

Leptarthrus brevirostris

Leptura aethiops

Lestes virens

Licinus depressus

Lithophane lamda

Lixus paraplecticus

Lucanus cervus

Lygephila craccae

Lygistopterus sanguineus

Lymexylon navale

Machimus arthriticus

Magdalis armigera

Malachius aeneus

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

167

0.6

44.3 72.5

1 100.0 100.0 100.0

150

0.0

2.0

3.3

16.7

8.3 25.0

3.3

10.0 33.3

0.0 100.0 100.0

420

2.6

13.3 22.6

120

0.8

48.3 63.3

170

4.1

11.8 25.3

0.0

17.7 41.2

0.0 100.0 100.0

14.3

42.9 57.1

0.0

75.0 100.0

0.0

54.4 63.2

0.0

40.0 43.8

0.0

0.0 50.0

8.3

58.3 83.3

0.0

45.5 72.7

0.0

94.9 97.4

42.6

59.0 68.9

19 100.0 100.0 100.0

73.5

79.4 79.4

0.0

50.0 100.0

0.0

40.0 40.0

0.0

50.0 100.0

428

2.3

7.2 10.5

0.0

12.9 73.1

0.0 100.0 100.0

0.0

3.9

7.7

7.3

69.8 86.5

208

8.7

48.6 66.8

2.7

25.7 36.5

0.0 100.0 100.0

12.5 100.0 100.0

0.0

1187

0.0

1.0

3.1

0.0

14.3 14.3

0.0 100.0 100.0

100

2.0

80.0 92.0

42.9

42.9 42.9

0.0

85.0 87.5

0.0

0.0 100.0

50.0

92.3 96.2

17.4

43.5 65.2

0.0

0.0 10.0

376

2.7

30.6 36.4

9.4

68.8 78.1

13.8

34.5 34.5

0.0

80.0 100.0

0.0

50.0 50.0

1.3

24.0 34.7

0.0

72.7 100.0

0.0 100.0 100.0

2 100.0 100.0 100.0

0.0

23.1 30.8

2.0

74.5 96.1

50.0

50.0 50.0

143

3.5

32.9 37.1

0.0

0.0 71.4

0.0

25.0 50.0

0.0

69.8 72.1

0.0

85.7 85.7

14.3

42.9 42.9

33.3

66.7 66.7

5.3

15.8 15.8

60.0

80.0 80.0

Grid cells

10 x 10 km

sac-set

172

cells

Total

137

Chapter 1

Page 7 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Other insect

Species name

Malacosoma neustria

Mallota cimbiciformis

Melandrya dubia

Melangyna lucifera

Melasis buprestoides

Meloe brevicollis

Merodon avidus

Metzneria santolinella

Microdon analis

Microdon myrmicae

Monochroa rumicetella

Mycetochara flavipes

Mycetophagus fulvicollis

Myolepta dubia

Mythimna turca

Nehalennia speciosa

Neomida haemorrhoidalis

Nola aerugula

Nosodendron fasciculare

Notodonta torva

Nudaria mundana

Oberea linearis

Oberea oculata

Ocys quinquestriatus

Odontoscelis fuliginosa

Odontoscelis lineola

Oedipoda caerulescens

Omaloplia nigromarginata

Omocestus haemorrhoidalis

Onthophagus joannae

Onthophagus vacca

Ophonus rupicola

Oplosia cinerea

Orgyia antiquoides

Orgyia recens

Orsodacne cerasi

Orthetrum coerulescens

Orthonevra elegans

Osphya bipunctata

Otiorhynchus rugifrons

Pabulatrix pabulatricula

Pachetra sagittigera

Panemeria tenebrata

Paracolax tristalis

Paragus albifrons

Paragus finitimus

Paragus tibialis

Paraphotistus nigricornis

Parasemia plantaginis

Pedostrangalia revestita

Peltis ferruginea

Pentaphyllus testaceus

Philorhizus quadrisignatus

Phimodera humeralis

Photedes captiuncula

Photedes morrisii

Phragmatiphila nexa

Phytometra viridaria

Pipiza austriaca

Pipiza luteitarsis

Pissodes validirostris

Plagionotus detritus

Platycheirus immarginatus

Platycheirus podagratus

Platycheirus transfugus

Platycnemis pennipes

Platynus krynickii

Pocota personata

Podistra schoenherri

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

993

4.6

23.4 40.9

3.7

37.0 51.9

0.0

0.0 27.3

10.7

32.1 57.1

0.0 100.0 100.0

0.0

5.3

0.0

2.4

23.8 42.9

0.0

72.4 86.2

0.0

0.0 100.0

0.0 100.0 100.0

0.0

8.3 11.1

239

0.0

6.7 20.9

0.0 100.0 100.0

14.3

53.6 78.6

630

2.4

12.5 17.5

0.0

66.7 66.7

128

0.0

55.5 61.7

546

0.2

5.5

7.0

0.0

14.3 14.3

0.0

28.6 42.9

0.0

3.9

13.7 37.3

16.7

66.7 75.0

0.0

0.0 100.0

92.9 100.0 100.0

14 100.0 100.0 100.0

20.0

40.0 60.0

4.8

85.7 85.7

1 100.0 100.0 100.0

0.0 100.0 100.0

19.0

51.9 67.1

77.5

80.0 95.0

0.0 100.0 100.0

110

0.0

53.6 82.7

0.0

50.0 100.0

0.0

0.0 75.0

0.0

33.3 66.7

23 100.0 100.0 100.0

210

1.9

54.8 59.1

1.1

9.5 23.2

0.0

8.3 16.7

0.0

2.6

18.4 50.0

0.0

80.0 100.0

1 100.0 100.0 100.0

9.0

21.8 38.5

0.0

50.0 50.0

23.8

95.2 100.0

0.0 100.0 100.0

13.3

13.3 93.3

0.0 100.0 100.0

286

21.0

30.1 36.7

163

6.8

16.6 49.1

196

6.6

25.5 76.5

0.0

28.6 28.6

0.0

15.0 30.0

0.0

92.3 92.3

0.0

63.2 63.2

9.4

21.9 56.3

0.0

44.4 48.2

50.0

50.0 50.0

0.0

44.0 92.0

0.0 100.0 100.0

0.0

10.5 47.4

1 100.0 100.0 100.0

Grid cells

10 x 10 km

sac-set

172

cells

Total

102

Chapter 1

Page 8 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Species name

Other insect

Pogonocherus decoratus

Other insect

Pogonus luridipennis

Other insect

Polymixis flavicincta

Other insect

Polymixis polymita

Other insect

Polypogon tentacularia

Other insect

Prostomis mandibularis

Other insect

Protolampra sobrina

Other insect

Pseudeustrotia candidula

Other insect

Pterostichus aterrimus

Other insect

Ptilophora plumigera

Other insect

Ptocheuusa inopella

Other insect

Pyrrhidium sanguineum

Other insect

Pytho depressus

Other insect

Rhagades pruni

Other insect

Rhynchaenus alni

Other insect

Rhynchaenus calceatus

Other insect

Rhynchaenus rufus

Other insect

Rusticoclytus rusticus

Other insect

Schrankia taenialis

Other insect

Scolytus laevis

Other insect

Scolytus scolytus

Other insect

Setina irrorella

Other insect

Shargacucullia lychnitis

Other insect

Shargacucullia scrophulariae

Other insect

Sigara hellensii

Other insect

Smaragdina salicina

Other insect

Somatochlora arctica

Other insect

Sophronia chilonella

Other insect

Spaelotis ravida

Other insect

Spathocera dalmanii

Other insect

Sphaerophoria loewi

Other insect

Sphaerophoria rueppelli

Other insect

Spiris striata

Other insect

Stagonomus bipunctatus

Other insect

Stictoleptura scutellata

Other insect

Tachyta nana

Other insect

Temnostoma apiforme

Other insect

Temnostoma meridionale

Other insect

Tenebrio opacus

Other insect

Tetrops starkii

Other insect

Thymalus limbatus

Other insect

Trachys minutus

Other insect

Trachys scrobiculatus

Other insect

Trichiura crataegi

Other insect

Trichopsomyia joratensis

Other insect

Trixagus exul

Other insect

Trox hispidus

Other insect

Typhaeus typhoeus

Other insect

Tyta luctuosa

Other insect

Volucella inanis

Other insect

Xanthogramma festivum

Other insect

Xestia agathina

Other insect

Xestia ditrapezium

Other insect

Xylophilus corticalis

Other insect

Xylota abiens

Other insect

Xylota meigeniana

Other insect

Zygaena lonicerae

Other insect

Zygaena minos

Other insect

Zygaena purpuralis

Other insect

Zygaena trifolii

Other insect

Zygaena viciae

Other invertebrateAraneus alsine

Other invertebrateAraneus angulatus

Other invertebrateAraneus marmoreus

Other invertebrateAraneus triguttatus

Other invertebrateDendryphantes rudis

Other invertebrateEnoplognatha oelandica

Other invertebrateEresus sandaliatus

Other invertebrateHeliophanus dampfi

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

0.0

0.0 100.0 100.0

0.0

566

0.4

4.6

8.7

0.0

0.0 100.0

0.0

20.0 20.0

1550

1.1

5.7 10.3

0.0

0.0 100.0

120

0.0

1.7

0.0

7.1

22.9 50.0

3.6

78.6 78.6

3.7

50.6 70.4

0.0

0.0 100.0

0.0

4.2 12.5

0.0

0.0 25.0

0.0

92.5 92.5

0.0

0.0 100.0 100.0

307

16.0

28.7 56.0

0.0

10.0 24.0

242

1.2

8.7 16.9

0.0

0.0 100.0

0.0

42.9 71.4

7.6

81.1 98.1

0.0

418

1.4

6.2 11.0

0.0

80.0 100.0

6.7

23.3 53.3

238

0.4

68.5 87.4

0.0

42.9 42.9

11.4

54.3 61.4

0.0

50.0 50.0

75.0 100.0 100.0

1.8

26.3 43.9

0.0

0.0 87.5

0.0

33.3 33.3

15.4 100.0 100.0

0.0

0.0 100.0

0.0

211

1.9

22.3 34.6

0.0

0.0 100.0 100.0

169

5.9

45.6 86.4

2.9

8.6

0.0

8.3

2.4

40.5 57.1

423

8.3

46.1 66.7

3.0

6.1

9.1

42.9

71.4 71.4

0.0

22.5 30.0

0.0

20.0 20.0

862

12.1

42.2 58.9

533

3.0

48.0 75.6

213

97.2

98.1 99.1

162

14.8

52.5 77.8

330

1.2

58.2 70.3

25.0

62.5 81.3

30.4

69.6 78.3

23.2

63.2 83.2

0.0

6.1

0.0 100.0 100.0

1 100.0

0.0 100.0

240

15.8

48.8 71.3

43.8

56.3 62.5

Grid cells

10 x 10 km

sac-set

172

cells

Total

211

Chapter 1

Page 9 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Species name

Other invertebrateHygrolycosa rubrofasciata

Other invertebrateLepidurus apus

Other invertebrateMarpissa radiata

Other invertebrateMidia midas

Other invertebrateOxyopes ramosus

Other invertebratePirata latitans

Other invertebrateSegestria bavarica

Other invertebrateThanatus formicinus

Other invertebrateXerolycosa miniata

Fungi

Agaricus devoniensis

Fungi

Agaricus moelleri

Fungi

Agaricus phaeolepidotus

Fungi

Agaricus porphyrizon

Fungi

Agrocybe firma

Fungi

Agrocybe vervacti

Fungi

Albatrellus pes-caprae

Fungi

Aleurodiscus disciformis

Fungi

Amanita eliae

Fungi

Amanita lividopallescens

Fungi

Amanita olivaceogrisea

Fungi

Amanita solitaria

Fungi

Amanita strobiliformis

Fungi

Amaurodon cyaneus

Fungi

Anomoporia myceliosa

Fungi

Antrodia heteromorpha

Fungi

Antrodia malicola

Fungi

Armillaria ectypa

Fungi

Arrhenia epichysium

Fungi

Arrhenia lobata

Fungi

Athelidium aurantiacum

Fungi

Aurantiporus alborubescens

Fungi

Aurantiporus croceus

Fungi

Aureoboletus gentilis

Fungi

Bankera fuligineoalba

Fungi

Bankera violascens

Fungi

Boletopsis leucomelaena

Fungi

Boletus aereus

Fungi

Boletus queletii

Fungi

Buchwaldoboletus lignicola

Fungi

Buglossoporus quercinus

Fungi

Calocera glossoides

Fungi

Camarophyllopsis atropuncta

Fungi

Camarophyllopsis hymenocephala

Fungi

Camarophyllopsis micacea

Fungi

Ceriporia purpurea

Fungi

Ceriporiopsis gilvescens

Fungi

Ceriporiopsis pannocincta

Fungi

Cerrena unicolor

Fungi

Chamaemyces fracidus

Fungi

Cheimonophyllum candidissimum

Fungi

Choiromyces meandriformis

Fungi

Chromocyphella muscicola

Fungi

Clavaria amoenoides

Fungi

Clavaria asperulispora

Fungi

Clavaria flavipes

Fungi

Clavaria fumosa

Fungi

Clavaria macouni

Fungi

Clavaria tenuipes

Fungi

Clavaria zollingeri

Fungi

Clavicorona taxophila

Fungi

Clavulinopsis cinereoides

Fungi

Clavulinopsis fusiformis

Fungi

Clavulinopsis microspora

Fungi

Climacocystis borealis

Fungi

Clitocybe alexandri

Fungi

Coltricia confluens

Fungi

Conocybe dumetorum

Fungi

Coprinopsis insignis

Fungi

Coprinopsis pannucioides

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

53.3

93.3 100.0

0.0

56.0 84.0

0.0

33.3 100.0

0.0

12.5 12.5

3.5

72.4 86.2

25.0 100.0 100.0

0.0

91.7 91.7

8 100.0 100.0 100.0

0.0

0.0 63.6

0.0

71.4 100.0

17.0

26.4 34.0

5.3

42.1 47.4

8.1

35.1 46.0

0.0

44.4 55.6

0.0

38.5 61.5

26.9

53.9 76.9

28 100.0 100.0 100.0

0.0

11.1 25.9

42.9

85.7 100.0

60.0

92.0 96.0

153

20.3

30.1 32.7

0.0 100.0 100.0

0.0

55.6 55.6

0.0 100.0 100.0

4.3

21.3 72.3

0.0 100.0 100.0

80.0

86.7 86.7

6.3

43.8 84.4

80.0 100.0 100.0

172

11.1

41.3 54.1

0.0 100.0 100.0

137

9.5

21.9 24.1

2.9

44.1 61.8

0.0

36.5 36.5

0.0

6.7

18.0 24.7

0.0

4.0

44.0 56.0

126

19.8

82.5 90.5

14.3

38.1 66.7

0.0

23.5 23.5

2.7

40.5 46.0

9.1

27.3 63.6

12.3

39.7 52.1

34.0

57.5 63.8

37.5

43.8 56.3

12.2

47.6 63.4

60.0

84.0 84.0

45.5

90.9 90.9

0.0

83.3 83.3

0.0

40.0 90.0

42.9

42.9 85.7

8.6

42.9 94.3

3.9

70.6 82.4

75.0

75.0 75.0

0.0

64.3 82.1

6.7

53.3 66.7

4.4

47.8 71.7

6.3

31.3 87.5

6.5

32.3 58.1

20.0

36.0 48.0

0.0

7.7 26.9

5.3

21.1 31.6

12.5

37.5 62.5

14.3

92.9 92.9

20.0

40.0 60.0

Grid cells

10 x 10 km

sac-set

172

cells

Total

Chapter 1

Page 10 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Fungi

Species name

Coprinopsis spelaiophila

Corticium expallens

Cortinarius acetosus

Cortinarius albertii

Cortinarius alborufescens

Cortinarius arcuatorum

Cortinarius areni-silvae

Cortinarius armeniacus

Cortinarius aureocalceolatus

Cortinarius betulinus

Cortinarius bulliardii

Cortinarius caesiocortinatus

Cortinarius caesiolatens

Cortinarius caesiostramineus

Cortinarius cagei

Cortinarius camphoratus

Cortinarius caperatus

Cortinarius catharinae

Cortinarius cinnabarinus

Cortinarius cisticola

Cortinarius cliduchus

Cortinarius coerulescentium

Cortinarius colus

Cortinarius cotoneus

Cortinarius elegantissimus

Cortinarius emunctus

Cortinarius eucaeruleus

Cortinarius flavovirens

Cortinarius fragrantior

Cortinarius fulvocitrinus

Cortinarius gracilior

Cortinarius humicola

Cortinarius humolens

Cortinarius imperialis

Cortinarius insignibulbus

Cortinarius langeorum

Cortinarius leucophanes

Cortinarius lilacinovelatus

Cortinarius luteoimmarginatus

Cortinarius maculosus

Cortinarius magicus

Cortinarius multiformium

Cortinarius nanceiensis

Cortinarius nymphicolor

Cortinarius odoratus

Cortinarius olearioides

Cortinarius orellanus

Cortinarius osmophorus

Cortinarius porphyropus

Cortinarius quarciticus

Cortinarius rufo-olivaceus

Cortinarius saporatus

Cortinarius selandicus

Cortinarius serratissimus

Cortinarius sodagnitus

Cortinarius splendens

Cortinarius suaveolens

Cortinarius talus

Cortinarius tophaceus

Cortinarius traganus

Cortinarius urbicus

Cortinarius variiformis

Cortinarius venustus

Cortinarius vesterholtii

Cortinarius violaceocinereus

Cortinarius xanthochlorus

Cortinarius xantho-ochraceus

Cotylidia pannosa

Craterellus cinereus

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

11.1

33.3 55.6

0.0

5.6

0.0

5.6

0.0

0.0 12.5

0.0

40.0

66.7 66.7

7.1

28.6 100.0

0.0

12.5 18.8

25.4

42.4 72.9

117

6.8

32.5 37.6

40.0

70.0 70.0

0.0

16.7 22.2

6.7

53.3 53.3

215

6.5

48.4 53.5

76.5

88.2 88.2

5.4

33.3 45.2

0.0

42.9 57.1

0.0

50.0 50.0

0.0

38.8

45.0 48.8

136

25.0

55.9 62.5

0.0 100.0 100.0

0.0

11.1 36.1

0.0

0.0 20.0

0.0

50.0 50.0

63.3

80.0 80.0

5 100.0 100.0 100.0

0.0

70.6 70.6

0.0

75.0 83.3

0.0

6.7

3 100.0 100.0 100.0

41.7

91.7 91.7

0.0

88.9 88.9

64.5

77.4 77.4

0.0 100.0 100.0

66.7

83.3 83.3

53.6

67.9 89.3

6 100.0 100.0 100.0

33.3

33.3 33.3

0.0

1.4

14.3 32.9

10.0

60.0 60.0

51.6

77.4 80.7

0.0

0.0 25.0

23.1

69.2 73.1

18.3

40.9 49.3

93.3

93.3 93.3

26.7

53.3 53.3

25.0

50.0 75.0

59.5

69.1 71.4

48.7

69.2 74.4

0.0

22.2 22.2

11.9

25.4 45.8

0.0

80.0 80.0

4.7

10.9 12.5

0.0

16.7 66.7

0.0

66.7 66.7

0.0

16.7 50.0

0.0

4.1

44.9 44.9

0.0

12.5 50.0

144

3.5

29.9 39.6

Grid cells

10 x 10 km

sac-set

172

cells

Total

Chapter 1

Page 11 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Fungi

Species name

Craterellus melanoxeros

Crepidotus cinnabarinus

Cristinia gallica

Cystolepiota hetieri

Cystolepiota icterina

Cystolepiota moelleri

Dacrymyces enatus

Dendrocollybia racemosa

Dendrothele commixta

Dentipellis fragilis

Dermoloma pseudocuneifolium

Dichomitus campestris

Disciseda bovista

Disciseda candida

Echinoderma boertmannii

Echinoderma calcicola

Echinoderma hystrix

Echinoderma perplexum

Echinoderma pseudoasperulum

Elaphomyces anthracinus

Entoloma ameides

Entoloma anatinum

Entoloma aprile

Entoloma bloxamii

Entoloma caeruleopolitum

Entoloma caesiocinctum

Entoloma callirhodon

Entoloma clandestinum

Entoloma cocles

Entoloma corvinum

Entoloma cruentatum

Entoloma cuspidiferum

Entoloma depluens

Entoloma dichroum

Entoloma elodes

Entoloma formosum

Entoloma fuscomarginatum

Entoloma glaucobasis

Entoloma griseocyaneum

Entoloma hirtum

Entoloma hispidulum

Entoloma huijsmanii

Entoloma indutoides

Entoloma jubatum

Entoloma juniperinum

Entoloma lampropus

Entoloma lepidissimum

Entoloma lividocyanulum

Entoloma longistriatum

Entoloma mougeotii

Entoloma neglectum

Entoloma ortonii

Entoloma parkensis

Entoloma placidum

Entoloma plebejum

Entoloma porphyrogriseum

Entoloma porphyrophaeum

Entoloma prunuloides

Entoloma queletii

Entoloma rhombisporum

Entoloma roseum

Entoloma scabropellis

Entoloma scabrosum

Entoloma sinuatum

Entoloma sodale

Entoloma solstitiale

Entoloma sphagneti

Entoloma strigosissimum

Entoloma transvenosum

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

14.3

66.7 76.2

0.0

62.5 62.5

73.3

80.0 80.0

15.7

28.6 37.1

19.2

23.1 34.6

20.0

48.6 60.0

44.4

44.4 44.4

0.0

60.0 70.0

27.3

68.2 86.4

75.0 100.0 100.0

10.7

61.9 73.8

2.7

43.2 54.1

0.0

50.0 100.0

50.0 100.0 100.0

54.6

54.6 54.6

52.9

64.7 64.7

14.0

20.0 34.0

35.5

48.4 51.6

12.5

21.9 34.4

0.0 100.0 100.0

15.0

55.0 75.0

13.4

50.8 82.1

12.5

6.3 37.5

0.0

22.6 58.1

0.0

0.0 33.3

25.6

69.8 88.4

0.0

0.0 100.0

15.4

69.2 96.2

0.0

41.1 78.6

22.6

50.9 83.0

0.0

60.0 100.0

20.0 100.0 100.0

0.0

50.0 50.0

33.3

52.4 57.1

25.0

91.7 91.7

14.9

38.3 76.6

0.0 100.0 100.0

21.4

35.7 78.6

220

5.9

52.7 75.9

0.0

33.3 100.0

16.7

50.0 100.0

0.0

66.7 100.0

0.0 100.0 100.0

4.8

33.3 47.6

0.0

50.0 50.0

0.0

6.3 56.3

10.0

20.0 60.0

12.7

58.7 74.6

10.7

61.3 76.0

22.5

87.8 95.9

23.5

52.9 64.7

0.0

0.0 100.0

0.0

46.7 60.0

15.4

53.9 69.2

0.0

0.0 66.7

55.6

88.9 100.0

2.4

29.3 70.7

255

4.3

46.7 73.7

3.9

26.9 46.2

20.0

60.0 71.1

0.0 100.0 100.0

0.0

31.0 72.4

9.1

45.5 54.6

1.1

16.3 19.6

8.5

51.1 59.6

2.7

32.4 56.8

0.0

0.0 100.0

0.0

66.7 66.7

0.0 100.0 100.0

Grid cells

10 x 10 km

sac-set

172

cells

Total

Chapter 1

Page 12 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Fungi

Species name

Entoloma turci

Entoloma weholtii

Entoloma xanthochroum

Erythricium aurantiacum

Exidia cartilaginea

Exidia repanda

Faerberia carbonaria

Femsjonia peziziformis

Flammulaster limulatus

Flammulaster muricata

Flammulaster novasilvensis

Fomitiporia robusta

Ganoderma resinaceum

Geastrum corollinum

Geastrum elegans

Geastrum minimum

Geastrum quadrifidum

Geoglossum atropurpureum

Geoglossum difforme

Geoglossum littorale

Geoglossum sphagnophilum

Geoglossum starbaeckii

Gloeocystidiellum clavuligerum

Gloeohypochnicium analogum

Gloeoporus dichrous

Gomphus clavatus

Gymnopus brassicolens

Gymnopus hariolorum

Gymnopus impudicus

Gymnopus inodorus

Gyromitra fastigiata

Gyromitra gigas

Gyromitra parma

Hebeloma alvarense

Hebeloma fusisporum

Helvella albella

Helvella costifera

Helvella queletii

Hemipholiota heteroclita

Hericium cirrhatum

Hericium erinaceus

Hohenbuehelia atrocoerulea

Hohenbuehelia auriscalpium

Hohenbuehelia mastrucata

Hohenbuehelia petaloides

Hohenbuehelia unguicularis

Hydnellum aurantiacum

Hydnellum auratile

Hydnellum caeruleum

Hydnellum concrescens

Hydnellum ferrugineum

Hydnellum gracilipes

Hydnellum peckii

Hydnellum scrobiculatum

Hydnellum spongiosipes

Hydnum albidum

Hydropus scabripes

Hydropus trichoderma

Hygrocybe aurantiosplendens

Hygrocybe calciphila

Hygrocybe calyptriformis

Hygrocybe coccineocrenata

Hygrocybe colemanniana

Hygrocybe constrictospora

Hygrocybe flavipes

Hygrocybe fornicata

Hygrocybe intermedia

Hygrocybe lacma

Hygrocybe ovina

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

20.0

82.2 88.9

60.0

80.0 80.0

7.7

30.8 92.3

6.1

18.2 33.3

20.0

70.0 90.0

85.7

85.7 85.7

0.0

60.0 60.0

0.0

50.0 50.0

47.6

76.2 81.0

60.0

86.7 86.7

66.7 100.0 100.0

7.4

59.3 59.3

0.0

7.5 17.9

0.0 100.0 100.0

7.7

69.2 84.6

0.0

82.4 94.1

20.0

0.0 26.7

1.7

76.7 91.7

0.0

66.7 66.7

0.0 100.0 100.0

0.0

55.6 77.8

3.5

41.4 69.0

50.0

75.0 75.0

37.5

58.3 70.8

42.2

82.2 86.7

0.0

41.2 41.2

46.0

63.5 67.6

0.0

9.1

36.4 54.6

0.0

9.1

2 100.0 100.0 100.0

0.0

2.9

9 100.0 100.0 100.0

0.0

27.6 44.8

33.3

66.7 100.0

0.0

40.0 70.0

0.0

41.9 41.9

2.5

55.0 75.0

130

13.9

31.5 37.7

6.1

67.7 70.7

7.7

30.8 57.7

51.9

59.3 66.7

32.3

51.6 67.7

14.3

28.6 28.6

0.0

14.3 14.3

114

1.8

14.9 19.3

16 100.0 100.0 100.0

5.6

37.5 68.1

243

2.5

36.2 50.6

0.0

31.5 33.7

0.0

40.0 40.0

0.0

4.1 12.2

0.0

2.2

35.6 40.0

56.9

70.8 98.5

40.0

40.0 40.0

72.7

90.9 100.0

0.0

50.9 75.5

16.7

64.3 76.2

55.6

55.6 88.9

114

3.5

49.1 70.2

100

0.0

64.0 76.0

0.0

75.0 100.0

234

1.7

35.5 66.2

154

4.6

57.1 77.9

2.4

69.1 81.0

7.4

51.9 81.5

136

2.2

53.7 75.7

Grid cells

10 x 10 km

sac-set

172

cells

Total

Chapter 1

Page 13 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Fungi

Species name

Hygrocybe punicea

Hygrocybe quieta

Hygrocybe radiata

Hygrocybe splendidissima

Hygrocybe subpapillata

Hygrocybe substrangulata

Hygrocybe turunda

Hygrocybe viola

Hygrocybe vitellina

Hygrophorus camarophyllus

Hygrophorus lucorum

Hygrophorus mesotephrus

Hygrophorus nemoreus

Hygrophorus persoonii

Hygrophorus poetarum

Hymenochaete ulmicola

Hyphoderma macedonicum

Hyphoderma medioburiense

Hyphoderma obtusiforme

Hyphodermella corrugata

Hypholoma ericaeoides

Hypholoma ericaeum

Hypsizygus ulmarius

Inocybe auricoma

Inocybe calamistrata

Inocybe cryptocystis

Inocybe flavella

Inocybe huijsmanii

Inocybe hystrix

Inocybe margaritispora

Inocybe quietiodor

Inocybe sambucina

Inocybe tenebrosa

Inocybe terrigena

Inonotus dryadeus

Inonotus hispidus

Inonotus ulmicola

Irpex lacteus

Ischnoderma resinosum

Kavinia himantia

Lactarius acerrimus

Lactarius albocarneus

Lactarius aquizonatus

Lactarius azonites

Lactarius decipiens

Lactarius evosmus

Lactarius hysginus

Lactarius lignyotus

Lactarius mairei

Lactarius mammosus

Lactarius musteus

Lactarius porninsis

Lactarius repraesentaneus

Lactarius rostratus

Lactarius scrobiculatus

Lactarius spinosulus

Lactarius uvidus

Lactarius violascens

Lactarius volemus

Lactarius zonarius

Lentaria byssiseda

Lentaria epichnoa

Lentinellus ursinus

Lentinellus vulpinus

Lentinus suavissimus

Lentinus tigrinus

Lepiota cingulum

Lepiota echinella

Lepiota fuscovinacea

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

413

3.6

49.2 77.2

232

2.6

43.5 68.1

4.2

40.3 63.9

198

2.0

46.0 78.3

5.3

47.4 73.7

22.6

93.6 96.8

100

6.0

63.0 77.0

0.0

7.3

50.0 91.5

0.0

50.0 50.0

0.0 100.0 100.0

3.4

27.1 35.6

23.1

23.1 38.5

0.0

43.8 43.8

0.0

50.0 50.0

0.0

50.0 50.0

80.0 100.0 100.0

39.3

53.6 64.3

0.0

50.0 50.0

0.0

62.5 62.5

0.0

66.7 66.7

0.0

75.0 100.0

0.0

5.6 11.1

1 100.0 100.0 100.0

0.0

25.0 33.3

0.0

17.4

47.8 65.2

0.0

28.6 57.1

0.0

25.0 25.0

0.0

37.5 37.5

0.0

25.0 25.0

8.3

22.2

88.9 88.9

0.0

253

5.5

27.3 37.6

0.0

16.4 23.6

0.0

1 100.0 100.0 100.0

213

35.2

62.4 68.5

65.2

87.0 87.0

143

0.0

21.0 25.9

6.5

41.3 52.2

0.0

0.0 90.9

2.8

28.2 31.0

7.1

46.4 53.6

0.0

44.8 50.0

0.0

25.0 25.0

0.0

61.8 61.8

0.0

11.7

15.0 33.3

31.6

36.8 40.4

0.0

53.3 53.3

0.0

27.6 39.7

0.0

7.4 25.9

0.0

50.0 50.0

0.0

9.4

18.8 59.4

0.0

52.6 52.6

251

6.4

35.5 50.2

0.0

7.1

2.2

55.6 95.6

50.0

57.1 64.3

24.2

58.2 65.9

0.0

58.8 82.4

0.0

80.0

80.0 80.0

34.4

59.4 68.8

22.0

44.0 48.0

Grid cells

10 x 10 km

sac-set

172

cells

Total

Chapter 1

Page 14 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Fungi

Species name

Lepiota grangei

Lepiota griseovirens

Lepiota ignivolvata

Lepiota ochraceofulva

Lepiota poliochloodes

Lepiota pseudolilacea

Lepiota subgracilis

Lepiota xanthophylla

Leucoagaricus badhamii

Leucoagaricus sublittoralis

Leucocoprinus brebissonii

Leucopaxillus alboalutaceus

Leucopaxillus compactus

Lichenomphalia hudsoniana

Limacella glioderma

Lindtneria trachyspora

Lycoperdon mammiforme

Lyophyllum deliberatum

Lyophyllum eustygium

Lyophyllum hebelomoides

Lyophyllum leucophaeatum

Lyophyllum semitale

Melanophyllum eyrei

Microglossum olivaceum

Mycena clavata

Mycena concolor

Mycena leptophylla

Mycena pseudopicta

Nemania carbonacea

Nemania diffusa

Omphalina lilacinicolor

Ossicaulis lignatilis

Pachykytospora tuberculosa

Perenniporia fraxinea

Perenniporia medulla-panis

Phaeocollybia arduennensis

Phaeocollybia christinae

Phaeocollybia lugubris

Phaeogalera stagnina

Phellinus laevigatus

Phellodon confluens

Phellodon melaleucus

Phellodon niger

Phellodon tomentosus

Phlebia subserialis

Pholiota henningsii

Pholiota lucifera

Pholiota squarrosoides

Pholiota tuberculosa

Phylloporus pelletieri

Phyllotopsis nidulans

Pisolithus arrhizus

Pluteus atromarginatus

Pluteus aurantiorugosus

Pluteus exiguus

Pluteus hispidulus

Pluteus inquilinus

Pluteus insidiosus

Pluteus leoninus

Pluteus pellitus

Pluteus roseipes

Polyporus melanopus

Porodaedalea pini

Poronia punctata

Porotheleum fimbriatum

Porpoloma metapodium

Porpoloma spinulosum

Psathyrella caput-medusae

Psathyrella leucotephra

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

17.0

40.4 44.7

11.4

40.9 50.0

9.1

27.3 45.5

37.9

48.3 51.7

14.3

42.9 42.9

0.0

60.0 63.3

0.0

40.0 53.3

66.7 100.0 100.0

0.0

29.4 41.2

50.0

50.0 75.0

2.9

2.9 14.3

60.0

60.0 60.0

6 100.0 100.0 100.0

0.0

66.7 66.7

33.3

33.3 50.0

57.1

71.4 78.6

7.7

30.8 30.8

0.0

5.3 10.5

0.0

20.0 26.7

0.0

7.1

28.6 28.6

0.0

31.6 57.9

11.1

22.2 33.3

2.9

48.6 81.4

12.5

47.5 60.0

14.3

71.4 100.0

40.6

71.9 75.0

11.1

88.9 88.9

48.5

63.6 66.7

13.6

40.9 59.1

0.0

12.5

52.1 60.4

89.5

94.7 94.7

41.7

54.2 56.3

0.0

0.0 100.0

0.0

11.1 11.1

0.0

60.0 60.0

33.3

66.7 66.7

0.0

50.0 75.0

25.0

25.0 50.0

7.1

26.8 33.9

252

3.2

24.6 30.6

221

11.3

35.8 52.0

164

3.1

18.9 23.8

0.0

50.0 100.0

0.0

0.0 12.5

0.0

83.3 83.3

17.8

44.4 55.6

8.3

38.3 43.3

57.1

85.7 85.7

0.0

0.0 32.3

0.0

27.3 31.8

7.7

30.8 46.2

34.8

56.5 69.6

29.0

36.8 55.3

45.5

45.5 54.6

20.0

50.0 80.0

18.0

51.3 84.6

3.7

29.6 33.3

0.0

37.5 100.0

24.6

55.4 61.5

17.2

37.9 44.8

0.0

92.9 100.0

68.4

84.2 89.5

0.0

53.9 87.2

0.0

14.3 14.3

30.0

45.0 45.0

Grid cells

10 x 10 km

sac-set

172

cells

Total

Chapter 1

Page 15 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Fungi

Species name

Psathyrella sphagnicola

Psathyrella spintrigeroides

Psathyrella suavissima

Psathyrella sylvestris

Pseudoclitocybe expallens

Pseudotomentella nigra

Psilocybe turficola

Pycnoporellus fulgens

Ramaria botrytis

Ramaria fagetorum

Ramaria fennica

Ramaria flavescens

Ramaria formosa

Ramaria krieglsteineri

Ramaria largentii

Ramaria pallida

Ramaria sanguinea

Ramaria suecica

Ramariopsis pulchella

Rhizomarasmius undatus

Rhodocollybia prolixa

Rhodocybe hirneola

Rhodocybe melleopallens

Rhodocybe nitellina

Rugosomyces cerinus

Rugosomyces chrysenteron

Rugosomyces ionides

Rugosomyces obscurissimus

Russula albonigra

Russula anthracina

Russula badia

Russula caerulea

Russula carpini

Russula decipiens

Russula emeticicolor

Russula fragrantissima

Russula helodes

Russula innocua

Russula integra

Russula laeta

Russula lilacea

Russula melliolens

Russula minutula

Russula mustelina

Russula pallidospora

Russula persicina

Russula puellula

Russula rhodopus

Russula roseoaurantia

Russula rubra

Russula sanguinea

Russula seperina

Russula torulosa

Russula turci

Russula zonatula

Russula zvarae

Sarcodon glaucopus

Sarcodon imbricatus

Sarcodon lepidus

Sarcodon scabrosus

Sarcodon squamosus

Simocybe sumptuosa

Spongipellis delectans

Squamanita paradoxa

Steccherinum litschaueri

Steccherinum robustius

Steccherinum subcrinale

Stypella dubia

Stypella subgelatinosa

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

0.0

66.7 100.0

50.0

70.0 70.0

0.0

50.0 50.0

55.6

61.1 66.7

0.0

61.1 83.3

0.0 100.0 100.0

0.0

50.0 75.0

112

11.6

47.3 54.5

162

3.1

30.9 38.3

13.3

68.0 69.3

0.0

56.0 56.0

14.3

71.4 71.4

6.3

31.3 31.3

11.1

33.3 33.3

1 100.0 100.0 100.0

130

4.6

43.1 45.4

145

20.0

48.3 53.1

22.7

45.5 45.5

27.8

50.0 77.8

1 100.0 100.0 100.0

12.5

0.0 25.0

85.7 100.0 100.0

55.6

77.8 88.9

46.2

61.5 61.5

27.3

54.6 63.6

0.0

20.0 40.0

31.4

68.6 86.3

0.0

18.2 45.5

8.7

43.5 45.7

11.4

45.5 50.0

0.0 100.0 100.0

0.0

52.6 55.3

0.0

4.7

0.0

42.9 57.1

0.0

29.4 29.4

0.0

5.0 15.0

9.1

59.1 63.6

6.3

12.5 12.5

0.0

40.0 40.0

3.1

23.7 28.9

5.4

21.4 28.6

1.8

42.9 42.9

0.0

37.5 37.5

4.8

9.5 23.8

25.0

43.8 50.0

0.0

38.1 47.6

9.1

18.2 36.4

0.0

29.4 49.0

0.0

47.1 47.1

1.5

24.2 31.8

0.0 100.0 100.0

4.0

30.0 30.0

13.8

24.1 27.6

0.0

0.0 100.0 100.0

0.0

9.7 16.1

93.3

93.3 93.3

2.4

41.5 58.5

108

0.9

39.8 43.5

26.3

73.7 84.2

7.7

74.4 74.4

0.0

0.0 100.0

75.0

75.0 75.0

85.7

14.3 85.7

50.0 100.0 100.0

73.3

86.7 86.7

53.6

60.7 64.3

Grid cells

10 x 10 km

sac-set

172

cells

Total

Chapter 1

Page 16 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Fungi

Lichen

Species name

Suillus placidus

Tomentella crinalis

Tomentella italica

Tomentella lateritia

Tomentella pilosa

Tomentella umbrinospora

Trametes pubescens

Trametes suaveolens

Trechispora silvae-ryae

Tremellodendropsis tuberosa

Trichoglossum walteri

Tricholoma apium

Tricholoma arvernense

Tricholoma aurantium

Tricholoma basirubens

Tricholoma columbetta

Tricholoma focale

Tricholoma inamoenum

Tricholoma matsutake

Tricholoma sejunctum

Tricholoma sudum

Tricholoma umbonatum

Tricholoma ustaloides

Tulostoma brumale

Tulostoma fimbriatum

Tulostoma kotlabae

Tulostoma melanocyclum

Volvariella caesiotincta

Volvariella hypopithys

Volvariella murinella

Volvariella surrecta

Xenasma pruinosum

Xenasma pulverulentum

Xerula caussei

Xerula longipes

Xylobolus frustulatus

Acarospora smaragdula

Acarospora veronensis

Acrocordia gemmata

Agonimia allobata

Anisomeridium biforme

Anisomeridium polypori

Arctoparmelia incurva

Arthonia radiata

Arthonia vinosa

Aspicilia aquatica

Bacidina phacodes

Bactrospora corticola

Baeomyces placophyllus

Caloplaca cerina

Caloplaca crenularia

Caloplaca ferruginea

Caloplaca flavorubescens

Caloplaca lucifuga

Caloplaca luteoalba

Caloplaca obscurella

Catillaria atomarioides

Cercidospora epipolytropa

Cetraria ericetorum

Cladonia bellidiflora

Cladonia botrytes

Cladonia cariosa

Cladonia carneola

Cladonia cenotea

Cladonia crispata

Cladonia cryptochlorophaea

Cladonia diversa

Cladonia floerkeana

Cladonia glauca

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

0.0 100.0 100.0

11.4

28.6 60.0

0.0

66.7 100.0

71.4

88.6 94.3

36.4

36.4 45.5

53.3

66.7 66.7

50.0

50.0 50.0

0.0

5.0 51.7

58.6

69.0 82.8

0.0

53.1 75.5

23.5

70.6 88.2

0.0

15.6 15.6

115

2.6

31.3 32.2

21.9

51.0 88.5

14.8

37.0 40.7

128

5.5

36.7 48.4

110

0.9

31.8 38.2

0.0

63.6 63.6

0.0

124

6.5

29.8 34.7

10.5

29.0 39.5

13.3

80.0 80.0

13.8

24.1 34.5

141

1.4

62.4 83.0

0.0

75.6 95.1

0.0 100.0 100.0

0.0

16.0

44.0 60.0

8.3

19.4 25.0

5.6

16.7 25.0

2.8

9.9 18.3

87.5 100.0 100.0

52.9

58.8 82.4

58.3

70.8 70.8

5.9

47.1 47.1

23.1 100.0 100.0

0.0

50.0 66.7

20.0

40.0 40.0

1 100.0 100.0 100.0

40.0

80.0 80.0

30.8

61.5 61.5

0.0

0.0 100.0

21.7

47.8 62.3

50.0

62.5 75.0

0.0

1 100.0 100.0 100.0

2 100.0 100.0 100.0

0.0 100.0 100.0

0.0

12.5

25.0 25.0

0.0 100.0 100.0

0.0

25.0 25.0

2 100.0 100.0 100.0

1 100.0 100.0 100.0

50.0

50.0 50.0

0.0

33.3 33.3

1 100.0 100.0 100.0

0.0 100.0 100.0

0.0

0.0 50.0

0.0

0.0 100.0

80.0 100.0 100.0

0.0 100.0 100.0

28.6

71.4 92.9

0.0

25.0 50.0

24.0

60.0 72.0

287

17.1

49.8 68.3

101

21.8

57.4 71.3

Grid cells

10 x 10 km

sac-set

172

cells

Total

104

Chapter 1

Page 17 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Lichen

Species name

Cladonia humilis

Cladonia incrassata

Cladonia novochlorophaea

Cladonia parasitica

Cladonia phyllophora

Cladonia pocillum

Cladonia pulvinata

Cladonia ramulosa

Cladonia rangiformis

Cladonia rei

Cladonia squamosa

Cladonia strepsilis

Cladonia subcervicornis

Cladonia subulata

Cladonia sulphurina

Cladonia zopfii

Clauzadea monticola

Collema flaccidum

Collema limosum

Cyphelium sessile

Cystocoleus ebeneus

Dactylospora parasitica

Dermatocarpon luridum

Dibaeis baeomyces

Diploschistes scruposus

Flavocetraria cucullata

Flavocetraria nivalis

Fuscidea cyathoides

Gyalecta flotowii

Gyalecta truncigena

Gyalecta ulmi

Haematomma ochroleucum

Icmadophila ericetorum

Lecanactis abietina

Lecania cyrtellina

Lecania hyalina

Lecania naegelii

Lecanora argentata

Lecanora confusa

Lecanora glabrata

Lecanora intricata

Lecanora intumescens

Lecanora leptyrodes

Lecanora orosthea

Lecanora pulicaris

Lecanora rupicola

Lecanora sambuci

Lecanora soralifera

Lecanora subrugosa

Lecanora sulphurea

Lecanora symmicta

Lecanora varia

Lecidea fuscoatra

Leptogium gelatinosum

Leptogium lichenoides

Lichina confinis

Lobaria pulmonaria

Megalaria laureri

Melanohalea elegantula

Miriquidica deusta

Mycoblastus sanguinarius

Mycocalicium subtile

Nectriopsis lecanodes

Nephroma laevigatum

Normandina pulchella

Ochrolechia androgyna

Ochrolechia frigida

Ochrolechia microstictoides

Ochrolechia pallescens

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

8.6

28.6 48.6

33.3

33.3 100.0

0.0

27.3 63.6

66.7

66.7 100.0

33.3

60.0 86.7

1 100.0 100.0 100.0

20.0

80.0 80.0

159

15.1

49.1 64.2

144

7.6

39.6 62.5

11.1

66.7 88.9

19.1

40.5 57.1

33.3

33.3 100.0

40.0

80.0 100.0

9.8

40.2 52.4

7.1

78.6 92.9

9.7

41.9 74.2

0.0 100.0 100.0

0.0

50.0 50.0

0.0

0.0 100.0

66.7 100.0 100.0

0.0

2 100.0 100.0 100.0

1 100.0 100.0 100.0

0.0 100.0 100.0

0.0

0.0 100.0

0.0 100.0 100.0

0.0

83.3 100.0

0.0 100.0 100.0

0.0

0.0 100.0

0.0

0.0 100.0

10.1

33.3 44.9

0.0

50.0 100.0

342

71.9

98.5 98.8

66.7

66.7 66.7

1 100.0 100.0 100.0

0.0 100.0 100.0

35.3

76.5 82.4

0.0 100.0 100.0

21.4

35.7 35.7

0.0

58.3 75.0

33.3

44.4 66.7

0.0 100.0 100.0

0.0

17.4 21.7

18.0

41.0 67.2

153

2.6

17.0 23.5

0.0 100.0 100.0

0.0

33.3 33.3

50.0

50.0 50.0

0.0

20.4 22.2

15.6

62.2 73.3

5.3

26.3 47.4

8.1

29.1 33.7

0.0 100.0 100.0

0.0

66.7 66.7

147

34.7

89.1 91.2

33.3 100.0 100.0

0.0

20.0 26.7

42.9

71.4 71.4

0.0

12.5

37.5 50.0

0.0

0.0 50.0

0.0 100.0 100.0

1 100.0 100.0 100.0

50.0

87.5 87.5

50.0

50.0 50.0

75.0

75.0 75.0

0.0

50.0 50.0

Grid cells

10 x 10 km

sac-set

172

cells

Total

Chapter 1

Page 18 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Lichen

Species name

Ochrolechia subviridis

Ochrolechia turneri

Opegrapha herbarum

Opegrapha ochrocheila

Opegrapha sorediifera

Opegrapha varia

Opegrapha vermicellifera

Opegrapha viridis

Opegrapha vulgata

Pachyphiale carneola

Parmeliella triptophylla

Parmelina tiliacea

Peltigera collina

Peltigera leucophlebia

Peltigera malacea

Peltigera neckeri

Peltigera polydactylon

Peltigera ponojensis

Peltigera praetextata

Peltigera rufescens

Pertusaria amara

Pertusaria coccodes

Pertusaria corallina

Pertusaria flavida

Pertusaria hemisphaerica

Pertusaria leioplaca

Pertusaria multipuncta

Phaeophyscia endophoenicea

Physcia stellaris

Placynthium nigrum

Polysporina simplex

Polysporina subfuscescens

Porina aenea

Porina borreri

Porina chlorotica

Porina lectissima

Porpidia cinereoatra

Porpidia crustulata

Porpidia macrocarpa

Porpidia soredizodes

Porpidia tuberculosa

Protoblastenia rupestris

Protoparmelia badia

Protoparmeliopsis achariana

Protoparmeliopsis macrocyclos

Pseudosagedia borreri

Psilolechia lucida

Punctelia subrudecta

Pycnothelia papillaria

Pyrenula chlorospila

Pyrenula nitida

Pyrenula nitidella

Pyrrhospora quernea

Ramalina calicaris

Ramalina pollinaria

Ramonia chrysophaea

Rhaphidicyrtis trichosporella

Rimularia furvella

Rimularia insularis

Ropalospora viridis

Sclerophora peronella

Sphinctrina turbinata

Staurothele areolata

Stereocaulon condensatum

Stereocaulon dactylophyllum

Stereocaulon evolutum

Stereocaulon pileatum

Stereocaulon saxatile

Stereocaulon vesuvianum

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

37.5 100.0 100.0

11.1

55.6 55.6

20.0 100.0 100.0

3 100.0 100.0 100.0

50.0

66.7 75.0

145

56.6

98.6 98.6

40.0

80.0 100.0

44.4

66.7 88.9

27.3

81.8 81.8

0.0 100.0 100.0

0.0

33.3 33.3

1 100.0 100.0 100.0

12.5

25.0 37.5

0.0

33.3 50.0

20.0

60.0 60.0

0.0

0.0 11.1

16.7

50.0 58.3

11.1

27.8 33.3

14.8

50.6 59.3

33.3

53.3 60.0

0.0

66.7 100.0

50.0

66.7 66.7

66.7

88.9 88.9

25.0

56.3 68.8

1 100.0 100.0 100.0

0.0 100.0 100.0

50.0 100.0 100.0

2.8

11.1 18.1

0.0

14.3 14.3

16.7

45.2 54.8

0.0 100.0 100.0

8.7

30.4 39.1

0.0

0.0 100.0

8.3

16.7 16.7

50.0

50.0 50.0

0.0

42.9 42.9

1.5

14.5 15.9

0.0

20.0 25.0

0.0

50.0 50.0

25.0

50.0 50.0

0.0 100.0 100.0

0.0

57.1 57.1

0.0

25.7 34.3

0.0

8.3 50.0

0.0 100.0 100.0

66.7

77.8 77.8

387

44.4

96.1 97.2

2 100.0 100.0 100.0

30.0

55.0 65.0

0.0 100.0 100.0

2 100.0 100.0 100.0

0.0

33.3 33.3

0.0

3 100.0 100.0 100.0

1 100.0 100.0 100.0

0.0 100.0 100.0

50.0 100.0 100.0

0.0

62.5 62.5

0.0

25.0 50.0

0.0

30.8

76.9 84.6

0.0

18.2 27.3

Grid cells

10 x 10 km

sac-set

172

cells

Total

Chapter 1

Page 19 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Lichen

Plant

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

Species name

Thelopsis rubella

1 100.0 100.0 100.0

Thelotrema lepadinum

821

54.1

97.9 98.3

Trapelia obtegens

0.0 100.0 100.0

Trapeliopsis gelatinosa

50.0

75.0 100.0

Umbilicaria deusta

0.0

50.0 83.3

Umbilicaria polyphylla

6.5

32.3 51.6

Usnea filipendula

9.1

40.9 54.6

Usnea genus

0.0 100.0 100.0

Usnea hirta

17.4

47.8 73.9

Usnea subfloridana

14.3

37.1 48.6

Verrucaria mucosa

0.0 100.0 100.0

Xanthomendoza poeltii

0.0

50.0 100.0

Xanthoparmelia conspersa

6.4

33.3 52.4

Xanthoparmelia loxodes

2.3

31.8 38.6

Xanthoparmelia pulla

0.0

41.7 66.7

Xanthoparmelia somloensis

0.0

28.6 42.9

Xanthoparmelia tinctina

0.0

27.3 36.4

Xanthoparmelia verruculifera

2.1

23.4 29.8

Zamenhofia hibernica

0.0 100.0 100.0

Alisma gramineum

0.0 100.0 100.0

Alisma lanceolatum

0.0

43.2 62.2

Althaea officinalis

151

0.0

79.5 88.7

Anacamptis pyramidalis

81.1

81.1 84.4

Anemone vernalis

25.0

58.3 75.0

Anthericum liliago

0.0

15.6 59.4

Anthyllis vulneraria ssp. danica

0.0 100.0 100.0

Asplenium scolopendrium

0.0

72.1 74.4

Botrychium matricariifolium

3.3

30.0 90.0

Botrychium multifidum

175

0.0 100.0 100.0

Bromus racemosus

11.8

35.3 72.6

Calamagrostis stricta

221

4.5

61.5 95.0

Carex buxbaumii

0.0

41.7 91.7

Carex chordorrhiza

22.9

62.9 94.3

Carex ligerica

0.0

64.8 85.7

Carex maritima

0.0

0.0 100.0

Carex paleacea

50.0

50.0 50.0

Cephalanthera longifolia

40.0

50.0 58.3

Cephalanthera rubra

87.9

98.9 98.9

Cerastium subtetrandrum

0.0

70.0 100.0

Corrigiola litoralis

0.0

90.3 90.3

Crassula aquatica

0.0 100.0 100.0

Crepis praemorsa

7.7

69.2 76.9

Cuscuta epithymum ssp. epithymum

123

0.8

51.2 82.1

Cyperus fuscus

0.0

55.0 100.0

Dactylorhiza incarnata ssp. lobelii

0.0

76.5 79.4

Dactylorhiza incarnata ssp. ochroleuca

2.7

78.4 83.8

Dactylorhiza majalis ssp. integrata

147

0.0

33.3 56.5

Dactylorhiza sambucina

127

2.4

94.5 96.9

Dianthus armeria

0.0

53.3 77.2

Draba incana

6.9

70.8 80.6

Draba muralis

0.0 100.0 100.0

Drosera anglica

216

47.2

83.8 97.7

Eleocharis parvula

0.0 100.0 100.0

Epipactis atrorubens

79.7

84.8 91.5

Epipactis leptochila

43.1

54.9 54.9

Epipogium aphyllum

2 100.0 100.0 100.0

Eriophorum gracile

0.0

81.1 94.6

Euphorbia palustris

0.0 100.0 100.0

Euphrasia arctica ssp. minor

42.1 100.0 100.0

Euphrasia dunensis

0.0

59.1 95.5

Euphrasia rostkoviana ssp. rostkoviana

0.0 100.0 100.0

Festuca polesica

0.0

46.2 96.2

Galium valdepilosum

0.0

62.5 75.0

Gentianella campestris

107

15.0

49.5 89.7

Gymnadenia conopsea

0.0

63.2 68.4

Herminium monorchis

121

5.0

80.2 89.3

Hypochoeris maculata

621

1.5

60.2 89.1

Illecebrum verticillatum

0.0

90.5 97.6

6.3

25.0 87.5

Juncus alpinoarticulatus ssp. alpinoarticula

Grid cells

10 x 10 km

sac-set

172

cells

Total

111

Chapter 1

Page 20 of 20,

Table

Suppl. Materials, Table A3.

Danish records 1991-2015 of 1371 threatened species; Erik Buchwald.

Threat listing

Taxon_group

Plant

Presence records

% in

Total iucn1- % in iucn1-

sacs

records

Species name

Juncus alpinoarticulatus ssp. nodulosus

8.2

96.7 100.0

Laserpitium latifolium

4.8

57.1 76.2

Lathyrus sphaericus

7.6

84.8 92.4

Limosella aquatica

0.0

38.1 85.7

Melampyrum cristatum

100

45.0

49.0 75.0

Melampyrum nemorosum

151

39.7

37.8 59.6

Mertensia maritima

0.0 100.0 100.0

Neotinea ustulata

0.0

62.7 66.1

Ophrys insectifera

84.4

92.2 92.2

Orobanche purpurea

0.0

8.1

Orobanche reticulata

0.0

0.0 27.8

Peucedanum oreoselinum

0.0

24.1 51.7

Pilosella cymosa

0.0

26.7 53.3

Platanthera bifolia ssp. latiflora

11.3

28.3 28.3

Poa supina

2.0

60.8 84.3

Polygonum oxyspermum

0.0 100.0 100.0

Polygonum raii ssp. norvegicum

63.6

81.8 81.8

Polystichum aculeatum

0.0

70.6 88.2

Potamogeton coloratus

0.0

57.4 94.1

Potamogeton compressus

0.0

43.5 78.3

Potamogeton filiformis

0.0

48.4 91.4

Potamogeton friesii

0.0

58.2 95.6

Potamogeton rutilus

0.0

72.7 100.0

Potamogeton trichoides

0.0

0.0 50.0

Potentilla sordida

0.0

33.3 66.7

Prunella grandiflora

0.0

60.8 68.6

Pseudorchis albida

0.0

87.5 100.0

Pulmonaria angustifolia

0.0

48.5 48.5

Pulmonaria officinalis

5.3

5.3 15.8

0.0

91.9 96.5

Ranunculus polyanthemos ssp. polyanthem 172

Rhododendron tomentosum

0.0

60.0 90.0

Rosa elliptica ssp. inodora

1.3

23.8 66.3

Rosa tomentosa

31.8

50.0 59.1

Sagina subulata

18.1

54.3 92.6

Salix repens ssp. rosmarinifolia

0.0

0.0 77.8

Scabiosa canescens

0.0

77.9 84.9

Scheuchzeria palustris

141

13.5

60.3 94.3

Schoenus ferrugineus

107

10.3

86.9 93.5

Schoenus nigricans

136

16.2

89.7 97.1

Scutellaria hastifolia

0.0 100.0 100.0

Sedum sexangulare

0.0

13.8 37.9

Selaginella selaginoides

0.0

82.4 98.0

Selinum dubium

0.0

59.7 93.0

Senecio erucifolius

0.0

75.7 81.1

Sorbus hybrida

2.8

22.2 41.7

Spergula morisonii

26.9

55.8 76.9

Stachys officinalis

0.0

12.5 37.5

Stellaria crassifolia

2.1

50.0 91.7

Subularia aquatica

0.0

75.0 100.0

Tephroseris integrifolia

107

5.6

56.1 76.6

Tetragonolobus maritimus

322

0.0

20.2 71.4

Trichophorum alpinum

0.0

57.9 100.0

Ulmus laevis

2.3

14.0 32.6

Utricularia ochroleuca

5.3

29.0 100.0

Veronica verna

2.5

30.0 56.3

Vicia orobus

135

3.7

23.0 61.5

Viola epipsila

8.8

61.8 82.4

Viola mirabilis

62.1

66.7 82.8

Viola persicifolia

176

1.7

59.7 96.0

Viola uliginosa

0.0

89.5 100.0

Grid cells

10 x 10 km

sac-set

172

cells

Total

CHAPTER 2

Marxan with Zones applied to

prioritize management for threatened forest

species in Denmark to improve reaching

2020 biodiversity targets

Erik Buchwald & Jacob Heilmann-Clausen

(In preparation for PLOS ONE)

Previous page: Red deer in front of blooming Tephroseris palustris (photo by Erik Buchwald)

Chapter 2

Research article for PLOS ONE

Marxan with Zones applied to prioritize

management for threatened forest species in

Denmark to improve reaching 2020 biodiversity

targets.

Erik Buchwald

1,2,

*, Jacob Heilmann-Clausen

Center for Macroecology, Evolution and Climate, Natural History Museum of Denmark, University of

Copenhagen, Copenhagen, Denmark, The Danish Nature Agency, Ministry of Environment and Food,

Randbøl, Denmark

* Corresponding author

E-mail: [email protected], [email protected] (EB)

Abstract

In 2016 the Danish government decided to expand the area of forest protected for biodiversity

from 11,700 to 28,300 hectares (from 1.9 to 4.5% of Danish forest area) and to base the

designations on scientific recommendations involving systematic conservation planning. The

decision was part of a "Nature Package" to help stop the decline in biodiversity and thus better

reach national, European Union (EU) and United Nations biodiversity targets for 2020. The main

part of new protected areas shall be placed in State owned forests with 10,000 hectares of new

minimum-intervention reserves and 3,300 hectares of other protection types. As an input to the

selection of new forest reserves we used

Marxan with Zones

to optimize cost-effective coverage

of threatened biodiversity, using presence data 1991-2015 for 626 forest species threatened at

global, EU or national level. The species represented three biological kingdoms and presences

were extracted from data sources comprising a total of 22 million species records. Four Marxan

zones were defined to represent different protection regimes with various cost assumptions: 1)

Normal, 2) Conifer woods with extra protection, 3) Active protection of semi-open deciduous

woods and 4) Untouched (minimum-intervention) deciduous woods. Targets were set to at least 3

or 5 representations in zones matching ecological preferences for each species, determined from

literature and expert consultation. 26 scenarios were run to inspect sensitivity to input

assumptions varying 1) cost as area or money, 2) cost of zone 2 and 3 compared to normal, 3)

exclusion of conifer affiliated species or of species not in decline, and finally 4) locking already

fully protected forests a priori to the relevant zone 4. In addition, three control scenarios were run

to test effects of including poorly known species and to evaluate the potential cost reductions

achieved by working with protection zones. Success rate for scenarios was assessed as 1) cost

(net income foregone) and 2) target achievement for 304 threatened species in decline and

therefore top priority. Results showed that target achievement varied from 199 to 262 of the 304

threatened species in decline, while cost varied from 25.5 to 68.7% of State Forest annual net

income from wood harvest. It was impossible to reach targets for all species in any of the

multizone scenarios, primarily because many forests were home to species with opposing

Chapter 2

ecological preferences. The zone designation for each forest was primarily influenced by the

biological input data, i.e. 450 of the 670 forests known to harbor threatened species were

systematically placed in the same zone regardless of scenario assumptions (375 in zone 1, 0 in

zone 2, 55 in zone 3, and 20 in zone 4). An ensuing process of designation looked into finer

geographical scales to refine protection schemes for different parts of especially the larger forests

in order to further enhance target achievement.

Marxan with zones

proved to be very effective

and is highly recommended for solving this type of conservation problem.

Introduction

Decline of global biodiversity including extinction of threatened species is a pressing issue, which

has been addressed widely in the literature (e.g. Dirzo et al. 2014; Newbold et al. 2015; Pimm et

al. 2014; Steffen et al. 2015) and in political agreements of which the United Nations (UN)

Convention on Biological Diversity (CBD) 1992 and its follow-up decisions are of major

importance (Venter et al. 2014, 2017; Watson et al. 2016). The COP 10 meeting (UN-CBD

conference of the parties) at Nagoya 2010 adopted a "Strategic Plan for Biodiversity 2011-2020"

with the mission to "take effective and urgent action to halt the loss of biodiversity in order to

ensure that by 2020 ecosystems are resilient and continue to provide essential services, thereby

securing the planets variety of life, and contributing to human well-being, and poverty

eradication" (CBD 2010).

National follow-up of the 20 so-called Aichi targets listed in the plan is essential. For Denmark

much of this is aligned with initiatives of the European Union (EU) – especially implementation

of the Birds- and Habitats Directives including the Natura 2000 program. This paper describes

one of the efforts done in Denmark to help reach the targets and mission. It focusses on forest

species and Aichi target 12: "By 2020 the extinction of known threatened species has been

prevented and their conservation status, particularly of those most in decline, has been improved

and sustained".

Danish government decided in May 2016 to expand the area of woodland protected for

biodiversity from 11,700 to 28,300 hectares (from 1.9 to 4.5% of Danish forest area). The main

part of new protected areas should be placed in State owned forests, with 10,000 hectares of new

minimum-intervention reserves and 3,300 hectares of other protection types (Anon. 2016).

Designations were to be based on scientific recommendations and to follow-up on the forest

reserves designated in the 1990'ies based on the 1992 "Strategy for natural forests and other forest

types of high conservation value in Denmark" (Anon. 1994). Woodlands covered 624,782 ha of

Denmark in 2015, or 14.5% of the land area with 50/50 proportion of conifers and deciduous trees

(Nord-Larsen et al. 2016).

Chapter 2

Science recommends the use of systematic conservation planning (SCP) for ensuring cost-

effective biodiversity protection (Margules & Pressey 2000; Moilanen et al. 2009;Venter et al.

2014, 2017; Watson et al. 2016). SCP has been developed over the past few decades in order to

provide best possible guidance to decide where and how to efficiently achieve conservation goals

(Beyer et al. 2016; Kukkala & Moilanen 2013; Moilanen et al. 2009).

Numerous studies using systematic conservation planning have been published (Kukkala &

Moilanen 2013) but only a minority have been applied in reality (Knight et al. 2008). One of

several reasons for this implementation gap is that reality is quite complex compared to

theoretical model studies and involves many political and socio-economic issues and constraints.

An important aspect in this context is that many planning studies only discern between

"protected" and "unprotected" in the planning algorithms, which in many cases is too simplistic

and may overestimate the economic costs of conservation (Watts et al. 2009; Wilson et al. 2010).

Wilson et al. (2010) found that accounting only for the contribution of protected areas could

overestimate the required expenditure by 15 times, and the area requiring strict protection by

almost 50 times.

Refining "protection" to different categories or management "zones" relevant to the ecological

requirements of different threatened species is thus an interesting solution, since some species

may actually require less than strict protection (e.g. Bernes et al. 2015; Brunet et al. 2010;

Lindenmayer & Franklin 2002; Lundström et al. 2016; Peterken 1996). In strongly human-

influenced landscapes like Europe, pristine nature is almost lacking and biodiversity is mainly

associated with semi-natural habitats with a stronger or weaker cultural impact. This is also true

for forest biodiversity that survives in fragmented forest landscapes heavily influenced by modern

forest management, and lacking natural dynamics and old-growth structures (Burrascano et al.

2013; Christensen & Emborg 1996). In many landscapes, the largest biodiversity values are found

in landscapes with legacies from historical land-use types, e.g. pollarding, coppicing or forest

grazing (Lindenmayer & Franklin 2002; Peterken 1996). The most suitable means to protect these

values are debated (e.g. Götmark 2013; Vera 2000), with recommendations spanning from

continuation or reestablishment of traditional management systems like wood pasture or coppice

(e.g. Peterken 1996; Tybirk & Strandberg 1999) to non-intervention (Petersen et al. 2016a). These

different perspectives on forest conservation reflect differences in conservation baselines: Focus

on traditional management types are linked to a historical perspective on nature, appreciating

habitat continuity and local species pools as most important (Lindenmayer & Franklin 2002).

Focus on non-intervention on the other hand builds on natural dynamics as the trademark of

nature, and appreciates species turnover as a natural response to these dynamics, with a risk of

local species loss due to unintended vegetation dynamics especially if natural grazing is missing

(Götmark 2013; Nilsson 2009; Vera 2000).

Chapter 2

The risk of species loss due to minimum or non-intervention is most evident for species

associated with the more open parts of the forest - open habitat continuum (Lindhe et al. 2005;

Tybirk & Strandberg 1999; Vera 2000). The reasons for this can be found in a combination of

landscape history and present pressures: The species communities of Europe including Denmark

have co-evolved during millennia in non-fragmented primeval landscapes with a suite of large

animals, that gradually went functionally or totally extinct during the last thousands of years

(Andersson & Appelqvist 1990; Dirzo et al. 2014; Nielsen 2009; Owen-Smith 1992; Vera 2000).

More recently, the Danish and other NW European forest landscapes have changed dramatically

over the last 200 years from a human-induced nutrient-poor and over-exploited situation with

relatively open and mainly grazed woodlands (Odgaard 1994; Timmermann et al. 2015; Tybirk &

Strandberg 1999; Vandekerkhove et al. 2012) into an air-pollution induced nutrient- and CO

enriched situation, usually without grazing. This has generally led to unnaturally high, dense and

lush vegetation (Kahle 2008; Timmermann et al. 2015; Tybirk & Strandberg 1999). Furthermore

forestry has changed most Danish woodlands into mosaics of biologically young, even-aged

stands with a high percentage of monocultures and non-native conifers (Christensen & Emborg

1996; Feilberg 2004; Nord-Larsen et al. 2016) and with only minor proportions of more natural

forest and open habitats. If simply left to succession such woodlands will keep their homogenous

stand character for many decades while growing darker and denser, and will thus be unlikely to

provide suitable habitats for most threatened species in the short- to medium term (e.g. Lindhe et

al. 2005; Nilsson 2009; Tybirk & Strandberg 1999; Vera 2000). A range of other threats like

climate change, isolation, invasive species, and extinction debts may also require some level of

action, if species losses are to be avoided (Geldmann et al. 2014; Joppa et al. 2016; Timmermann

et al. 2015; Virkkala et al. 2013; Wind & Pihl 2010).

To counteract negative effects of regrowth, eutrophication and darkening and to speed up the

formation of old growth structures some experts argue that active restoration to enhance e.g. old-

growth structures can be combined with non-traditional or close-to-nature forestry to protect

biodiversity, potentially in a more cost-effective way than non-intervention (e.g. Brunet et al.

2010; Götmark 2013; Lindenmayer & Franklin 2002; Peterken 1996).

In this study we applied systematic conservation tools as part of the previously mentioned

governmentally decided effort targeting biodiversity conservation in the Danish State Forests,

which is planned to be implemented during the coming decade (Naturstyrelsen 2018). Compared

to previous reports on systematic conservation planning targeting forest biodiversity in Denmark

(Petersen et al. 2016a,b) we worked with taxa across three biological kingdoms, involving all

terrestrial and amphibious species threatened at national or international level, and with a narrow

focus only targeting State Forests. Further, we worked with quantitative species targets and a

zonation approach differentiating four levels of protection, as a tool to reduce costs for protecting

Chapter 2

threatened species that are not benefitting from non-intervention (Nilsson 2009). A number of

scenarios varying costs associated with intermediate protection zones were run to handle

economic uncertainty, and similar evaluation was done for different levels of target, included

species groups and previous protection.

It would have been optimal to analyze private and State forests together, and this has been done

by Petersen et al. (2016b) who analyzed priority forest areas countrywide for Denmark at 10 km

grid cell scale. For private forest owners interested in biodiversity protection the Danish

government has set up a subsidy program which can guide subsidies to the most relevant forests

based on Petersen et al. (op.cit.). The present study uses more detailed input data available for

State Forests but not available for private forests.

In the rest of this paper the term

forest

is used as a description of State Forest property polygons

including other habitats than woods, while the terms

woods

and

woodland

are used in the meaning

of areas with >10% tree cover (FAO forest definition). Woods in Denmark generally have more

than 50% tree cover (crown projection) and mostly 80-90%.

Material and methods

Several available methods and software systems for Systematic Conservation Planning were

screened including Zonation, C-Plan, Marxan and integer programming methods (Moilanen et al.

2009). This resulted in the choice of Zonae Cogito (Segan et al. 2011) incorporating Marxan with

Zones (Watts et al. 2009) as analysis platform, due to 1) their good documentation, 2) widespread

use, 3) ability to work efficiently with several different types of protection / management at the

same time (zones), 4) ability to simultaneously solve the planning problem for many hundred

species distributed at many hundred different sites and 5) incorporation with GIS to support an

iterative interactive planning process (Segan et al. 2011; Watts et al. 2008a; Wilson et al. 2010).

In this paper all further references to Marxan deal with Marxan With Zones.

The problem to be solved in this study was of the "minimum-set" type (Possingham et al. 2006) to

meet quantitative conservation objectives as cheaply as possible by optimizing which sites should

be selected for protection using a complementarity approach.

Data

Danish State Forest data (status 2016) were supplied by The Nature Agency of The Ministry of

Environment & Food of Denmark and covered 202,402 ha (4.7% of Danish land surface). The

area was composed of 976 administrative polygons varying from less than 0.1ha up to 6,399 ha in

size, and included lakes, heaths and other habitats besides woodland. Woods covered 105,919 ha

Chapter 2

(52.3%) of which 41% were deciduous and 59% conifer dominated. For each polygon economic

data in the form of annual potential net income from deciduous and conifer wood harvest was

provided by The Nature Agency, with a note that the figures were model-based using 2016 price

and expense levels. The modelled values do not necessarily correspond to reality, but were

considered to be fine for comparison of relative values and for estimating annual net income loss

if the woodland was to be protected.

For 18 polygons with negative net income the cost was changed to zero because the negative cost

values were small and would probably not be generally implemented in real life. Another 327

polygons had net income at zero being already totally protected woods or other biotopes with no

relevant wood production e.g. lakes with bordering trees.

Study species (N=1,932) were delimited as being terrestrial or amphibious species known from

Denmark, and being globally or nationally red-listed (IUCN 2015; Wind & Pihl 2010) in the high

threat categories (RE, CR, EN, VU), plus birds listed on annex I of the EU Birds Directive (EU

1979) and species listed on annexes II, IV or V of the EU Habitats Directive (EU 1992). These

species are henceforth noted as threatened. Taxonomy and naming was updated to match the

standard species checklist of Denmark maintained by the Danish Biodiversity Information

Facility (DANBIF 2016).

Study species were placed in two priority classes A or B (Table 1) based on the geographical

scope of threat combined with trend information about populations (declining or not) in red lists

(IUCN 2015; Nieto & Alexander 2010; Wind & Pihl 2010) and in EU reports on Natura 2000

species (EIONET 2013a, 2013b). Globally threatened species were all taken as first priority (A),

while species threatened at EU and national level were only taken as priority A if they were

reported as declining in Denmark.

In total about 22 million available species records were retrieved for the 1,932 study species from

a number of sources (S1 Table). Occurrences were extracted from the study period 1991-2015,

quality-checked and referred to localities yielding detailed occurrence data (N= 267,438) for

1,378 species. The resulting data-set was uploaded to GBIF (Buchwald 2018a). For 554 study

species no occurrences were found in the available records. These comprised 231 regionally

extinct (RE) species not found or refound in the study period 1991-2015 plus 323 species not

extinct according to the Danish red list, but missing records in the available data sources. These

missing species are mostly lichens (Ascomycetes), some families of small beetles (Coleoptera)

and twirler moths (Gelechiidae) where the relevant specialists have not made records digitally

available, but have provided data to the redlisting process.

Study species were defined as either obligate forest, facultative forest or non-forest species, and as

obligately or facultatively associated with coniferous or deciduous trees, based on previous work

Chapter 2

on the habitats of Danish species (Petersen et al. 2016a,b). Species not included in Petersen et al.

(2016b) were referred to habitats using the same methodology, i.e. using data on detailed biotopes

of each species recorded in the Danish Red list Database (Wind & Pihl 2010) supplemented by

expert consultation.

Species only associated with non woodland biotopes were scored as non-forest species, species

with only woodland biotopes as obligate forest species, and species with both woodland and

openland biotopes as facultative forest species. Facultative forest species thus included species

found both in fully open biotopes, e.g. heaths, moors or grasslands, and in woodland biotopes like

glades, forest rims or forest interior (Table 1 & 3). Non-forest study species were excluded from

the present study, but were analyzed for further protection in another sub-project (Buchwald

2018b).

Species were noted as saproxylic (involved in or dependent on wood decay) if they were included

as facultatively or obligately saproxylic in any of the available lists of saproxylic species relevant

for Denmark (Alexander 2002; Köhler 2000; Nieto & Alexander 2010; Stokland & Meyke 2008;

Wind & Pihl 2010). In total 156 species (24.9%) of fungi (n=105), insects (n=50) and

Pseudoscorpions (n=1) were noted as saproxylic. Threats and more specific ecological

requirements (e.g. soil or climate affinities) were also noted for each species, based on the

detailed literature and expert based information in the Danish Red list Database (Wind & Pihl

2010).

For a few species with missing or insufficient information supplementary species-specific

literature was consulted. Species experts were finally consulted to quality assure critical taxon

groups not well-known to the authors.

Table 1. Status, population trend and overall habitat for the 1066 study species recorded in

State Forests.

Priority

Scope of threat

Globally threatened (IUCN red list level

CR, EN or VU)

EU listed in annex 1of the Birds- or

annex II, IV or V of the Habitats

Directive

Danish Red list (RE, CR, EN or VU)

Danish Red list (RE, CR, EN or VU) or

EU species of the above-mentioned

annexes.

Decline

reported?

Yes or no

Yes

Forest

species (N)

Non-forest

species (N)

Total

(N)

Yes

290

321

276

128

566

449

Total

626

440

1066

Chapter 2

Using ArcView GIS the species occurrences were referred to the above-mentioned 976 State

Forest polygons. These forests had documented occurrence of 1,066 (77.4%) of the 1,378

threatened species which had records from Denmark 1991-2015 comprising 626 forest species

and 440 non-forest species (Fig 1a,b).

Fig 1a,b. Distribution by taxon group of the 1066 threatened species found in the 976 State

Forest polygons: a) absolute species numbers, b) percentages.

Chapter 2

Analysis methods

Four zones were defined in Marxan with Zones (Watts et al. 2009) to describe in broad terms

different categories of forest protection which were possible to relate to the habitat requirements

of species and to potential costs of protection (adjusted on the basis of Anon. 1994; Boch et al.

2013; Buchwald 2005; Götmark 2013; Peterken 1996): 1) State Forest with normal protection

level including wood production and harvest, 2) Conifer woodland with extra species protection

efforts, 3) Actively protected deciduous woodland, and 4) Minimum-intervention deciduous

woodland (Table 2).

Zone 1 reflects the existing management rules for all Danish State Forests which include a

number of protective biodiversity measures - most importantly enhancement of natural hydrology

and deadwood, no clear cuts, and protection of a minimum of 5 trees per hectare to reach natural

old-age and eventual natural death and decay.

Zone 2 was defined as woodland where extra protection is needed for threatened species

dependent on conifers. All coniferous forests in Denmark derive from plantations since 1750

because natural stands of Scots pine (Pinus

sylvestris)

were exterminated in the preceding

centuries. As a consequence no genuine old-growth conifer forests exist in Denmark, and most

threatened species associated with conifers depend on planted conifers or on early succession

stages on very poor soils (e.g. Flensted et al. 2016), that may not benefit from non-intervention.

Zone 3 was targeted at deciduous forest species with high light requirements, including need for

open biotopes or open-grown trees (Lindhe et al. 2005; Nilsson 2009), and defined by less strict

protection than zone 4, but involving active conservation management to create or maintain

wood-pasture, meadows, glades, old veteran oaks, other specific host tree species or sunlit

deadwood by means of selective cutting, grazing, coppicing or other tools. Cost levels of zone 3

were assessed to be variable depending on type and intensity of conservation measures and

amount of restrictions necessary on wood harvesting. This was reflected in applying three levels

of potential costs in the analysis.

Finally, zone 4 was defined as minimum-intervention deciduous woods.

Table 2. The four zones chosen and different levels of cost assessed.

Zone Short name

Normal

Conifer

Active

Untouched

Description

No extra protection (available zone)

Woods given extra protection for species needing conifers

Active conservation management in deciduous woods

Minimum-intervention deciduous woods

Cost

10 or 25%

50, 75 or 100%

100%

Chapter 2

Forest species were split into eight groups (Table 3) related to the four Marxan zones, based on

habitat preferences noted for each species. The split into groups was done based on the

abovementioned species data regarding preferences for a) deadwood habitats (saproxylic species),

b) open or shaded habitats (light demand), c) conifers versus deciduous trees and d) long

untouched woodland habitats. Because all study species are threatened, zone 1 (Normal) was not

assessed as suitable as preference for any species.

Species with narrow preferences were referred narrowly to one zone, while species with less strict

requirements were referred to combinations of two zones. The species information used for

referring each of the 626 forest species to the Marxan zones is listed in S2 Table.

Table 3. Characteristics of species groups and their inferred preferences for the four defined

Marxan zones. Species with facultative or obligate preference for conifers were excluded

from group 1-5.

Group

Total

174

102

147

626

Group name

Open

saproxylic

Open

woodland

Woodland

saproxylic

Untouched

deciduous

Other

deciduous

Conifer

saproxylic

Conifer

obligate

Conifer

facultative

Forest

species

Basic

zone(s)

3+4

2+4

2+3

Habitat preferences / requirements / associations

Saproxylic species associated with deciduous trees found in open habitats

but also with woodland habitats.

Non-saproxylic species associated with open habitats but also with

deciduous woodland habitats.

Saproxylic species of deciduous trees associated only with woodland

habitats and not with open habitats.

Non-saproxylic species associated with non-intervention deciduous woods,

but not with open habitats.

Non-saproxylic species associated with deciduous woods, but neither

associated neither with non-intervention nor with open habitats.

Saproxylic species with preference for coniferous trees.

Non-saproxylic species associated only with conifers or coniferous

woodland.

Non-saproxylic species preferring conifers or coniferous woodland, but also

associated with deciduous trees or open habitats.

Basic zones were changed for 15% of the 626 species after expert consultation (S2 Table shows final zone

preferences). For 532 species (85%) the consulted species experts agreed with the basic zone(s) noted in

table 3, while 71 species were given a supplementary zone, 5 species a changed zone and 18 species one

zone less in order to match expert knowledge.

Species referred to zone 3 (Active zone) include many multi-habitat species where woodland may

be a less important marginal habitat, while open habitats like grass- or heathlands are the primary

biotope. For such species zone 4 Untouched, would be unnecessarily expensive or even counter-

productive. Optimal biotopes for such species (e.g. light-loving insects like butterflies

Chapter 2

(Papilionoidea)) are open habitats surrounded by or intermixed with woods (managed or

unmanaged) providing a warm and calm microclimate, with transient scrub or tall herb

communities. Other examples of zone 3 species include saproxylic beetles (Coleoptera) needing

both rich nectar sources from flowers and warm forest rims with dead wood and veteran trees.

Loss of open habitat to succession and tree encroachment is an important threat to these species

(Lindhe et al. 2005; Nilsson 2009). In line with this the protection of zone 3 does not include a

total cessation of wood harvest, but focusses on active management of woodland and of open

habitats intermixed with trees or groves. The combination of open habitat management with some

wood-harvest allows zone 3 to potentially have relatively low costs, but with a rather high level of

uncertainty reflected in the analysis cost.

In order to run the Marxan optimization a number of assumptions need to be made about

conservation target and other input. The general objective of halting the loss of biodiversity was

quantified into a target of getting at least 3 sites / 5 sites for each species protected in State Forests

with a protection regime matching the ecological requirements of the species (Nilsson 2009;

Villard & Jonsson 2009). There is no guarantee that 3 or 5 sites will be enough for long-term

species survival, but for many threatened species it amounts to 100% of extant known sites in

State Forests and can be supplemented by sites for the same species outside of the State Forests

(Cabeza & Moilanen 2001; Justus et al. 2008; Petersen et al. 2016a; Tear et al. 2005).

Other inputs that were varied in two or more levels were cost (as money or as area, with several

levels of cost %), which species groups to base the optimization on, and finally if already fully

protected forests should be a priori locked as such in the analysis. Table 4 gives an overview of

the assumptions and input for all 26 scenarios run. Not all combinations of assumptions were run

because of the large number of possible combinations (192). Instead scenarios were successively

added in order to allow pairwise comparisons. Assumptions that resulted in relatively expensive,

less effective or unrealistic solutions were dis-continued. In this way sensitivity to the various

assumptions could be assessed.

Scenarios including only priority A species implicitly make the assumption that priority B species

can persist in the matrix without extra protection (zone 1). This has by definition been the case

until now because species were only taken as priority B if they were not in decline. On the other

hand the species in decline have a diversity of ecological preferences, requirements and threats,

meaning that diversity in protection regime is necessary. This is reflected in the differences

between zones 2 (Conifer), 3 (Active) and 4 (Untouched).

Forests already protected as untouched were a priori locked to the corresponding zone 4 in

scenario O – Z if the forest matched the following criteria: 1) 100% of the total forest area already

decided protected untouched AND woodland area at least 5 hectares, OR 2) At least 100 hectares

Chapter 2

of woodland already protected as untouched AND it occupies more than 45% of the forest area.

The cut-off levels of 5 ha and 45% were selected to ensure inclusion of large and important extant

reserves without a priori locking a long list of forests to zone 4. Seven forests matched the criteria

(S3 Table).

Table 4. Overview of the 26 scenarios.

Scenario Target

Cost

(Reps.) Zone 2 Zone 3

(%)

Five

Three

Five

Three

Five

Three

Five

Three

Five

Three

Five

Three

100

Species Priority

(N)

species

included

626

516

304

626

516

304

626

304

626

304

All

Priority A

All

Priority A

All

Priority A

All

Priority A

Conifer

associated

species

included?

Yes

Cost as

money

area?

Money

Area

Money

Area

Money

Area

Money

Area

Money

Area

Money

Area

Money

Area

Money

Area

Money

Area

Money

7 already

untouched

forests

locked

Yes to z4

Cost was always set at 100% for zone 4 (Untouched) and at 0% for zone 1 (normal or available

zone). Reps. = representations.

The first 18 scenarios (A-R) were pairwise identical except for costs being as money versus as

woodland area (Table 4). To ensure that all polygons had a non-zero cost as required by Marxan,

1,000 kroner (approx. 133 Euros) was added to all costs before calibration and runs and

subtracted from costs after runs.

In order to produce valid, robust results and to run scenarios efficiently, standard Marxan

calibrations were undertaken for number of runs, number of iterations and Species Penalty

Factors, SPF (Ardron et al. 2010; Segan et al. 2011; Watts et al. 2008a, 2008b, 2009, 2011).

Because all targets were not simultaneously achievable the methods of calibration in Watts et al.

Chapter 2

(2008a) were applied (Watts et al. 2011). As a standard 100 runs was chosen as in Watts et al.

(2008a) although scenario results were already very similar for number of runs above 10.

Calibration showed that scenario results were similar above 10,000 iterations. Because slight

lowering of cost continued up to 10 million iterations that value was chosen. The difference

between the calibrated SPF for money and area scenarios (7.188 and 7.75 respectively) had no

effect on zone results so the average SPF figure of 7.47 was used. The calibrated input settings

and Species Penalty Factor were kept constant over all scenarios.

Success rate for scenarios was assessed by 1) cost (annual net income foregone) and by 2) how

many of the 304 priority A species that achieved their target. For species with occurrence in fewer

polygons than the target level (3 or 5) the target was truncated to achieving 100% of forests with

occurrence. As a supplement and to assess how far off-target species were, the relative target

achievement for each of the 304 priority A species in each scenario was computed. The average

target achievement (ATA) equals 1 if all species reached their target.

On the basis of results of the first 26 scenario runs, a final test of validity and sensitivity to

assumptions was carried out as three variations of scenario Q (Q1, Q2 and Q3). These were run to

see how much results would deviate if Q1) a more conservative subset of 115 study species

overlapping Petersen et al. (2016b) was used targeting well-known and well-mapped species in

Denmark, so that data could be assumed to be closer to presence-absence data (on species

populations), and thereby better satisfy the theoretical assumptions behind optimization in

Marxan; or Q2) zone preference was extended with zone 4 (Untouched) for all study species not

having that preference defined, because given sufficient time, space and natural dynamics all

species might be able to survive with non-intervention. Finally, in scenario Q3) zone preference

was set to only zone 4 (Untouched) for all study species to evaluate how much the economic costs

can be lowered by working with more than one protection zone. All other assumptions were

unchanged from scenario Q. That scenario was chosen because it had intermediate cost

assumptions and highest absolute target achievement.

Results

Study species occurred in 692 State Forest polygons (70.9%) of which 670 polygons with

presence of defined forest species were included in the Marxan analyses (woodland total 103,234

ha). The remaining 306 polygons without known occurrence of threatened forest species generally

had little woodland area (total 2,684 ha) and were manually assigned to zone 1 (Normal zone).

European hare (Lepus

europeaus)

was the most widely distributed threatened species being

known from 321 different polygons, while 652 study species (396 forest species) each occurred in

less than 5 polygons (Fig 1a). Overall, fungi and insects dominated the dataset, in line with the

Chapter 2

dominance of these species groups in the Danish biota (and red list) (Fig 1a). Fungi (incl. lichens)

clearly dominated among species with records in less than 15 polygons, while insects had a more

even distribution across frequency classes (Fig 1b). Birds, herptiles and to a lesser extent

mammals tended to have much wider distributions than species in other taxonomic groups.

Hotspots, i.e. polygons with at least 100 threatened study species were widely spread over

Denmark and comprised seven forest polygons in the size range 538 to 3845 ha. On average 25%

of their wooded area was already protected, i.e. six of them had protection corresponding to zone

3 Active (1-87% of each polygon) while untouched protection covered 3-24% of each polygon.

Tisvilde Hegn (1992 ha) was the most species-rich forest having 192 different threatened species.

Fig 2 shows the zone affinities and taxonomic groups of the 626 threatened forest species known

from the State Forests.

Fig 2. Zone affinity by taxonomic groups for the 626 threatened forest species.

The Marxan scenario runs assigned 450 forest polygons to a non-varying zone. This combined

with the above-mentioned 306 polygons without forest species meant that totally 756 (77.5%) of

all forests were systematically placed in the same zone in all scenarios regardless of assumptions

(Table 5), while 220 forests had zone assignment dependent on choice of assumptions.

Chapter 2

Table 5. Assignment of 976 State Forests to zones across 26 scenarios.

Short name

Always zone 4

Always zone 3

Always zone 2

Zone variable

Always zone 1

Total

Untouched

Active

Conifer

Normal

Forests (N)

220

681

976

Wooded

area (ha)

7,824

9,044

62,253

26,797

105,918

Money

value (%)

100

Details

S4 Table

S5 Table

None

S6 Table

S7 Table

Details about each forest can be seen in S4-S7 Tables (area, woodland types, existing protection, Marxan

zones chosen, species numbers, percentage of observations in already protected parts of the forest, and

zone results for scenarios O and Q if variable).

Depending on scenario 26-58% of the State Forest wooded area was assigned to zone 1 (Normal),

while 9-23% was assigned to the untouched zone 4 (Table 6 and S8 table). 0-24% and 15-41% of

the wooded area was assigned to Zone 2 (Conifer) and zone 3 (Active) respectively, showing the

wide variability of results. Economic cost varied over scenarios from 26-69% loss of potential

annual net income from wood harvest.

Table 6. Average scenario results grouped by assumptions and number of included species.

Sorted by "Target" and then by "Target met".

Tar-

get

Target

met (of

304

species)

255

259

199

218

230

231

Scenarios

scen-

arios

species

included

626

304

516

626

304

Cost

assump-

tions

Low end

High and

low

High end

High and

low

High end

Medium-

low

area

cost%

annual

(ha)

(n)

GHOPVZ

CDKL

ABIJSW

EFMNR

QUY

36 to

26 to

58 to

43 to

46 to

38 to

17338

11565

22606

21623

16483

15281

z = zone. All area measures deal with woodland area, i.e. excluding unwooded areas like

grasslands etc. Range is only given for cost%, because range was small or zero for other

columns. See S8 table for detailed results for each scenario.

Area-based solutions were on average 0.16% more expensive than money-based solutions (range:

0.72% cheaper to 1.02% more expensive, S8 table). Target achievement measured as number of

priority A species matching target was on average 0.07% better for area-based scenarios (range:

Chapter 2

0.93% worse to 1.29% better). As these differences were minor and monetary cost was deemed

most relevant, the last 8 scenarios were run with money-based cost only.

Exclusion of species associated mainly with conifers (Scenario C, D, K, L) was 8.3 to 12.0%

more expensive than including them (Table 6, S8 table). The reason was that no forests were

assigned to the less expensive zone 2 Conifer, so that all species protection even of species with

broad preferences, e.g. forest-glade species, had to be in the more expensive zones 3 and 4. Target

achievement was also low, so conifer preferring species were not excluded in the following

scenarios.

Scenarios including both priority A and B species (scenario A, B, I, J, S, T, W, X) were 11-43%

more expensive and with 1.2-7.3% lower target achievement than scenarios based only on priority

A species (Table 6, S8 table).

Lowering cost assumptions from e.g. 100 to 75% (zone 3) or from 25 to 10% (zone 2) of course

resulted in less costly results but made no changes (scenarios A/I, B/J, E/M, F/N, Q/U, S/W, T/X,

U/Y, V/Z) or only minor changes (O/V) to target achievement, implying that the biological input

data were the main driver of zone results. The zone-forest constellation was thus identical for

scenarios Q, U and Y although cost assumptions were different (Table 6). Similarly, zone

constellation was almost identical for scenarios O, V and Z (S8 table). This simple sensitivity

analysis showed that zone results were generally not sensitive to changes in the economic cost

assumptions.

A priori locking seven already untouched forests to zone 4 (scenario Q compared to M) resulted

in 0.78% improvement of average target achievement to the highest level of any 5-representation

money-based scenario but at a 1.1% higher cost (S8 table). As target achievement had high

priority, and it was not on the agenda to delete protection of already protected forests, all further

scenarios kept the a priori lock to zone 4 for these seven forests.

Not surprisingly, a change of target from 5 to 3 representations of each species had a major

influence on results as seen from e.g. scenario G compared to E, or O compared to Q (S8 table).

In both cases about 30 more (12-13%) priority A species achieved the target at a greatly reduced

(-29.5 to -30.4%) expense. But then it must be noted that the absolute protection level is not as

high as in the 5 representation scenarios (Fig 3). Scenario Q/U/Y with 5 as representation target

had almost as many species (258/258/258) represented 3 times as the corresponding 3

representation scenarios O/V/Z (261/258/258) and had many more species covered 5 times.

Chapter 2

Fig 3. Money-based scenarios sorted by target level, by relative achievement of 5

representation target, and finally by cost (% loss of annual income from wood harvest).

The most cost-efficient scenarios are thus to the right.

The highest absolute target achievement occurred for five representations identically in scenarios

Q, U and Y, where costs varied from 38-46% of net income depending on the cost assumptions

(Table 6). The zone constellation of these three scenarios was identical and un-influenced by the

varying cost assumptions making this constellation of protection robust and most cost-efficient of

the five representation scenarios.

The zone constellation of scenario Q, U and Y included 50 forests selected for untouched (zone 4)

covering 15,281 hectares of woodland, including 2,256 ha already protected as untouched,

besides 2,676 ha protected as grazing forest or selective cutting for biodiversity reasons. The

hitherto unprotected parts thus comprise 10,349 ha. The gap species, i.e. species not meeting their

target are identical for scenario Q, U and Y and listed in S9 table with details.

Chapter 2

Table 7. Variant scenario results compared to scenario Q.

Target Scenario N

Cost

z4 cost%

met (of

species

assumptions area area area area annual

(ha) (n)

304

included

species)

231

304

intermediate

46 15281 50

166

115

intermediate

33 12771 33

304

intermediate

48 26119 69

304

intermediate

59 56679 203

NA = Not applicable. z = zone. Not noted assumptions are as in scenario Q (see table 4).

The variant scenario Q1 based on a more limited number of well-known and well-mapped species

assigned 33 forests to zone 4 Untouched (12,771 ha) as opposed to 50 forests and 15,281 ha in

scenario Q (Table 7). It had costs of 33.1% instead of 46.1%, but missed the target for 138 of the

304 priority A species. For most taxon groups the relative proportion of gap-species was similar

between scenario Q and Q1 (results not shown), but lichens were missed to a greater extent (20%

of gap-species being lichens in Q1 opposed to 8% in Q) and insects to a lesser extent (30% of

gap-species being insects in Q1 opposed to 40% in Q).

The variant scenario Q2 where all species had preferences supplemented with zone 4 Untouched,

assigned 69 forests to zone 4 Untouched (26,119 ha) opposed to the 50 forests and 15,281 ha in

scenario Q (Table 7). It had costs of 48.1% instead of the 46.1% for scenario Q - the relatively

small difference being due to smaller areas assigned to zones 2 (9,4%) and 3 (19,4%) compared to

scenario Q.

The variant scenario Q3 with protection only possible in zone 4 (simulating no zoning) assigned

203 forests to protection at a cost of 58.9% of annual net income from wood harvest, compared to

46.1% for scenario Q (Table 7). The benefit of zoning thus corresponds to 21.7% in terms of

money potentially saved compared to applying only non-intervention protection. If the lower cost

assumptions in scenario U or Y for zone 2 and 3 protection are possible in reality, savings will be

proportionally higher.

Discussion

In this study we show that, if carefully planned, the government decision of designating 13,300 ha

of new protected forests (10,000 ha untouched and 3,300 ha other protection types) in the Danish

State Forests should be able to provide most threatened species with relevant protection in all

known sites for rare species (less than five occurrences in state forests), and in at least 5 sites for

less rare species (Scenario Q, U and Y). Depending on cost assumptions, this implies a 38-46%

loss of annual net income from wood harvest in the State Forests.

Chapter 2

Combining results into an evaluation of the most cost-efficient constellation of forests to zones is

not straightforward but was done separately for 3 and 5 representation targets and focusing on

money-based scenarios with forests already protected as untouched locked as such. For five

representations the highest target achievement was thus in scenario Q, U and Y with identical

zone constellation, thereby being robust to the uncertainty of cost assumptions. Scenario Y had

the lowest costs thereby being most cost-efficient. Since scenario Q had intermediate cost

assumptions and matched well with the government decision for 10,000 ha of new untouched

forest, it was taken as the most relevant scenario to use for further work and evaluation.

For three representations the highest money-based target achievement was in scenario O which at

the same time had the fourth lowest costs of 26 scenarios thereby being cost-efficient. Scenarios

V and Z had almost the same zone constellation and high target achievement with lower costs,

thereby also being efficient if the lower cost assumptions are possible in reality. These scenarios

had lower protection level than the government decision and were therefore less relevant in

relation to the process of designating new untouched forest reserves.

How much protection is needed for long-term persistence of species is a difficult question (Tear et

al. 2005). In the absence of population viability analyses the choice in this study was to compare

two levels of representation (3 or 5 representations of each species) to be able to evaluate

differences. Scenarios with 3 and 5 representation targets differed only slightly in the number of

species with 3 representations, but the latter had many more species covered 5 times, improving

chances of persistence. The target level of 5 representations can thus be superior in achieving the

UN and EU biodiversity objective of halting the decline in biodiversity, but has c.30% higher

costs.

Even for declining threatened species there will often be populations which live in the

"unprotected" matrix in State Forests or on private land. These populations may complement the

populations in protected areas thus enhancing persistence of the species (Brunet et al. 2010;

Lindenmayer & Franklin 2002; Wilson et al. 2010) as long as they are not just population sinks

(Pulliam 1988). Chances of persistence can thus be improved by enhancing habitat quality in the

managed matrix or by targeting conservation effort on private lands. All Danish State Forests are

subject to a policy to raise amounts of coarse deadwood and veteran trees (including retention

trees) and enhance natural hydrology (Rigsrevisionen 2016) and are also certified by both the

Forest Stewardship Council (FSC) and the Programme for the Endorsement of Forest

Certification (PEFC). This is a substantial improvement to previous management regimes, and

will undoubtedly improve habitat quality in the matrix, most likely enhancing survival and

dispersal of threatened species between protected areas.

Chapter 2

The refinement of "protection" to different categories or "zones" targeting species groups with

different habitat requirements, and with differing economic costs was a primary reason for the

choice of Marxan With Zones. Zoning was shown to be potentially more than 20% cheaper than

simple full protection. But the attribution of zone preferences to species is demanding on input

data regarding species ecological requirements, which are not always well understood or even

erroneous in the data sources used.

As an example the habitat quality for species of zone 4 Untouched can be strongly influenced by

grazing regime. With grazing at moderate (supposedly natural) levels in otherwise untouched

woodland, many species of zone 3 Active will also often find their requirements satisfied.

Missing, too high or too low grazing pressure can on the other hand be detrimental to the survival

of many threatened species.

Similarly the definitions of zone 2 Conifer and zone 3 Active encompass a range of different

protection and management types and intensities which are not directly linkable to the

information on biotopes, threats and preferences for each species. Expert consultation showed a

number of uncertainties and knowledge gaps regarding species preferences and need for

protection regime, and results should thus be interpreted with care.

Scenarios excluding conifer-associated species were more expensive than scenarios including

them because some forests were assigned to zone 3 or 4 instead of the less costly zone 2. The

lower cost assumption for zone 2 was chosen because many of the threatened zone 2 species, e.g.

the globally threatened ant

Formicoxenus nitidulus,

can have their requirements satisfied with

continuation of more or less normal economic-based conifer forestry with only minor

adjustments. The main reason for assessing the effect of excluding species of zone 2 (Conifer),

was that Danish conifer woods are sometimes seen as low priority for conservation (Feilberg

2004; Petersen et al. 2016b), even though other studies have shown them to be of larger

conservation importance than anticipated (Flensted et al. 2016).

The mentioned globally threatened ant – being small and unobtrusive – is only known and

recognized by a few specialist entomologists. False absence (omission error) is an issue for this

and many other species. False presence and other commission errors have been minimized as far

as possible by careful quality checks of all data both by data suppliers and in this project. To

minimize the effect of omission errors the study period was chosen 25 years long in order to

include systematic national surveys of butterflies and flora started around 1991 and all later

systematic "atlas-type" surveys. If nobody has recorded a threatened species from a site for 25

years, chances are good that the site is not important for that species.

Insufficient understanding of species-preferences might also lead to suboptimal results. Such error

was minimized by using the best available systematic database- and literature-sources and finally

Chapter 2

adjusting by expert consultation. But even so there seems to be examples of species with broader

or other preferences than the ones used as input, e.g. the fungi

Coprinus pannucioides

and

Leucocoprinus brebissonii,

assigned to untouched zone 4, were recorded in a young afforestation

but not in any already untouched woodlands.

Because the real cost level of zone 2 and 3 is uncertain and variable, it was fortunate that results

were not influenced to any great extent by change of cost assumptions. Zone-constellations

remained constant over a wide range of costs. More detailed zone 2 and 3 planning will be

necessary forest by forest for optimal protection and for more precise assessment of costs.

It was surprising that locking seven forests already fully protected to zone 4 did not result in a

lowered target achievement (scenario Q compared to M) because these forests had been protected

in the 1990'ies or earlier with no use of systematic conservation planning. On the contrary,

scenario Q turned out to have 0.8% higher average target achievement and full achievement for

231 species instead of 229. This came at a 1.1% higher cost showing that the Marxan algorithms

do not simply maximize target achievement but find a balance between target achievement and

cost minimization.

It must be noted that due to the lack of solid presence-absence data the analyses were based on

presence data only. Much of the presence data was based on citizen science data which is biased

(Geldmann et al. 2016). This makes the resulting zone-constellations sub-optimal compared to a

theoretically ideal situation.

The variant scenario Q1 used to assess sensitivity to some of the abovementioned biases showed

1) that the amount of species included has an important impact; 2) many threatened species can be

missed if designations are based on only well-known and well-mapped threatened species, and 3)

it makes a difference which taxon groups are included. This can be seen as a sign of

complementarity in habitat requirements between e.g. the threatened lichens and insects, but also

between well-known and less well-known species.

The variant scenarios Q2 and Q3 showed that zoning is efficient in lowering costs of protection.

Zoning should at the same time enhance efficiency of species persistence assuming their

requirements are sufficiently well-known and can be coupled with a relevant protection regime.

The zoning approach of Marxan With Zones is thus superior to conservation planning discerning

only between protected and non-protected areas as demonstrated by Watts et al. (2009).

Even the scenarios with highest target achievement missed out on dozens of priority A species.

These gap species occurred in forests selected for a non-compatible zone due to optimization for

co-occurring species with other ecological requirements. To reach a higher overall target

achievement it will be necessary to apply a varied protection scheme with more than one zone in

some of the forests having species with differing habitat requirements, e.g. forests with species

Chapter 2

needing semi-open habitats in one part and species needing closed forest conditions with

deadwood in another part. Combination of zone 4 Untouched with a grazing regime mimicking

primeval conditions should also be suitable for many species of zone 3 (and zone 2 if conifers are

present) and theoretically be optimal even though not always possible to implement in reality.

After the Marxan analyses further work on the planned woodland reserves thus looked into

optimization by diversifying protection regime in some forests. Detailed forest-by-forest GIS

analysis of which parts of the forests harbor which species showed how much could be won by

zoning internally. The Marxan results were very helpful in this process, by identifying the 48

(5%) forests with gap-species assigned to a non-compatible zone and thereby relevant for a more

detailed look.

Area-based data on forest- and habitat-structures assessed as important for threatened species

comprised another independent input to the State Forest designation process of new reserves. The

Marxan results from the present study were combined with such other data, legal restrictions and

other ecosystem services than biodiversity, e.g. recreational interests, as input to the government

decision January 2018 (Naturstyrelsen 2018) identifying the new forest reserves and announcing

them for public hearing. In the process between systematic conservation planning and final

selection the sites usually shift somewhat (Schröter et al. 2014) which was also the case here but

to a minor extent.

Conclusion

Marxan with Zones was used to develop examples of cost-efficient site networks which could

represent known threatened forest species at least 3 or 5 times with a protection scheme matching

their ecological requirements. Marxan with Zones proved to be effective in optimizing the site

selection and target achievement for sites in the State Forests. The 976 sites and 1,066 threatened

species were too many to make it realistic to optimize site selection in other ways.

The cost of example networks amounted to 26-69% of State Forest net income from forestry if

whole forests were designated, depending on cost assumptions and representation target. The

costs could be reduced and target achievement enhanced if large forests were only partially

designated (covering mainly the relevant habitats of the occurring focal species) and the rest

continued with normal operations.

The government decision of 10,000 hectares of new minimum-intervention reserves matched well

with the Marxan outputs for some scenarios with target set at 5 representations (scenario Q, U and

Y) because 4,932 of the 15,271 hectares these scenarios assigned to untouched was already

protected in the 1990'ies. On the other hand the analyses highlight the need of many forest species

for other types of protection besides minimum-intervention reserves. The Marxan examples

Chapter 2

include larger actively protected areas than currently decided. More active protection thus seems

necessary for efficient protection of such species besides the decided expansion of minimum-

intervention.

Acknowledgements

This study would not have been possible without the immense data collection efforts of numerous

persons active in Birdlife Denmark, Lepidopterological Society, Danish Mycological Society,

Danish Botanical Society, Entomological Society of Fünen, Spiders of Denmark (online

database), the Natural History Museum of Denmark, the Danish Nature Agency and Birds &

Nature web-database (Fugle & Natur license B05/2014). All persons and organizations involved

deserve sincere thanks for their efforts and for permitting use of data. Species experts J. Pedersen

(Coleoptera), N. Scharff (Arachnida) and U. Søchting (Lichens) at the Natural History Museum

of Denmark are thanked for help and expert consultations. C. Rahbek, B. Hermansen, A. H.

Petersen, L. A. Hansen and L. N. B. Jensen at the Centre for Macroecology, Evolution and

Climate (CMEC), University of Copenhagen, are thanked for help and advice during the project.

Thanks also to H. Possingham and M. Watts for providing the Marxan with zones software.

Erik Buchwald and Jacob Heilmann-Clausen acknowledge the Danish National Research

Foundation for funding for the Center for Macroecology, Evolution and Climate (grant-number

DNRF96). The Danish Nature Agency and Innovation Fund Denmark (industrial Ph.D. grant no.

4135-00145B) are thanked for economic support to Erik Buchwald.

Appendix A. Supplementary material

S1 Table. Data sources and records.

S2 Table. Species data.

Details of status, preferences and zones for the 626 forest species.

S3 Table. A priori fully protected forests.

The seven forests locked to zone 4 in scenario O-Z.

S4 Table. Zone 4 forests.

Details of 20 forests which all scenarios put in zone 4 (Untouched

protection).

S5 Table. Zone 3 forests.

Details of 55 forests which all scenarios put in zone 3 (Active

protection).

S6 Table. Variable forests.

Details of 220 forests variably selected to different zones by

scenarios.

S7 Table. Zone 1 forests.

Details of 681 forests which were always assigned to zone 1 (Normal).

S8 Table. Results of scenarios.

Details of 26 scenarios and 3 variants with varying assumptions.

S9 Table. Gap species of scenario Q.

Details of 73 species not reaching target protection in

scenario Q.

Chapter 2

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Chapter 2

S1 Table

S1 Table. Data sources and records.

Used

records include only study species and the years 1991-2015.

Records

Data provider

Atlas- & observation-database (Birdlife

Denmark, DOFbasen)

Birds & Nature web-database (license

B05/2014, Fugle & Natur)

Bugbase (Lepidopterological Society,

Denmark)

Biodiversity map, HNV-forest update

(Danish Nature Agency)

Biodiversity map, 2014 (Danish Nature

Agency)

Danish fungal records database (Danish

Mycological Society)

An Annotated Atlas of the Danish

Butterflies (Zoological Museum)

"Forests of special nature value § 25"

(Danish Nature Agency)

Danish Nature Agency database on

things to protect, "Pas-paa-kort"

Atlas Flora Danica data 1992-2013,

redlist & forest extract (Danish

Botanical Society)

Entomological Society of Fünen

database (Fynske insekter)

Spiders of Denmark online database

Epiphytic lichens and bryophytes in the

forests of Lille Vildmose in 2013

Roof-top insect study 1992-2009

(Zoological Museum)

Digital databases of invertebrates

(Natural History Museum of Denmark)

Epiphytic lichens in the forest of Kås in

2002

Epiphytic lichens and bryophytes on oak

in the forest of Tofte 2012-2013

Status books on the forests of Tofte &

Høstemark

Danish Topographical Botanical Survey

online, "TBU collections"

Hepatics at the Herbarium C. (Natural

History Museum of Denmark)

Total

17.961.625

1.958.677

859.969

54.997

23.185

559.159

199.307

49.894

8.526

123.334

Used

110.029

57.401

23.689

22.656

20.919

20.358

5.288

2.399

1.865

1.459

Date

accessed

17/12 2015

2/12 2015

21/12 2015

2/5 2016

9/12 2015

17/11 2015

15/1 2014

1/12 2015

3/11 2015

22/4 2014

Reference

http://dofbasen.dk/

http://www.fugleognatur.dk

http://www.bugbase.dk/

Johannsen, V. K., 2015 &

digital data 2/5 2016

Ejrnæs et al., 2014 & digital

update accessed 9/12 2015

http://www.svampeatlas.dk

Stoltze, M., 1994

Naturstyrelsen 2007

Naturstyrelsen 2015

Hartvig, P., 2015

167.905

31.731

210

44.088

69.436

104

3.331

3.299

9.306

22.128.168

971

158

267.438

20/1 2016

3/11 2015

19/4 2016

14/1 2016

1/12 2015

19/4 2016

21/12 2015

3/11 2015

Total

http://www.fynskeinsekter.

dk/

http://www.danmarks-

edderkopper.dk/

Fritz, Ô., 2014

Thomsen et al., 2015

Stein, M., 2015

Larsen, R.S., 2002

Mouridsen, M.T., 2014

Hald-Mortensen, P., 2012 &

2002

http://www.daim.snm.ku.dk

/TBU-en

http://www.daim.snm.ku.dk

/hepatics

Chapter 2

S1 Table References:

Ejrnæs et al., 2014.

Biodiversitetskort for Danmark.

Rapp 112/2014, DCE.

Fritz, Ô. 2014.

Epiphytic lichens and bryophytes in the forests of Lille Vildmose in 2013.

Naturcentrum report 2014-03-04, 34 pp.

GBIF 2016. GBIF.org (5th September 2016)

GBIF Occurrence Download

http://doi.org/10.15468/dl.obr3f0

Hald-Mortensen, P. 2002.

Høstemark - status 2001.

AVJF, 302 pp.

Hald-Mortensen, P. 2012.

Tofte Skov og Mose - status 2012.

AVJF, 387 pp.

Hartvig, P., 2015.

Atlas Flora Danica.

3 volumes. Gyldendal, København.

Johannsen, V. K., 2015.

Udvikling af et High Nature Value - HNV-skovkort for Danmark.

IGN

rapport, nov 2015.

Larsen, R.S., 2002.

Biodiversitet, kvistflora og kvælstofbelastning. Kås Skov, 2002.

KU speciale

rapport.

Mouridsen, M.T., 2014.

Epifytiske laver og mosser på eg i Tofte Skov. Status 2012-13.

Specialerapport, KU Biol. Inst. 2014.

Naturstyrelsen, 2007.

Data from 2007 COWI project "Forests of special nature value § 25".

Unpublished.

Naturstyrelsen, 2015.

Database on things to protect, "Pas-paa-kort", Naturreg.

Unpublished.

Naturstyrelsen, 2016.

Digital biodiversity map 2016,

updatet ArcGis files March 18th 2016.

Unpublished.

Stein. M., in litt. 2015.

RE: Forespørgsel på invertebratdata til mit PhD.

Email 1/12 2015 from

SNHM.

Stoltze, M., 1994.

An Annotated Atlas of the Danish Butterflies.

Zoological Museum, 134 pp.

Thomsen, P. F., Jørgensen, P. S., Bruun, H. H., Pedersen, J., Riis-Nielsen, T., Jonko, K., Słowińska, I.,

Rahbek, C., Karsholt, O., 2015.

Resource specialists lead local insect community turnover associated

with temperature – analysis of an 18-year full-seasonal record of moths and beetles.

Journal of

Animal Ecology. doi: 10.1111/1365-2656.12452.

Chapter 2

Data for the 626 forest species

Taxon

Mammalia

Aves

Amphibia

Coleoptera

Species name

Barbastella barbastellus

Canis lupus

Eptesicus nilssonii

Eptesicus serotinus

Lepus europaeus

Martes martes

Muscardinus avellanarius

Myotis bechsteinii

Myotis brandtii

Myotis dasycneme

Myotis daubentonii

Myotis mystacinus

Myotis nattereri

Nyctalus noctula

Pipistrellus nathusii

Pipistrellus pipistrellus

Pipistrellus pygmaeus

Plecotus auritus

Aegolius funereus

Bubo bubo

Caprimulgus europaeus

Dryocopus martius

Falco subbuteo

Ficedula parva

Grus grus

Haliaeetus albicilla

Jynx torquilla

Lanius collurio

Lullula arborea

Mergus merganser

Nucifraga caryocatactes

Oriolus oriolus

Pernis apivorus

Serinus serinus

Tringa ochropus

Hyla arborea

Rana arvalis

Rana dalmatina

Rana temporaria

Triturus cristatus

Allandrus undulatus

Allecula morio

Ampedus hjorti

Ampedus quercicola

Ampedus sanguineus

Carabus glabratus

Carabus intricatus

Colydium elongatum

Crepidophorus mutilatus

Cymindis vaporariorum

Dictyoptera aurora

Gnorimus nobilis

Hallomenus axillaris

Hypulus quercinus

Ischnodes sanguinicollis

Judolia sexmaculata

Labidostomis longimana

Lasiorhynchites cavifrons

Leptura aethiops

Lucanus cervus

Lygistopterus sanguineus

Lymexylon navale

Magdalis armigera

Malachius aeneus

Melasis buprestoides

Mycetophagus fulvicollis

Neomida haemorrhoidalis

Oberea linearis

Omaloplia nigromarginata

Osmoderma eremita

Peltis ferruginea

Pissodes validirostris

Plagionotus detritus

Pogonocherus decoratus

Pyrrhidium sanguineum

Pytho depressus

Rhynchaenus rufus

Scolytus scolytus

Smaragdina salicina

Stictoleptura scutellata

Threat

geography

Denmark

Globally

Denmark

Page 1 of 8

S2 Table

Saproxylic?

Redlist (Decline? Priority Main affinity

VU no

Deciduous

Non‐saproxylic

‐

Deciduous

Non‐saproxylic

NA no

Deciduous

Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

NT no

Conifer facultative Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

DD yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

NA no

Conifer obligate Non‐saproxylic

NT no

Deciduous

Non‐saproxylic

LC no

Conifer obligate Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

NA no

Deciduous

Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

NT no

Conifer facultative Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

RE RE

Conifer obligate Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

VU no

Conifer facultative Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

LC yes

Deciduous

Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

LC no

Deciduous

Non‐saproxylic

LC yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

VU no

Conifer obligate Saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

VU yes

Conifer facultative Non‐saproxylic

VU yes

Deciduous

Saproxylic

CR yes

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

EN yes

Conifer obligate Saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Saproxylic

RE RE

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

EN no

Deciduous

Non‐saproxylic

EN yes

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Conifer obligate Saproxylic

RE RE

Deciduous

Saproxylic

EN no

Conifer obligate Saproxylic

VU no

Deciduous

Saproxylic

VU yes

Conifer obligate Saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

Group

Zone Forests (N) Match gap_Q?

Open woodland

3 gap1

Other decidous

Open woodland

321

Conifer facultative 23

Open woodland

Open woodland 34

Open woodland

Conifer obligate

Open woodland

Conifer obligate

Other decidous

113

Open woodland

Other decidous

Open woodland

Other decidous

Open woodland

201

Conifer facultative 23

105

Open woodland

Conifer obligate

Other decidous

Conifer facultative 23

Other decidous

Open woodland

179

Open woodland

187

Open woodland

142

Woodland saprox 4

Open saproxylic

0 gap1

Open saproxylic

3 gap2

Open saproxylic

Conifer saproxylic 24

Other decidous

Woodland saprox 4

Open saproxylic

3 gap2

Conifer facultative 23

Woodland saprox 4

Open saproxylic

0 gap1

Woodland saprox 4

Open saproxylic

0 gap1

Conifer saproxylic 24

Open woodland

Open saproxylic

0 gap2

Woodland saprox 4

Open saproxylic 34

Open saproxylic

Woodland saprox 4

Open saproxylic

Open woodland

Open saproxylic

Open woodland

Conifer saproxylic 2

Woodland saprox 4

Conifer saproxylic 24

Open saproxylic 34

Conifer saproxylic 24

Open woodland

Open saproxylic 34

Open woodland

4 gap1

100

Data for the 626 forest species

Threat

geography

Taxon

Species name

Coleoptera

Tachyta nana

Denmark

Coleoptera

Tetrops starkii

Denmark

Coleoptera

Thymalus limbatus

Denmark

Coleoptera

Xylophilus corticalis

Denmark

Diptera

Arctophila bombiformis

Denmark

Diptera

Arctophila superbiens

Denmark

Diptera

Brachyopa bicolor

Denmark

Diptera

Brachyopa panzeri

Denmark

Diptera

Brachyopa scutellaris

Denmark

Diptera

Brachypalpus laphriformis Denmark

Diptera

Caliprobola speciosa

Denmark

Diptera

Chalcosyrphus piger

Denmark

Diptera

Chalcosyrphus valgus

Denmark

Diptera

Chamaesyrphus lusitanicus Denmark

Diptera

Cheilosia flavipes

Denmark

Diptera

Cheilosia frontalis

Denmark

Diptera

Cheilosia illustrata

Denmark

Diptera

Chrysotoxum verralli

Denmark

Diptera

Criorhina floccosa

Denmark

Diptera

Cyrtopogon lateralis

Denmark

Diptera

Epistrophe grossulariae

Denmark

Diptera

Epistrophe melanostoma

Denmark

Diptera

Eriozona syrphoides

Denmark

Diptera

Eumerus ornatus

Denmark

Diptera

Eumerus sogdianus

Denmark

Diptera

Laphria ephippium

Denmark

Diptera

Mallota cimbiciformis

Denmark

Diptera

Melangyna lucifera

Denmark

Diptera

Microdon analis

Denmark

Diptera

Myolepta dubia

Denmark

Diptera

Pocota personata

Denmark

Diptera

Temnostoma meridionale

Denmark

Diptera

Trichopsomyia joratensis

Denmark

Diptera

Volucella inanis

Denmark

Diptera

Xanthogramma festivum

Denmark

Diptera

Xylota abiens

Denmark

Diptera

Xylota meigeniana

Denmark

Heteroptera Aradus betulae

Denmark

Heteroptera Eurydema dominulus

Denmark

Heteroptera Stagonomus bipunctatus

Denmark

Hymenoptera Bombus veteranus

Denmark

Hymenoptera Formicoxenus nitidulus

Globally

Lepidoptera Acronicta cinerea

Denmark

Lepidoptera Acronicta tridens

Denmark

Lepidoptera Agrochola nitida

Denmark

Lepidoptera Argynnis adippe

Denmark

Lepidoptera Argynnis paphia

Denmark

Lepidoptera Boloria euphrosyne

Denmark

Lepidoptera Brachionycha nubeculosa

Denmark

Lepidoptera Calliteara abietis

Denmark

Lepidoptera Coenonympha arcania

Denmark

Lepidoptera Cosmia affinis

Denmark

Lepidoptera Diarsia dahlii

Denmark

Lepidoptera Drymonia obliterata

Denmark

Lepidoptera Eilema griseola

Denmark

Lepidoptera Eilema pygmaeola

Denmark

Lepidoptera Endromis versicolora

Denmark

Lepidoptera Eriogaster lanestris

Denmark

Lepidoptera Herminia tarsicrinalis

Denmark

Lepidoptera Lacanobia splendens

Denmark

Lepidoptera Lasionhada proxima

Denmark

Lepidoptera Leptidea juvernica

Denmark

Lepidoptera Leptidea sinapis

Denmark

Lepidoptera Malacosoma neustria

Denmark

Lepidoptera Melitaea athalia

Denmark

Lepidoptera Notodonta torva

Denmark

Lepidoptera Nudaria mundana

Denmark

Lepidoptera Nymphalis polychloros

Denmark

Lepidoptera Pabulatrix pabulatricula

Denmark

Lepidoptera Pachetra sagittigera

Denmark

Lepidoptera Panemeria tenebrata

Denmark

Lepidoptera Parasemia plantaginis

Denmark

Lepidoptera Proserpinus proserpina

Lepidoptera Satyrium w‐album

Denmark

Lepidoptera Shargacucullia scrophulariae Denmark

Lepidoptera Thecla betulae

Denmark

Lepidoptera Trichiura crataegi

Denmark

Lepidoptera Xestia ditrapezium

Denmark

Lepidoptera Zygaena viciae

Denmark

Odonata

Leucorrhinia pectoralis

Chapter 2

S2 Table

Saproxylic?

Redlist (Decline? Priority Main affinity

VU yes

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

VU yes

Conifer obligate Saproxylic

EN yes

Deciduous

Saproxylic

VU yes

Conifer facultative Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Saproxylic

CR yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Conifer obligate Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN yes

Conifer obligate Non‐saproxylic

EN yes

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Conifer facultative Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

CR yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Conifer obligate Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

RE RE

Deciduous

Non‐saproxylic

EN yes

Conifer obligate Non‐saproxylic

RE RE

Deciduous

Non‐saproxylic

RE RE

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Conifer facultative Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

RE RE

Conifer facultative Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

RE RE

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

CR yes

Conifer facultative Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

NA no

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

RE RE

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

Page 2 of 8

Group

Zone Forests (N) Match gap_Q?

Open saproxylic

0 gap1

Open saproxylic 34

Woodland saprox 4

Open woodland

Woodland saprox 4

Open saproxylic 34

Woodland saprox 4

1 gap1

Conifer saproxylic 24

Woodland saprox 4

2 gap2

Conifer facultative 23

Open woodland

0 gap1

Open woodland

Woodland saprox 4

4 gap1

Open woodland

1 gap1

Open woodland

2 gap2

Open woodland

Conifer obligate

Open woodland

1 gap1

Open woodland

Conifer obligate

1 gap3

Open saproxylic 34

Open woodland

Open saproxylic 34

4 gap1

Woodland saprox 4

Other decidous

Conifer facultative 23

Open woodland

3 gap2

Woodland saprox 4

2 gap1

Open saproxylic 34

Open woodland

Conifer obligate

3 gap2

Open woodland

Open woodland 34

Open woodland

132

Open woodland

4 gap1

Open woodland

0 gap1

Conifer obligate

0 gap3

Open woodland

3 gap1

Open woodland

Untouched decido 34

Other decidous

Conifer facultative 23

Other decidous

Open woodland

3 gap1

Other decidous

Conifer facultative 23

Open woodland

Conifer facultative 23

3 gap1

Open woodland

3 gap2

Other decidous

Open woodland

0 gap1

Open woodland

Chapter 2

Data for the 626 forest species

Threat

geography

Taxon

Species name

Orthoptera

Chorthippus dorsatus

Denmark

Araneae

Araneus alsine

Denmark

Araneae

Araneus angulatus

Denmark

Araneae

Araneus marmoreus

Denmark

Araneae

Araneus triguttatus

Denmark

Araneae

Dendryphantes rudis

Denmark

Araneae

Hygrolycosa rubrofasciata

Denmark

Araneae

Midia midas

Denmark

Gastropoda

Helix pomatia

Gastropoda

Vertigo angustior

Gastropoda

Vertigo moulinsiana

Globally

PseudoscorpionAnthrenochernes stellae

Ascomycota Elaphomyces anthracinus

Denmark

Ascomycota Erythricium aurantiacum

Denmark

Ascomycota Gyromitra gigas

Denmark

Ascomycota Helvella albella

Denmark

Ascomycota Helvella costifera

Denmark

Ascomycota Helvella queletii

Denmark

Ascomycota Nemania carbonacea

Denmark

Ascomycota Nemania diffusa

Denmark

Basidiomycota Agaricus moelleri

Denmark

Basidiomycota Agaricus phaeolepidotus

Denmark

Basidiomycota Agaricus porphyrizon

Denmark

Basidiomycota Agrocybe firma

Denmark

Basidiomycota Agrocybe vervacti

Denmark

Basidiomycota Albatrellus pes‐caprae

Denmark

Basidiomycota Aleurodiscus disciformis

Denmark

Basidiomycota Amanita lividopallescens

Denmark

Basidiomycota Amanita olivaceogrisea

Denmark

Basidiomycota Amanita solitaria

Denmark

Basidiomycota Amanita strobiliformis

Denmark

Basidiomycota Amaurodon cyaneus

Denmark

Basidiomycota Anomoporia myceliosa

Denmark

Basidiomycota Antrodia heteromorpha

Denmark

Basidiomycota Antrodia malicola

Denmark

Basidiomycota Arrhenia epichysium

Denmark

Basidiomycota Athelidium aurantiacum

Denmark

Basidiomycota Aurantiporus alborubescens Denmark

Basidiomycota Aureoboletus gentilis

Denmark

Basidiomycota Bankera fuligineoalba

Denmark

Basidiomycota Bankera violascens

Denmark

Basidiomycota Boletopsis leucomelaena

Denmark

Basidiomycota Boletus aereus

Denmark

Basidiomycota Boletus fechtneri

Denmark

Basidiomycota Boletus impolitus

Denmark

Basidiomycota Boletus legaliae

Denmark

Basidiomycota Boletus satanas

Denmark

Basidiomycota Buchwaldoboletus lignicola Denmark

Basidiomycota Buglossoporus quercinus

Denmark

Basidiomycota Calocera glossoides

Denmark

Basidiomycota Camarophyllopsis atropuncta Denmark

Basidiomycota Camarophyllopsis hymenocepDenmark

Basidiomycota Camarophyllopsis micacea Denmark

Basidiomycota Ceriporia purpurea

Denmark

Basidiomycota Ceriporiopsis gilvescens

Denmark

Basidiomycota Ceriporiopsis pannocincta

Denmark

Basidiomycota Cerrena unicolor

Denmark

Basidiomycota Chamaemyces fracidus

Denmark

Basidiomycota Cheimonophyllum candidissim

Denmark

Basidiomycota Chromocyphella muscicola Denmark

Basidiomycota Clavaria asperulispora

Denmark

Basidiomycota Clavaria flavipes

Denmark

Basidiomycota Clavaria tenuipes

Denmark

Basidiomycota Clavicorona taxophila

Denmark

Basidiomycota Clavulinopsis cinereoides

Denmark

Basidiomycota Clavulinopsis microspora

Denmark

Basidiomycota Climacocystis borealis

Denmark

Basidiomycota Clitocybe alexandri

Denmark

Basidiomycota Coltricia confluens

Denmark

Basidiomycota Conocybe dumetorum

Denmark

Basidiomycota Coprinopsis insignis

Denmark

Basidiomycota Coprinopsis pannucioides

Denmark

Basidiomycota Coprinopsis spelaiophila

Denmark

Basidiomycota Corticium expallens

Denmark

Basidiomycota Cortinarius acetosus

Denmark

Basidiomycota Cortinarius arcuatorum

Denmark

Basidiomycota Cortinarius aureocalceolatus Denmark

Basidiomycota Cortinarius betulinus

Denmark

Basidiomycota Cortinarius bulliardii

Denmark

Basidiomycota Cortinarius caesiocortinatus Denmark

101

Page 3 of 8

S2 Table

Saproxylic?

Redlist (Decline? Priority Main affinity

CR yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

EN yes

Conifer obligate Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Conifer obligate Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

‐

Deciduous

Non‐saproxylic

‐

yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

‐

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Conifer obligate Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Saproxylic

EN no

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

EN no

Conifer facultative Saproxylic

EN no

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

EN yes

Conifer obligate Non‐saproxylic

CR yes

Conifer obligate Non‐saproxylic

CR no

Conifer obligate Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

CR no

Conifer obligate Saproxylic

EN yes

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

EN yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

RE RE

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

EN yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN yes

Conifer obligate Saproxylic

EN yes

Conifer obligate Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

Group

Zone Forests (N) Match gap_Q?

Open woodland

1 gap3

Open woodland

1 gap1

Conifer obligate

Open woodland

Conifer obligate

Open woodland

0 gap1

Other decidous

101

Other decidous

Open saproxylic 34

Other decidous

Conifer obligate

1 gap2

Other decidous

Open woodland 34

Other decidous

Woodland saprox 4

Untouched decido 4

Open woodland 34

Woodland saprox 4

Open woodland

Other decidous

Woodland saprox 34

Open woodland

Open woodland 34

Other decidous

Open woodland

Woodland saprox 34

Conifer saproxylic 24

Woodland saprox 4

Other decidous

Conifer obligate

Other decidous

Open woodland

Other decidous

Open woodland

Conifer saproxylic 24

Woodland saprox 34

Woodland saprox 4

Open woodland

0 gap1

Open woodland

Woodland saprox 4

Other decidous

Woodland saprox 4

Open woodland 34

Open woodland

4 gap1

Open woodland

Open saproxylic 34

Open woodland

Conifer saproxylic 24

Conifer obligate

Other decidous

Woodland saprox 4

Open saproxylic 34

Woodland saprox 4

Open saproxylic 34

Other decidous

Untouched decido 34

Other decidous

Untouched decido 34

102

Data for the 626 forest species

Taxon

Basidiomycota

Threat

geography

Species name

Cortinarius caesiolatens

Denmark

Cortinarius caesiostramineus Denmark

Cortinarius cagei

Denmark

Cortinarius camphoratus

Denmark

Cortinarius caperatus

Denmark

Cortinarius catharinae

Denmark

Cortinarius cinnabarinus

Denmark

Cortinarius cisticola

Denmark

Cortinarius colus

Denmark

Cortinarius cotoneus

Denmark

Cortinarius elegantissimus Denmark

Cortinarius flavovirens

Denmark

Cortinarius fragrantior

Denmark

Cortinarius fulvocitrinus

Denmark

Cortinarius gracilior

Denmark

Cortinarius imperialis

Denmark

Cortinarius insignibulbus

Denmark

Cortinarius langeorum

Denmark

Cortinarius lilacinovelatus

Denmark

Cortinarius luteoimmarginatuDenmark

Cortinarius maculosus

Denmark

Cortinarius magicus

Denmark

Cortinarius multiformium

Denmark

Cortinarius nanceiensis

Denmark

Cortinarius olearioides

Denmark

Cortinarius orellanus

Denmark

Cortinarius osmophorus

Denmark

Cortinarius porphyropus

Denmark

Cortinarius quarciticus

Denmark

Cortinarius rufo‐olivaceus

Denmark

Cortinarius saporatus

Denmark

Cortinarius selandicus

Denmark

Cortinarius serratissimus

Denmark

Cortinarius sodagnitus

Denmark

Cortinarius splendens

Denmark

Cortinarius talus

Denmark

Cortinarius tophaceus

Denmark

Cortinarius traganus

Denmark

Cortinarius urbicus

Denmark

Cortinarius venustus

Denmark

Cortinarius violaceocinereus Denmark

Cortinarius xanthochlorus

Denmark

Cortinarius xantho‐ochraceus Denmark

Craterellus cinereus

Denmark

Craterellus melanoxeros

Denmark

Crepidotus cinnabarinus

Denmark

Cristinia gallica

Denmark

Cystolepiota hetieri

Denmark

Cystolepiota icterina

Denmark

Cystolepiota moelleri

Denmark

Dacrymyces enatus

Denmark

Dendrocollybia racemosa

Denmark

Dendrothele commixta

Denmark

Dentipellis fragilis

Denmark

Dichomitus campestris

Denmark

Echinoderma calcicola

Denmark

Echinoderma hystrix

Denmark

Echinoderma perplexum

Denmark

Echinoderma pseudoasperuluDenmark

Entoloma aprile

Denmark

Entoloma clandestinum

Denmark

Entoloma depluens

Denmark

Entoloma dichroum

Denmark

Entoloma formosum

Denmark

Entoloma lampropus

Denmark

Entoloma lepidissimum

Denmark

Entoloma parkensis

Denmark

Entoloma placidum

Denmark

Entoloma plebejum

Denmark

Entoloma porphyrogriseum Denmark

Entoloma queletii

Denmark

Entoloma scabrosum

Denmark

Entoloma sinuatum

Denmark

Entoloma strigosissimum

Denmark

Entoloma transvenosum

Denmark

Exidia cartilaginea

Denmark

Faerberia carbonaria

Denmark

Femsjonia peziziformis

Denmark

Flammulaster limulatus

Denmark

Flammulaster muricata

Denmark

Chapter 2

S2 Table

Saproxylic?

Redlist (Decline? Priority Main affinity

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Conifer obligate Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

RE RE

Conifer obligate Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Conifer facultative Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Conifer obligate Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN yes

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

VU yes

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

RE RE

Deciduous

Saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Conifer facultative Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

EN no

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

Page 4 of 8

Group

Zone Forests (N) Match gap_Q?

Untouched decido 34

Other decidous

Conifer obligate

Other decidous

Untouched decido 34

Other decidous

Untouched decido 34

Conifer obligate 23

Untouched decido 34

Other decidous

Untouched decido 34

Other decidous

Untouched decido 34

Other decidous

Conifer facultative 2

Untouched decido 34

Other decidous

Untouched decido 34

Other decidous

Conifer obligate

Other decidous

Untouched decido 34

Other decidous

Woodland saprox 4

0 gap1

Woodland saprox 4

Untouched decido 4

Woodland saprox 4

Other decidous

Woodland saprox 34

Woodland saprox 4

Woodland saprox 34

Untouched decido 4

Open woodland

2 gap2

Open woodland

Woodland saprox 4

Other decidous

Open woodland

4 gap1

Conifer facultative 23

2 gap1

Other decidous

Woodland saprox 4

Open woodland 34

Open woodland

Untouched decido 4

0 gap2

Open woodland 34

Other decidous

Open woodland

Woodland saprox 4

3 gap1

Woodland saprox 3

Woodland saprox 4

3 gap1

Woodland saprox 4

Chapter 2

Data for the 626 forest species

Taxon

Basidiomycota

Threat

geography

Species name

Fomitiporia robusta

Denmark

Ganoderma resinaceum

Denmark

Geastrum quadrifidum

Denmark

Gloeocystidiellum clavuligeru Denmark

Gloeohypochnicium analogum

Denmark

Gloeoporus dichrous

Denmark

Gomphus clavatus

Denmark

Gymnopus brassicolens

Denmark

Gymnopus hariolorum

Denmark

Gymnopus impudicus

Denmark

Gymnopus inodorus

Denmark

Hebeloma fusisporum

Denmark

Hemipholiota heteroclita

Denmark

Hericium cirrhatum

Denmark

Hericium erinaceus

Denmark

Hohenbuehelia atrocoerulea Denmark

Hohenbuehelia auriscalpium Denmark

Hohenbuehelia mastrucata Denmark

Hohenbuehelia petaloides Denmark

Hohenbuehelia unguicularis Denmark

Hydnellum aurantiacum

Denmark

Hydnellum auratile

Denmark

Hydnellum caeruleum

Denmark

Hydnellum concrescens

Denmark

Hydnellum ferrugineum

Denmark

Hydnellum gracilipes

Denmark

Hydnellum peckii

Denmark

Hydnellum scrobiculatum

Denmark

Hydnellum spongiosipes

Denmark

Hydnum albidum

Denmark

Hydropus scabripes

Denmark

Hydropus trichoderma

Denmark

Hygrocybe quieta

Denmark

Hygrocybe vitellina

Denmark

Hygrophorus camarophyllus Denmark

Hygrophorus lucorum

Denmark

Hygrophorus mesotephrus Denmark

Hygrophorus nemoreus

Denmark

Hygrophorus persoonii

Denmark

Hyphoderma macedonicum Denmark

Hyphoderma medioburiense Denmark

Hypholoma ericaeum

Denmark

Hypsizygus ulmarius

Denmark

Inocybe calamistrata

Denmark

Inocybe flavella

Denmark

Inocybe huijsmanii

Denmark

Inocybe hystrix

Denmark

Inocybe margaritispora

Denmark

Inocybe quietiodor

Denmark

Inocybe sambucina

Denmark

Inocybe tenebrosa

Denmark

Inonotus dryadeus

Denmark

Inonotus hispidus

Denmark

Ischnoderma resinosum

Denmark

Kavinia himantia

Denmark

Lactarius acerrimus

Denmark

Lactarius albocarneus

Denmark

Lactarius azonites

Denmark

Lactarius decipiens

Denmark

Lactarius evosmus

Denmark

Lactarius hysginus

Denmark

Lactarius lignyotus

Denmark

Lactarius mammosus

Denmark

Lactarius musteus

Denmark

Lactarius porninsis

Denmark

Lactarius repraesentaneus Denmark

Lactarius scrobiculatus

Denmark

Lactarius uvidus

Denmark

Lactarius violascens

Denmark

Lactarius volemus

Denmark

Leccinum crocipodium

Denmark

Lentaria byssiseda

Denmark

Lentaria epichnoa

Denmark

Lentinellus ursinus

Denmark

Lentinus suavissimus

Denmark

Lepiota cingulum

Denmark

Lepiota echinella

Denmark

Lepiota fuscovinacea

Denmark

Lepiota grangei

Denmark

Lepiota griseovirens

Denmark

103

Page 5 of 8

S2 Table

Saproxylic?

Redlist (Decline? Priority Main affinity

EN no

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

EN yes

Conifer facultative Non‐saproxylic

EN no

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

CR yes

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

EN no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

CR yes

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Saproxylic

CR yes

Conifer obligate Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

CR yes

Conifer facultative Non‐saproxylic

VU yes

Conifer facultative Non‐saproxylic

EN yes

Conifer obligate Non‐saproxylic

CR no

Conifer obligate Non‐saproxylic

EN yes

Conifer obligate Non‐saproxylic

RE RE

Conifer obligate Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

CR yes

Conifer obligate Non‐saproxylic

CR no

Conifer obligate Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

EN yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN yes

Conifer obligate Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

EN yes

Deciduous

Non‐saproxylic

EN yes

Conifer obligate Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN yes

Conifer obligate Non‐saproxylic

VU no

Conifer obligate Non‐saproxylic

VU no

Conifer facultative Non‐saproxylic

VU no

Conifer obligate Non‐saproxylic

VU no

Conifer obligate Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN no

Conifer obligate Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

EN yes

Conifer facultative Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

CR no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

Group

Zone Forests (N) Match gap_Q?

Open saproxylic 34

Conifer facultative 23

Woodland saprox 4

2 gap3

Other decidous

Untouched decido 4

Other decidous

Open woodland

Woodland saprox 4

Open woodland

Woodland saprox 4

4 gap1

Open saproxylic 34

Woodland saprox 4

Other decidous

Woodland saprox 4

Conifer obligate

Other decidous

Conifer facultative 23

Conifer obligate

Other decidous

Woodland saprox 4

Other decidous

Open woodland

Conifer obligate

Other decidous

Woodland saprox 4

Open woodland

0 gap1

Open saproxylic 34

Other decidous

Conifer obligate

1 gap1

Other decidous

Open saproxylic 34

Woodland saprox 4

Other decidous

Conifer obligate

2 gap3

Other decidous

Open woodland 34

Conifer obligate

1 gap1

Conifer obligate

Conifer facultative 2

Conifer obligate

Other decidous

Conifer obligate 24

Other decidous

Conifer facultative 23

Other decidous

Open saproxylic 34

Woodland saprox 4

Untouched decido 4

Open woodland 34

Untouched decido 4

Open woodland 34

104

Data for the 626 forest species

Taxon

Basidiomycota

Species name

Lepiota ignivolvata

Lepiota ochraceofulva

Lepiota poliochloodes

Lepiota pseudolilacea

Lepiota subgracilis

Leucoagaricus badhamii

Leucocoprinus brebissonii

Leucopaxillus compactus

Lichenomphalia hudsoniana

Lindtneria trachyspora

Lycoperdon mammiforme

Lyophyllum deliberatum

Lyophyllum eustygium

Lyophyllum leucophaeatum

Lyophyllum semitale

Mycena clavata

Mycena leptophylla

Ossicaulis lignatilis

Pachykytospora tuberculosa

Perenniporia fraxinea

Phaeocollybia arduennensis

Phaeocollybia christinae

Phaeocollybia lugubris

Phellinus laevigatus

Phellodon confluens

Phellodon melaleucus

Phellodon niger

Phellodon tomentosus

Phlebia subserialis

Pholiota squarrosoides

Pholiota tuberculosa

Phylloporus pelletieri

Pisolithus arrhizus

Pluteus atromarginatus

Pluteus aurantiorugosus

Pluteus exiguus

Pluteus hispidulus

Pluteus insidiosus

Pluteus leoninus

Pluteus pellitus

Pluteus roseipes

Polyporus melanopus

Porodaedalea pini

Porotheleum fimbriatum

Porpoloma metapodium

Psathyrella caput‐medusae

Psathyrella leucotephra

Psathyrella spintrigeroides

Psathyrella suavissima

Pycnoporellus fulgens

Ramaria botrytis

Ramaria fagetorum

Ramaria fennica

Ramaria flavescens

Ramaria formosa

Ramaria krieglsteineri

Ramaria largentii

Ramaria pallida

Ramaria sanguinea

Ramaria suecica

Ramariopsis pulchella

Rhizomarasmius undatus

Rhodocollybia prolixa

Rhodocybe hirneola

Rhodocybe melleopallens

Rhodocybe nitellina

Rugosomyces cerinus

Rugosomyces chrysenteron

Rugosomyces ionides

Rugosomyces obscurissimus

Russula albonigra

Russula anthracina

Russula caerulea

Russula carpini

Russula decipiens

Russula emeticicolor

Russula fragrantissima

Russula helodes

Russula innocua

Russula integra

Threat

geography

Denmark

Chapter 2

S2 Table

Saproxylic?

Redlist (Decline? Priority Main affinity

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN yes

Conifer facultative Non‐saproxylic

EN yes

Conifer obligate Non‐saproxylic

VU no

Conifer obligate Saproxylic

VU no

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

CR yes

Deciduous

Saproxylic

CR yes

Deciduous

Saproxylic

VU no

Conifer obligate Non‐saproxylic

VU no

Conifer obligate Non‐saproxylic

EN no

Conifer facultative Non‐saproxylic

EN yes

Deciduous

Saproxylic

EN no

Conifer facultative Non‐saproxylic

EN yes

Conifer facultative Non‐saproxylic

VU yes

Conifer facultative Non‐saproxylic

VU yes

Conifer obligate Non‐saproxylic

VU no

Conifer facultative Saproxylic

CR no

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

EN yes

Deciduous

Non‐saproxylic

VU no

Conifer facultative Non‐saproxylic

VU yes

Conifer obligate Saproxylic

EN yes

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

EN no

Conifer obligate Saproxylic

VU yes

Deciduous

Saproxylic

VU yes

Conifer obligate Saproxylic

VU no

Deciduous

Saproxylic

CR yes

Deciduous

Non‐saproxylic

CR yes

Conifer obligate Saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Saproxylic

EN no

Conifer facultative Non‐saproxylic

EN no

Conifer obligate Saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Conifer obligate Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Conifer obligate Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Conifer facultative Non‐saproxylic

EN yes

Conifer obligate Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Conifer obligate Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

EN no

Conifer obligate Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Conifer obligate Non‐saproxylic

Page 6 of 8

Group

Zone Forests (N) Match gap_Q?

Other decidous

Untouched decido 4

Open woodland

Other decidous

Untouched decido 4

Other decidous

Open woodland 34

Woodland saprox 4

Other decidous

Conifer facultative 23

Conifer obligate

Conifer saproxylic 24

Woodland saprox 4

Open saproxylic 34

Woodland saprox 34

Open saproxylic 34

Conifer obligate

Conifer facultative 23

Woodland saprox 4

1 gap1

Conifer facultative 23

Conifer obligate

Conifer saproxylic 24

Woodland saprox 34

Open saproxylic 34

Other decidous

Conifer facultative 23

Conifer saproxylic 24

Woodland saprox 34

Woodland saprox 4

Woodland saprox 34

Conifer saproxylic 24

Woodland saprox 4

4 gap1

Conifer saproxylic 24

Woodland saprox 4

Open woodland 34

Conifer saproxylic 24

Other decidous

Woodland saprox 4

Conifer facultative 23

Conifer saproxylic 24

Other decidous

1 gap1

Other decidous

Open woodland 34

Other decidous

Conifer obligate

Open woodland 34

Other decidous

Conifer obligate

Open woodland

Other decidous

Open woodland

Conifer facultative 23

Conifer obligate

1 gap1

Other decidous

Untouched decido 34

Other decidous

Conifer obligate

Open woodland

Other decidous

Open woodland

Other decidous

Conifer obligate

Other decidous

Conifer obligate

Chapter 2

Data for the 626 forest species

Taxon

Basidiomycota

Lichen

Threat

geography

Species name

Russula laeta

Denmark

Russula lilacea

Denmark

Russula melliolens

Denmark

Russula mustelina

Denmark

Russula pallidospora

Denmark

Russula pelargonia

Denmark

Russula persicina

Denmark

Russula puellula

Denmark

Russula rhodopus

Denmark

Russula roseoaurantia

Denmark

Russula sanguinea

Denmark

Russula torulosa

Denmark

Russula turci

Denmark

Russula zonatula

Denmark

Sarcodon imbricatus

Denmark

Sarcodon lepidus

Denmark

Sarcodon scabrosus

Denmark

Sarcodon squamosus

Denmark

Simocybe sumptuosa

Denmark

Spongipellis delectans

Denmark

Spongipellis fissilis

Denmark

Steccherinum litschaueri

Denmark

Steccherinum subcrinale

Denmark

Stypella dubia

Denmark

Stypella subgelatinosa

Denmark

Suillus placidus

Denmark

Tomentella crinalis

Denmark

Tomentella italica

Denmark

Tomentella lateritia

Denmark

Tomentella pilosa

Denmark

Tomentella umbrinospora

Denmark

Trametes suaveolens

Denmark

Trechispora silvae‐ryae

Denmark

Tremellodendropsis tuberosa Denmark

Tricholoma apium

Denmark

Tricholoma arvernense

Denmark

Tricholoma aurantium

Denmark

Tricholoma basirubens

Denmark

Tricholoma columbetta

Denmark

Tricholoma focale

Denmark

Tricholoma inamoenum

Denmark

Tricholoma matsutake

Denmark

Tricholoma sejunctum

Denmark

Tricholoma sudum

Denmark

Tricholoma ustaloides

Denmark

Tulostoma fimbriatum

Denmark

Volvariella caesiotincta

Denmark

Volvariella hypopithys

Denmark

Volvariella murinella

Denmark

Volvariella surrecta

Denmark

Xenasma pulverulentum

Denmark

Xerula caussei

Denmark

Xerula longipes

Denmark

Xylobolus frustulatus

Denmark

Anisomeridium biforme

Denmark

Anisomeridium polypori

Denmark

Arthonia radiata

Denmark

Baeomyces placophyllus

Denmark

Caloplaca cerina

Denmark

Cladonia carneola

Denmark

Cladonia cenotea

Denmark

Cladonia floerkeana

Denmark

Cladonia glauca

Denmark

Cladonia incrassata

Denmark

Cladonia novochlorophaea Denmark

Cladonia portentosa

Cladonia ramulosa

Denmark

Cladonia rangiferina

Cladonia squamosa

Denmark

Cladonia sulphurina

Denmark

Dibaeis baeomyces

Denmark

Fuscidea cyathoides

Denmark

Gyalecta flotowii

Denmark

Haematomma ochroleucum Denmark

Lecanactis abietina

Denmark

Lecania naegelii

Denmark

Lecanora argentata

Denmark

Lecanora glabrata

Denmark

Lecanora intumescens

Denmark

Lecanora pulicaris

Denmark

105

Page 7 of 8

S2 Table

Saproxylic?

Redlist (Decline? Priority Main affinity

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Conifer obligate Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU yes

Conifer obligate Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Conifer obligate Non‐saproxylic

EN no

Conifer obligate Non‐saproxylic

VU no

Conifer obligate Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

VU yes

Conifer obligate Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

CR yes

Conifer facultative Non‐saproxylic

EN yes

Conifer obligate Non‐saproxylic

VU no

Deciduous

Saproxylic

CR no

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

CR no

Conifer obligate Non‐saproxylic

VU no

Deciduous

Saproxylic

EN no

Deciduous

Saproxylic

VU yes

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

EN yes

Deciduous

Saproxylic

VU no

Deciduous

Saproxylic

VU yes

Deciduous

Non‐saproxylic

EN no

Conifer obligate Non‐saproxylic

VU yes

Conifer obligate Non‐saproxylic

EN yes

Conifer facultative Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Conifer obligate Non‐saproxylic

VU yes

Conifer obligate Non‐saproxylic

EN no

Conifer obligate Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Conifer obligate Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

EN no

Deciduous

Saproxylic

EN yes

Deciduous

Non‐saproxylic

EN no

Deciduous

Saproxylic

CR yes

Deciduous

Saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Conifer facultative Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Conifer facultative Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Conifer facultative Non‐saproxylic

VU yes

Conifer facultative Non‐saproxylic

CR yes

Conifer facultative Non‐saproxylic

VU yes

Conifer facultative Non‐saproxylic

LC yes

Deciduous

Non‐saproxylic

EN yes

Conifer facultative Non‐saproxylic

LC yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

CR no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Conifer facultative Non‐saproxylic

Group

Zone Forests (N) Match gap_Q?

Open woodland

Other decidous

Conifer obligate

Other decidous

Open woodland

Other decidous

Untouched decido 34

Conifer obligate

3 gap1

Untouched decido 34

Conifer obligate

Untouched decido 34

Conifer obligate

Other decidous

Conifer facultative 23

3 gap1

Conifer obligate

Woodland saprox 4

Woodland saprox 34

Open saproxylic 34

Woodland saprox 4

Conifer obligate

Woodland saprox 4

1 gap2

Woodland saprox 4

1 gap1

Woodland saprox 4

Open woodland

Conifer obligate

Conifer facultative 23

3 gap2

Untouched decido 34

Other decidous

Conifer obligate

1 gap2

Conifer obligate

Other decidous

Conifer obligate

Other decidous

Open woodland

Woodland saprox 4

Untouched decido 4

Open woodland 34

Other decidous

Woodland saprox 4

Other decidous

Woodland saprox 34

Woodland saprox 4

1 gap1

Open woodland

3 gap1

Other decidous

Conifer facultative 23

Open woodland

Conifer facultative 23

Open woodland

Conifer facultative 23

0 gap1

Conifer facultative 23

2 gap1

Open woodland

Conifer facultative 23

Open woodland

4 gap1

Open woodland

Other decidous

Conifer facultative 23

106

Data for the 626 forest species

Taxon

Lichen

Bryophyta

Tracheophyta

Threat

geography

Species name

Lecanora symmicta

Denmark

Lecanora varia

Denmark

Lobaria pulmonaria

Denmark

Melanohalea elegantula

Denmark

Mycocalicium subtile

Denmark

Nephroma laevigatum

Denmark

Ochrolechia androgyna

Denmark

Ochrolechia pallescens

Denmark

Ochrolechia subviridis

Denmark

Ochrolechia turneri

Denmark

Opegrapha herbarum

Denmark

Opegrapha varia

Denmark

Opegrapha vermicellifera

Denmark

Opegrapha viridis

Denmark

Opegrapha vulgata

Denmark

Pachyphiale carneola

Denmark

Peltigera praetextata

Denmark

Pertusaria amara

Denmark

Pertusaria coccodes

Denmark

Pertusaria hemisphaerica

Denmark

Pertusaria leioplaca

Denmark

Porina aenea

Denmark

Pyrenula chlorospila

Denmark

Pyrenula nitida

Denmark

Pyrrhospora quernea

Denmark

Ramalina calicaris

Denmark

Thelotrema lepadinum

Denmark

Trapeliopsis gelatinosa

Denmark

Usnea filipendula

Denmark

Usnea genus

Denmark

Usnea hirta

Denmark

Usnea subfloridana

Denmark

Xanthomendoza poeltii

Denmark

Buxbaumia viridis

Leucobryum glaucum

Sphagnum fallax

Sphagnum fimbriatum

Sphagnum palustre

Sphagnum squarrosum

Sphagnum teres

Asplenium scolopendrium

Denmark

Bromus racemosus

Denmark

Cephalanthera longifolia

Denmark

Cephalanthera rubra

Denmark

Cypripedium calceolus

Dianthus armeria

Denmark

Diphasiastrum complanatum EU

Diphasiastrum tristachyum EU

Epipactis atrorubens

Denmark

Epipactis leptochila

Denmark

Galium valdepilosum

Denmark

Huperzia selago

Laserpitium latifolium

Denmark

Lycopodium annotinum

Lycopodium clavatum

Melampyrum cristatum

Denmark

Melampyrum nemorosum Denmark

Ophrys insectifera

Denmark

Pilosella cymosa

Denmark

Platanthera bifolia ssp. latiflo Denmark

Poa supina

Denmark

Pulmonaria officinalis

Denmark

Sorbus hybrida

Denmark

Ulmus laevis

Denmark

Viola epipsila

Denmark

Viola mirabilis

Denmark

Chapter 2

S2 Table

Saproxylic?

Redlist (Decline? Priority Main affinity

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

RE RE

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

RE RE

Conifer facultative Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

‐

Deciduous

Non‐saproxylic

‐

Conifer facultative Non‐saproxylic

‐

unknownB

Deciduous

Non‐saproxylic

‐

unknownB

Deciduous

Non‐saproxylic

‐

unknownB

Deciduous

Non‐saproxylic

‐

unknownB

Deciduous

Non‐saproxylic

‐

unknownB

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

VU no

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

LC no

Conifer facultative Non‐saproxylic

EN no

Deciduous

Non‐saproxylic

LC no

Conifer facultative Non‐saproxylic

LC no

Conifer facultative Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

EN yes

Deciduous

Non‐saproxylic

CR yes

Deciduous

Non‐saproxylic

VU yes

Deciduous

Non‐saproxylic

Page 8 of 8

Group

Zone Forests (N) Match gap_Q?

Open woodland

Other decidous

Open woodland

0 gap2

Open woodland

0 gap1

Other decidous

Open woodland

Other decidous

Open woodland

Other decidous

Open woodland

Other decidous

Open woodland

Other decidous

Conifer facultative 23

Other decidous

Open woodland

Open woodland 23

Other decidous

Open woodland

Other decidous

Conifer facultative 23

Other decidous

Open woodland

2 gap1

Untouched decido 34

Open woodland

1 gap1

Open woodland

3 gap1

Open woodland

Untouched decido 34

Other decidous

Conifer facultative 23

Open woodland

Conifer facultative 23

Open woodland

3 gap1

Open woodland

1 gap1

Open woodland

Open woodland 34

Open woodland

2 gap2

Open woodland

4 gap1

Open woodland

0 gap2

Other decidous

S2 Table. Species data.

Details of status, preferences and zones for the 626 forest species.

Threat geography: Most of the internationally threatened species are also threatened at national level, but only the most international threat listing is shown

Redlist: Global category if globally threatened. Danish redlist category otherwise (never EU category).

Decline: According to redlist information (for reasons of consistency not according to EU reports).

Zone: The species affinity or preference for one or more of the defined managemenet types (z1= Normal, z2 = Conifer, z3 = Active, z4 = Untouched).

Forests: The number of State Forests in Denmark with occurrence of the species recorded 1991‐2015.

Match: The number of State Forests put in a matching zone for the species by scenario Q. (Only noted for priority A species).

gap_Q?: Gap between target (5 representations or 100%) and matches in scenario Q.

Forests a priori locked to zone 4 (Untouched)

S3 Table

Page 1 of 1

Dist

SHL

VAD

FYN

SDJ

Forest name

Velling Skov

Vorsø mm.

Draved Skov og Kongens Mose

Kasmose Skov

Midtskov

Rønhave Skov

Bolderslev Skov

Decid

103

198

109

Area (ha)

% wood already in MX zones Number of species from each zone group Priority A species % of obs already

Conif Unwood Total zone 3 zone 4 chosen z2 z3 z4 z23 z24 z34 non‐forest Total

All Forest

in z3

in z4

186

47 335

1 4

7 3 3

100

1 4

100

366 565

100

16 10 5

100

3 4

3 2

100

1 4

none

100

1 4

none

46 157

100 1 3 4

100

Criteria for a priori locking to zone 4 ( Untouched):

Chapter 2

Either 100 % of total area untouched already AND > 5 wooded hectares untouched

> 100 hectares woodland untouched AND > 45% of total area untouched

Already untouched includes conifer areas of Velling Forest decided in 1994 to be included as untouched after a transition period removing conifers.

107

108

Forests always in zone 4 (N = 20)

S4 Table

Page 1 of 1

Chapter 2

Dist

HIM

SHL

KJY

VAD

SDJ

STS

HST

NSJ

OSJ

Forest name

Decid

Nørreskov

181

Dalgård

Klostermølle mm.

Ajstrup Strand

Draved Skov og Konge 198

Søgård Skov

Klinteskov

250

Ravneholmene

Jægersborg Dyrehave

435

Ordrup Krat

Arresødal skov

Esrum Møllegård

Gribskov

1943

Søskoven

498

Store Dyrehave

614

Harager Hegn

259

Horserød Hegn

381

Gurre Vang

146

Farum Lillevang

Store Hareskov

181

total

5442

Area (ha)

Conif Unwood

227

366

286

452

1180

722

188

100

352

121

166

258

2382

2802

% already in MX zones Number of species from each zone group Priority A species % of obs already

Total zone 3 zone 4 chosen z2 z3 z4 z23 z24 z34 non‐forest Total

All Forest

in z3

in z4

472

2 14 13 4 2 42

6 1 2

7 1 2

565

16 10 5

160

4 1 1

538

1 33 32 12

46 179

129

2 6 1 2 2

895

1 30 30 7 1 47

31 147

1 3

5 7 1

3 1

3845

7 28 19 16 5 45

43 163

785

1 13 4 6 2 24

1087

1 7 10 4 3 17

359

6 2 3 1 8

616

1 3 5 2 1 13

442

11 2 2 1 8

106

3 4

295

1 10 6 2

10625

Chapter 2

109

Forests always in zone 3 (N = 55)

S5 Table

% already

Number of species from each zone

zones

group

zone zone

non‐

4 chosen z2 z3 z4 z23 z24 z34 forest Total

Total

119

11 21

100

15 24

108 100

1 6

1 1 1

8 18

217

379 20 10

2 4

3 21

1 6

10 17

1682

2 6

23 37

717

5 13 3 5 1 2

15 44

836

2 25 1 6 2 13

59 108

213

23 57

2982

14 13

57 107

6 12

4173

26 31

22 28

189

6 1

2 13

320

1 11 2 3

9 30

10 18

13 20

8 16

2003

30 37

1254

1 9

22 39

124 21 27

14 1 8

20 48

102

409

15 1 1

6 25

124

314

9 28

44 41 20

7 14

2420

9 29 4 2 2 22

48 116

95 17 23

4 18

19 51

207 15

35 56

162 28 30

1 16

25 61

38 55 12

1 6

10 21

9 1 1

3 27

182

9 11

247

287

2 9

13 24

68 19

2 10

1 8 1

13 24

100

403 12

16 3 1

12 39

119

6 3

2 14

12 88

630

4 33

167

10 1

9 22

22310

Page 1 of 1

Area (ha)

Dist

HIM

VJY

SHL

THY

KJY

MJY

VAD

FYN

BLH

TRE

SDJ

STS

BON

HST

VSJ

NSJ

OSJ

Forest name

Decid

Gravlev ådal

Navn Sø

Rebild Bakker

Tihøje Hede

Østerskoven, østlige d

Bjørnholt

Hulbakken

Hvidbjerg Plantage

108

Svinkløv Plantage

132

Mols Bjerge

Hestehave Skov

126

Kalø Hovedgård

Nørlund Plantage

112

Årup Hede

Rømø Strand

Bobakkerne

Sollerup Skov

Svanninge Bakker

Rødme Svinehaver

Thurø Rev

Voderup Klint

Lille Rise

Skallingen

Fanø Plantage

Skærsø

Skovhave M.M.

Ussinggård Sønder og

Drenderup Skov

Svanemosen

Østergård

Sønderskov

278

Rise Skov

Kalvø mv.

Hannenov Skov

144

Halskov Vænge

Holtug Kalkbrud

Almindingen

916

Ølene

Hammersholm og Ham 38

Slotslyngen

Borrelyngen

Strandparken

Kalvebod Fælled

Kongsøre Skov

183

Sonnerup Skov

Vinderød skov

Lyngby skov

Ll. lyngby mose

Grønnæsse skov

Præstevang

Hellebæk Skov

171

Egebæksvang Skov

Fredensborg Skovene

Tokkekøb Hegn

409

Ganløse Eged m.m.

129

total

3918

Conif Unwood

114

100

107

210

138

149

740

835

332

253

715

1435

4171

254

2003

620

580

1097

406

169

153

173

221

108

113

5126 13266

Priority A % of obs

species

already

All Forest in z3 in z4

5 100

9 10 11

3 14

12 57 11

2 100

25 37 44

5 99

6 89

14 15 18

1 100

110

Forests with zone variability (N = 220)

Area (ha)

Dist Forest name

Decid

HIM Skindbjerglund

HIM Præstens Plantage

HIM Smidie Kalkgruber

HIM Vilsted Sø

HIM Fællesskov

187

HIM Livø

VJY Klosterheden Plantag 847

VJY Stråsø Plantage

216

VJY Arealer ved Ferring og

VJY Harboøre Tange

VJY Hoverdal Plantage

115

VJY Fejsø Plantage

200

VJY Husby Plantage

100

VJY Arealer i Ringkøbing k

VJY Fjand Enge

VJY Vest Stadil Fjord

VJY Blåbjerg Plantage

VJY Sandfær

SHL Kobskov Øst

SHL Nordskov

191

SHL Snabegård Skov

SHL Velling Skov

103

SHL Vorsø mm.

SHL Trustrup Høje

SHL Naturstien Stenballe S

SHL Thorsø Bakker

SHL Sønderskov

250

SHL Himmelbjerget

SHL Høgdal

SHL Kallehave

SHL Odderholm

SHL Mariedal

SHL Ny Vissingkloster

SHL Lysbro Skov

SHL Vesterskov

184

SHL Gjern Bakker

SHL Kobskov Vest

VSY Læsø Plantage

375

VSY Skagen Plantage

VSY Bunken Plantage

VSY Råbjerg Plantage

VSY Ålbæk Plantage

VSY Tversted Plantage

VSY Uggerby Plantage

VSY Lilleheden Plantage

VSY Tornby Plantage

VSY Rubjerg Plantage

VSY Slotved Skov

VSY Blokhus Plantage

VSY Tranum Plantage

272

VSY Hune Plantage

THY Lodbjerg Plantage

THY Lyngby Hede

THY Stenbjerg Plantage, ve 12

THY Stenbjerg Plantage, ø

THY Tvorup Syd

101

THY Nystrup Plantage, øst

THY Nystrup Plantage, ves

THY Vandet Plantage

THY Vilsbøl Plantage

143

THY Tved Plantage

181

THY Hanstholm Vildtreserv

THY Hanstholm Kystskræn

THY Østerild Plantage

222

THY Madsbøl Plantage

THY Lild Plantage, vestlige

THY Lild Plantage, østlige d 65

THY Vester Thorup Plantag 117

THY Fosdal Plantage

147

THY Slettestrand

THY Langdal Plantage

KJY Indskovene

276

KJY Hjermind Skov

KJY Lysnet

ConifUnwood

848

307

273

4462 1090

760

942

1114

1039

408

658

731

710

256

253

1108

337

375

529

607

188

186

107

205

229

375

786

714

698

732

484

690

345

525

132

592

127

427

276

233

280

237

104

414

701

1408 2243

340

616

73 1196

385

544

526

317

933

982

833

311

234

612

326

473

603

829

434

80 3208

245

923

450

325

436

631

584

367

651

1059

328

232

140

549

122

461

113

Total

861

592

334

6399

1917

1114

1562

1588

1066

257

1108

802

569

986

135

335

171

684

154

149

653

1875

1474

1221

409

736

812

786

388

560

141

1167

3922

1031

1270

941

931

2016

1239

863

444

1218

1444

3288

252

1595

773

1287

1083

1504

520

549

176

825

265

Chapter 2

S6 Table

already zones

in in

z3 z4 chosen

82 12

3 4

1 2

1 3

2 1 3

32 8

2 3 4

14 11

3 4

2 1 2 3

1 1 2

1 3

2 3 4

3 1 2 3

1 2

1 3

1 1 3

3 4

17 11

2 3

10 0 1 3

23 54 1 4

77 1 4

1 3

1 3 4

9 9 1 2

35 3

3 4

1 3

8 2 1 3

1 3

2 4

1 3

7 14

3 4

29 27

3 4

69 14 1 3

3 4

9 2

3 4

2 3 4

6 1 2 4

1 2

1 1 2

1 2

2 3

2 3 4

22 9 1 3

1 2

4 1 3 4

1 2

5 1 2

1 3 4

9 1 3 4

14 1 3

1 2

1 4

3 4

2 3 4

2 1 2 3

3 4

1 3 4

3 4

4 1 2

1 2

7 1 2 3

3 4

1 3

3 4

13 9

2 4

12 3 1 2 3 4

1 3

Number of species from each zone

group

non‐

z2 z3 z4 z23 z24 z34 forest Total

9 5 2 1 9

1 27

11 14

1 6 3 1

3 24

1 7

11 27

4 10

29 52

14 8

12 1 2

20 57

17 21

12 10 2 9 1 9

30 73

12 5

10 33

2 7

4 1

17 31

9 11

21 27

7 15

1 7

9 23

20 8 2 12 2 16

16 76

8 21

7 3 3

6 27

6 17

1 2 2 4

6 32

15 24

1 12 1 5

20 44

9 16

2 4 1

5 15

3 6 7 7

9 48

1 1 1 3

1 16

9 11 2 8

38 76

11 9 1 11 2 4

38 76

13 12 1 9

50 89

5 5

27 40

6 8

23 44

4 5

2 1 2

22 36

1 6

25 35

2 5

14 25

2 7

27 39

6 1

5 2 4

4 22

4 5

13 25

2 10

5 1 1

31 50

5 2

4 14

1 9

4 1 3

28 46

9 17

8 16

1 5

11 25

2 10

24 41

7 7

7 1 3

10 35

7 7

11 33

4 4 1 2

4 15

2 13 2 6 1 7

20 51

11 9

29 64

1 10

42 61

8 2

18 28

3 7 1 8 1 10

19 49

1 4

14 22

1 3

2 1 3

11 21

2 6

3 1 7

38 57

7 9

4 1 2

23 46

4 8 2 6

22 51

9 12

1 1 1 1

2 10 2 2

11 36

1 3 1 2

4 14

Priority A

species

Page 1 of 3

% of obs MX zone

already

result

All Forest in z3 in z4 O3 Q5

18 75 25 4

8 1

14 38 27 2

7 43

3 1

1 2

13 1

4 1

41 13 35 2

7 28

8 19 65 4

100 4

6 17 1

13 59

8 3

8 70

13 3

18 21 40 3

2 80

7 18

19 17

1 3

3 2

0 1

6 2

33 50 1

4 3

4 1

1 1

0 1

42 3

13 31 20 2

2 24 1

Chapter 2

Forests with zone variability (N = 220)

Area (ha)

Dist Forest name

Decid

KJY Helgenæs Syd

KJY Dragsmur

KJY Ørnbjerg Mølle

KJY Gjerrild Strand

KJY Viborg Plantage

132

KJY Hald Hovedgård

221

KJY Stanghede

KJY Hald Sø

KJY Inderø Skov

KJY Ringelmose Skov

119

KJY Markerne

MJY Sønderskoven

MJY Rydhave Skov

MJY Mønsted Kalkgruber

MJY Alheden Skov

251

MJY Kompedal Plantage

243

MJY Haunstrup Brunkulsle

MJY Bøllingsø

MJY Hastrup Plantage

MJY Kilderne

MJY Palsgård Skov

149

MJY Gludsted

181

VAD Hønning Plantage

120

VAD Lovrup Skov

179

VAD Soldaterskoven

VAD Magisterkog

VAD Klaskeroj Skov

VAD Barsbøl Skov

VAD Åtte Bjerge

VAD Stursbøl Hegn

145

VAD Lindet Skov

160

VAD Stensbæk Plantage

VAD Kirkeby Plantage

VAD Vråby Plantage

VAD Tingdal Plantage

FYN Kasmose Skov

FYN Topgården

FYN Frøbjerg Bavnehøj

FYN Flyvesandet

FYN Nordlangeland

308

FYN Ristinge

FYN Sydlangeland

106

BLH Nyminde Plantage, no 24

BLH Nyminde Plantage, sy

BLH Kærgård Plantage, no

BLH Vrøgum Plantage

BLH Vejers Plantage, sydlig 19

BLH Ål Plantage

BLH Oksby Plantage

TRE Gødding Skov

TRE Frederikshåb Plantage 80

TRE Gyttegård Plantage

TRE Randbøl Hede

TRE Grund Skov

109

TRE Stagsrode Skov

176

TRE Kærskov

TRE Skablund Skov

TRE Rold Skov

TRE Sønder Stenderup Mi 200

TRE Tapsøre Statsskov

148

TRE Kongens Kær

SDJ Nørreskov

546

SDJ Oleskobbel

SDJ Lambjerg Indtægt

SDJ Midtskov

SDJ Rønhave Skov

SDJ Dybbøl

SDJ Dyrehaven

SDJ Rinkenæs Skov

192

SDJ Skodsbøl Skov

SDJ Hønsnap Skov

SDJ Frøslev Plantage

160

SDJ Frøslev Mose

SDJ Areal ved Assenholm

ConifUnwood

174

275

201

198

342

388

640

185

511

2062

378

126

575

404

140

362

720

235

2597

689

256

148

150

227

244

191

365

320

166

235

120

333

255

351

541

332

398

285

681

133

387

284

377

127

431

292

181

504

220

205

252

710

122

198

343

327

Total

209

437

502

249

342

193

390

920

843

2683

236

630

631

456

1104

3467

524

382

173

227

118

465

397

767

406

154

391

447

630

920

754

896

691

599

748

294

804

491

712

154

218

241

232

736

198

111

275

1041

335

111

Page 2 of 3

Priority A

species

% of obs MX zone

already

result

S6 Table

Number of species from each zone

already zones

group

in in

non‐

z3 z4 chosen z2 z3 z4 z23 z24 z34 forest Total

3 4

8 2

14 27

1 3

20 26

37 2 1 4

3 1 1

5 10

1 2

5 12

3 1 2 3

4 10

6 34

3 4

8 3 2

9 38

8 6

3 4

6 1 10

13 36

1 3 4

82 63

3 4

2 21

3 4

5 1 1

13 32

2 3 4

1 4

5 12

1 2

2 4

6 16

61 30 1 3

3 4

6 2

4 13

1 2

1 4

7 18

1 2 4 5 7 1 8 1 1

14 37

1 2

1 9

16 31

6 3 1 3

2 8

4 1 4

5 24

16 4 1 3

28 47

1 2 3 4 1 4

23 40

1 2 3 8 8

22 53

10 3 1 2

3 7

10 26

1 4

1 4 1 2

3 13

1 3

4 13

1 3

20 26

11 2 1 3

1 3

15 5 1 2

1 2

1 3

1 2 3 3 7

6 19

7 5 1 4

2 7 1 3

2 22

1 2

2 9

5 1 3

27 47

1 2 3 1 3

1 1 1

17 24

1 2 3 1 6

9 17

1 3

8 12

44 28

3 4

3 2

1 16

1 3 4

1 3

1 3 4 1 2 1

7 12

3 4

5 2 1

2 12

1 3

11 14

9 0

3 4

2 15 2

21 52

1 3

1 5

21 32

1 2

3 4

20 32

22 1 2 4 1 5 1 4

22 35

1 2

2 2

13 19

2 3

2 4

17 28

1 3

1 6

12 22

1 2

3 3

16 27

1 3 4 1 2

1 3

3 9

11 28

1 3 4 1 9

5 1 6

37 59

1 3

1 9

33 48

3 4

6 2 1

1 14

5 9

3 4

1 10 5 1

1 58

1 2

1 4

1 3

4 2 1 4

2 4

1 3

1 7 1 3 4

3 20

1 3

2 1

1 1 1 3 4

6 10

## 1 4

none

## 1 4

none

0 4 1 2 4

5 1

4 21 1 3 4

4 3 1

3 17

5 1 1 3

1 9 1

5 21

5 1 3

1 3

1 2

2 7

2 1 7

14 33

4 1

3 4

1 10 2 2

34 56

1 3 4

14 19

538

All Forest in z3 in z4 O3 Q5

3 10

16 12 78 3

10 19 11 3

10 46 54 3

3 78

9 1

11 42

7 1

1 1

2 35

3 18

2 38

8 12 68 1

5 19 20 4

21 53

3 1

2 3

1 34 4

3 1

6 1

13 4

44 1

2 3

4 97

112

Forests with zone variability (N = 220)

Area (ha)

Dist Forest name

Decid ConifUnwood

Total

SDJ Bolderslev Skov

109

157

SDJ Jørgensgård Skov

145

168

SDJ Lerskov Plantage

140

SDJ Hytterkobbel

137

SDJ Pamhule Skov

234

353

SDJ Østerskov

145

185

SDJ Slivsø

198

STS Bangsebro Skov

STS Bøtø Nor

100

111

STS Lysbro Mose ved Hejr

STS Ulvshale Skov

308

410

STS Kongskilde

285

324

STS Hørhaven

STS Dybsø

136

BON Kilen

BON Blykobbe Plantage

204

BON Ejendommen Lassen

BON Pedersker Skov

BON Hammerknuden

170

197

BON Del af Ringebakkerne

BON Tuleborg

BON Rø Plantage

213

320

593

HST Jægersborg Hegn

426

554

HST Søllerød Kirkeskov

HST Geel Skov

145

193

HST Enrum Skov

HST Rygård

150

162

HST Charlottenlund Skov

HST Kongelunden

208

331

HST Vestamager

236

1730

1966

HST Folehave

185

263

HST Rude Skov og Frihede 321

120

130

571

HST Vestvolden

289

290

VSJ Rørvig Sandflugtsplan

136

206

VSJ Korshage

VSJ Hov Vig

241

266

VSJ Høve Skov

VSJ Kårup Skov

170

VSJ Sanddobberne

VSJ Røsnæs

166

196

VSJ Diesbjerg

VSJ Bidstrup Skovene

646

195

181

1021

VSJ Myrdeskov

VSJ Boserup Skov

168

224

VSJ Hyrdehøjskoven

NSJ Sonnerup Skov

NSJ Arresø

4102

4121

NSJ Tisvilde hegn

338 1280

375

1992

NSJ AP‐Møllergrunden

NSJ Aggebo og Græsted H 144

188

NSJ Freerslev hegn

210

275

NSJ Stenholt Vang

198

313

NSJ Esrum Sø

1734

NSJ Nakkehoved

NSJ Hornbæk Plantage

105

198

NSJ Teglstrup Hegn

306

138

100

544

NSJ Klosterris Hegn

204

122

353

NSJ Nyrup Hegn

109

103

242

NSJ Danstrup Hegn

197

325

NSJ Krogerup Skovene

154

223

NSJ Lave Skov og Stejlepla

OSJ Ryget

115

149

OSJ Nørreskoven

169

194

OSJ Furesø

932

OSJ Terkelskov

OSJ Ganløse Ore

216

150

396

OSJ Bøndernes Hegn

OSJ Lille Hareskov

110

159

OSJ Jonstrup Vang

174

240

OSJ Vestskoven

556

183

604

1342

OSJ Ravnsholt

150

123

312

OSJ Slagslunde skov

188

224

total

19391 42863 52346 114599

Chapter 2

S6 Table

Page 3 of 3

Number of species from each zone Priority A % of obs MX zone

already zones

group

species

already

result

in in

non‐

z3 z4 chosen z2 z3 z4 z23 z24 z34 forest Total All Forest in z3 in z4 O3 Q5

## 1 3 4

2 11

100 4

4 1

3 4

2 1

4 43

1 2

1 4

1 2

3 11

1 3 4

24 5

3 4

11 35 16

9 41

0 3

0 0 1 3

1 3

13 17

3 4

3 2

1 3

15 28 15

1 3 4

3 1

5 23

2 4

2 17 5 6 1 13

36 80 35

1 47 2

3 4

14 6

12 39 15

1 3

1 2

12 16 13

1 2

6 18

2 4

3 5 1 4

4 19 11

4 11 2

1 3

2 3

2 17

32 64 38

15 75

1 2

4 12

1 4

1 1

2 3 4

6 11

6 29 12

15 1

3 4

1 17 13 7

18 78 43

34 10 15 3

3 4

1 1

1 10

1 14

2 3 4

1 6 5

2 7

1 22 13

1 3

1 4

4 2

3 11

3 4

2 2

1 2

3 4

1 16 2 2 1 10

16 48 28

7 2

3 4

16 4 3

41 71 40

17 16

0 3

1 2 3 1 3 1

19 8 1 2 4 2 11 4 5 3 18

9 52 22

18 10 12 1

3 4

7 2 1

3 19

1 2

4 1

6 13

1 2 3

1 1 1

13 19 15

1 4

12 1 1

24 44 25

1 2

2 1

1 2

2 4

1 1

14 22 18

18 1 3 4

7 10

2 3 4

1 14 4 2 2 11

30 64 37

1 3

20 22 22

2 2

3 4

1 15 6 6 4 25

20 77 33

4 4

3 4

2 1

13 6

3 4

10 11

1 20

2 44 20

19 29

3 3

1 4

5 2

1 3

1 4

9 1 1

9 25 12

0 6

2 3 4

25 30 12 16 6 37

66 192 113

1 2

1 3 4

9 10

1 4

2 1 1 3 6

1 14

9 4 1 3 4

6 1

1 11

8 1 2 3 2 3

3 1 6

3 18

5 1

1 3

9 18

1 3

2 3 4

3 1 1 4 2 6

4 21 10

26 18

3 4

8 3 4 1 18

22 56 22

14 57

4 3

1 2 3 1 2 1 3

3 15

1 6

3 4

2 6 1 1 1 5

1 17

3 3

0 1 1 2

1 8

1 1 8

6 25 10

1 3

1 1 1

1 3

17 30

3 4

4 3 3 1 13

16 40 17

3 82 4

12 9 1 3 4

4 13 1

1 39 10

9 12 34 4

1 3 4

4 1

6 20

45 44

3 4

10 2

5 25 14

9 19 21 3

2 4

2 7 2

1 10

3 25 16

1 3 4

9 2 1

3 19

7 0 1 4

6 2

2 17

11 5

3 4

13 5 1

8 36 20

1 4

2 4

1 9 4 2 3 8

17 44 28

1 0

3 4

10 1 1 1 7

3 23 14

12 15

1 3 1 3 4

2 4

6 15 11

3 1

Chapter 2

113

Page 1 of 11

Forests always in zone 1 (N = 681)

S7 Table

Dist Forest name

Decid

HIM Svalebakken

HIM Stenholmen

HIM Julstrup Sø

HIM Øer ved Hals

HIM Lille Ravnkilde

HIM Rebstrup

HIM Vrå Mølle

HIM Blåkilde Dambrug

HIM Dokkedal

HIM Mosskov

133

HIM Halkær Mølle

HIM Naturstien Nibe‐Hau

HIM Vokslev Kalkgrav

HIM Aars Skov

104

HIM Drastrup Skov

209

HIM Nørager

HIM Poulstrup Skov og Ka

HIM Sct Nicolai Bjerg ved

HIM Arden Skov

HIM Østre Banevej Skov

HIM Hyllebjerg

HIM Grønnerup Strand

HIM P‐plads ved Svingelb

HIM Lille Skovsgårds Hag

HIM Lundshøj

HIM Als Havbakker

HIM P‐plads ved Als Odd

HIM Muddermarens Ø

HIM Pletten

HIM Jenle Plantage

HIM Plovmandshøj Plant

HIM Frendrup hede ‐ Vok

VJY Rønland Sandø

VJY Gørding Havn

VJY Skærum Mølle

VJY Langerhuse

VJY Sønderholmene og P

VJY Høfde 8

VJY Arealer ved Fjaltring

VJY Bøvling Klit

VJY Rammedige

VJY Femhøjsande

VJY Øster Lem Hede

VJY Nørre Vium Brunkul

VJY Troldhede Brunkulsl

VJY Ahlergaarde Brunku

VJY Lystbæk

VJY Rejkær Hede

VJY Arealer i Holmsland

VJY Feldsted Kog

VJY Skårmose Plantage

VJY Ølgryde Plantage

VJY Døes Højene

VJY Møborg Skov

VJY Storåen,Naur

VJY Naturskolen Kærgår

VJY Resenborg Plantage

VJY Birkild Hede

VJY Livbjerggård Strand

VJY Grisetå Odde

VJY Plethøj

VJY Bøløre Odde

VJY Søndbjerg Strand

Area (ha)

% already

ConifUnwood Total in z3 in z4

320

173 625 16

49 221

121 372

120

72 233

140 140

239 336

297 297

1483 1483

29 119

273

64 413

49 154

Priority A % of obs

Number of species from each zone group species

already

z2 z3 z4 z23 z24 z34 non‐forest Total All Forest in z3 in z4

none

7 3

none

6 5

23 16

9 50

9 6

2 1

none

1 1

7 6

2 1

6 5

7 5

none

13 9

2 37

none

4 4

2 2

none

1 1

3 1

none

2 1

none

3 3

none

2 1

1 1

none

4 1

5 2

none

11 8

3 2

9 6

14 9

1 1

none

3 1

none

1 1

3 2

none

1 1

none

2 2

114

Chapter 2

Forests always in zone 1 (N = 681)

S7 Table

Page 2 of 11

Dist

VJY

SHL

VSY

Forest name

Decid

Odby

Skalstrup Skov

Åbjerg Skov

Harpøth Bæk og Dam

Nees Sandø

Toftum Bjerge og Je

Gejlgård Bakke

Arealer ved Skæring

Ajstrup Strand Og N

Areal ved Brabrand

Stendysse ved Orms

Kollens Mølle

132

Bærmose‐ Himmerig

True Skov

260

Hvinningdal Skov

Solbjerg Skov

Anebjerg Skov

Geding Skov

Rugballegård Skov

Arealer i og ved Bryr

Illerup Ådal

Opholds‐ og stiareal

Areal ved Solbjerg S

Ring Kloster

Fårbjerg

Naturstien Horsens‐

Slaggård Banke

Tunø og småøer

Arealer på Samsø

Meden Kirkeruin

Kysing Strand

Hølken Strand

Spøtterup Strand

Hou Strand

Hundslund‐Åkjær na

Mosevej

Lovdal Skov

Østre Stenhule

Alling, vestlige del

P‐plads syd for Alling

Arealer ved Masken

Areal ved Knudsø sy

Knudhule Strand

Vestbirksøerne

Mossø Brå

Bryggebjerg

Birkhede

Øm Kloster

Pindals Mose

Vilholt

Siim Skov

Østerskoven, vestlig

Sminge kanolejrplad

Anderiet mm.

Bøsmølle Bro

P‐plads ved Nebel B

Trækstien

Sorring

Nordmarken

Danzigmand og Blød

Vesterø Sønderland

Rønnerne

Borfeld

Area (ha)

% already

ConifUnwood Total in z3 in z4

6 141

55 152

58 215

147 443

53 147

32 129

36 111

33 106

321 329

4 27

18 55

98 102

347 349

677 700

Priority A % of obs

Number of species from each zone group species

already

z2 z3 z4 z23 z24 z34 non‐forest Total All Forest in z3 in z4

none

2 2

none

2 2

none

3 1

3 3

none

4 2

2 2

1 1

5 3

1 1

none

2 1

none

2 1

7 6

none

2 2

24 20

none

1 1

none

5 4

2 1

none

2 2

none

2 1

1 1

1 33

none

1 1

none

1 1

none

3 2

8 6

4 3

18 13

12 10

2 1

Chapter 2

115

Page 3 of 11

Forests always in zone 1 (N = 681)

S7 Table

Dist

VSY

THY

Forest name

Decid

Hvide Fyr

Byfogedskoven

Skiverbakken

Skiveren Plantage

Tversted Rimmer

Råbjerg Mose

Videslet Engen

Hirsholmene

Areal ved Hulsig

Tversted Klit

Nejst Plantage

Kærsgård Strand

Lien Skallerup

P‐plads ved Skalleru

Areal ved Lønstrup

Mårup Kirke

Rubjerg Knude Fyr

Kajholm

P‐plads ved Kodal

Hjørring‐Astrup skov

Måstrup Mose

Mosbjerg

P‐plads ved Åsted Å

Areal ved Sulbæk

Solsbæk Strand

Søheden Skov

Slettingen

Nybæk Plantage

Munkens Klit

Lille Norge

Fårup Klit

Pirups Hvarre

Grishøjgårds Krat

Gjøl Bjerg

Store Vildmose

Agger Tange

Egebjerg

Aaby Skoven

Rønhede Plantage

Fjordholmene

Ydby‐Nygård Plantag

Hurup Golfskov

Faddersbøl

Sundby Sø

Øer omkring Mors

Ejerslev Vang

Areal ved Hanklit

Buksør Odde

Legind Vejle

Tissing Vig

Arealer Ved Søndre

Tvorup Nord

Vangså Hede

Snedsted Byskov

Kronens Hede Planta

Bavn Plantage

Sjørring Volde

Arealer ved Langdys

Eshøj

Hanstholm Byplanta

Vigsø rallejer

Vigsø og Ballerum P

Korsø Plantage

Area (ha)

% already

ConifUnwood Total in z3 in z4

106 161

196 196

49 116

161 165

869 869

765 765

7 102

132 132

455

67 552

618 623

172

19 196

46 121

340 340

160

80 286

356

434 845

Priority A % of obs

Number of species from each zone group species

already

z2 z3 z4 z23 z24 z34 non‐forest Total All Forest in z3 in z4

none

21 17

13 11

1 4

16 11

7 5

8 4

none

3 3

none

17 15

none

21 11

none

4 2

none

2 1

4 3

3 3

9 7

none

18 10

none

11 2

25 17

1 1

none

2 1

none

1 1

none

3 3

none

4 3

4 4

5 2

2 1

1 1

15 7

22 13

none

8 6

2 2

none

6 5

1 5

15 9

1 1

7 6

1 1

9 5

116

Chapter 2

Forests always in zone 1 (N = 681)

S7 Table

Page 4 of 11

Dist Forest name

Decid

THY Hjardemål Plantage

THY Tømmerby Kær

THY Areal ved Selbjerg V

THY Frøstrup Skov

THY Aggersborg

THY Kollerup Plantage

THY Hingelbjerg

THY Hingelbjerg Mose

THY Husby Hole

THY Bredkær Plantage

KJY Udskovene

201

KJY Randers Nørreskov

KJY Randers Nordre Fæl

161

KJY Vindum Skov

KJY Busbjerg

KJY Frisenvold Laksegård

KJY Frydensbjerg og Kat

KJY Hohøj Arealer

KJY Hadsund Bane

KJY Mosely

KJY Hærup P‐plads

KJY Ulbjerg Klint

KJY Sundstrup Arealerne

KJY Rønnen

KJY Bjødstrup Strand

KJY Karpenhøj

KJY Vænge Sø

KJY Mågeøen

KJY Areal ved Stubbe Br

KJY Natursti Ebeltoft‐Gr

KJY Vibæk Strand

KJY Holme strandareale

KJY Hyllested Bjerge

KJY Bisballe‐Almind

KJY Bruunshåb

KJY Klostermarken

KJY Klokkerholm Skov

KJY Randers Sønderskov

KJY Øer i Randers Fjord

KJY Elløv Enge

MJY Borbjerg‐Nørreskove 453

MJY Sjørup Skov

MJY Arealer på Nordfur

MJY Jenle

MJY Brokholm Sø

MJY Havbjerg Skov

MJY Vinderup Skov

MJY Geddal Strandenge

MJY Spøttrup Sø

MJY Arealer på Lundø

MJY Egekrat ved Aulum

MJY Småarealer Salling

MJY Grynderup Sø

MJY Arealer ved Durup

MJY Løvbakke Skov

101

MJY Areal ved Rabis

MJY Ikast Byskov

MJY Søby Brunkulslejer

MJY Funder‐Ejstrup natu

VAD Råbjerg Plantage

VAD Renbæk Plantage

VAD Arrild Plantage

VAD Nørreskov

Area (ha)

% already

ConifUnwood Total in z3 in z4

707

388 1156

134

115 254

254

185 479

136

14 350 20

93 140

116

47 324 27

135 135

39 98

105 132 20

1662

262 2376

208

42 331

6 13

141 143

62 146

89 171

126 126

113 114

100

46 186

129

215 366

15 117 21 15

44 205

Priority A % of obs

Number of species from each zone group species

already

z2 z3 z4 z23 z24 z34 non‐forest Total All Forest in z3 in z4

2 6

32 19

3 2

none

2 3

18 11

none

4 3

none

8 4

2 33

none

4 3

12 9

none

5 4

none

2 2

1 1

none

5 5

7 4

none

3 3

100

9 7

1 11

none

9 8

1 1

none

3 7

17 10

1 1

5 4

none

5 3

4 3

1 1

11 5

2 2

none

2 1

none

3 3

2 1

2 2

1 4

16 8

4 2

1 1

1 4

9 3

1 1

Chapter 2

117

Page 5 of 11

Forests always in zone 1 (N = 681)

S7 Table

Dist Forest name

Decid

VAD Gasse Høje

VAD Nørresø og Hestholm

VAD Haraldsholm Skov

VAD Jelssøerne

VAD Sidekanal ved Lintru

VAD Parceller i Sømose o

VAD Jættestuer ved Over

VAD Skrydstrup Skov

VAD Lindet Mose

VAD Mandbjerg Skov

VAD Varming og Nørbæk

VAD Toftlund Skov

VAD Dankirke

VAD Bevtoft Plantage

VAD Gammelskov

VAD Bjerreskov

VAD Favrholt Skov

VAD Tange Bakker

VAD Tange Enge

VAD Tvismark Plantage

VAD Vestergårde Bjerge

VAD Albatros

VAD Klægtagningsarealer

VAD Arealer ved Husum

VAD Areal på Mandø

FYN Røjle Klint

FYN Fortidsminder

FYN Småøer

FYN Vestermose Skov

FYN Holmeskoven

FYN Klakkebjerg

FYN Arealer ved Vissenbj

FYN Feddet

FYN Helnæs Made

FYN Nørreby Hals

FYN Fuglsanggård

FYN Otterup Byskov

FYN Fjordmarken

FYN Vigelsø

FYN Kirkendrup Skov

132

FYN Elmelund Skov

FYN Fyns Hoved

FYN Bogensø Strand

FYN Lods Huse

FYN Lærkedal

FYN Sønderskovgård

FYN Storelung

101

FYN Ringe Skov

FYN Naturstien Ringe ‐ K

FYN Trente Mølle

FYN Lyø

FYN Avernakø

FYN Tåsinge Vejle

FYN Vorbjerg

FYN Borgnæs

FYN Gråsten Nor

FYN Toftegårdsskoven

FYN Egehovedskoven

FYN Hov Østerland

FYN Rudkøbing Fredskov

FYN Humble Byskov

FYN Næs

BLH Blåbjerg Plantage

124

Area (ha)

% already

ConifUnwood Total in z3 in z4

5 129

100

58 58 14

296

63 458

62 16

134

14 191

34 112

240 302

248 250

108 132

79 150

124 270

63 169

4 100

806

467 1396

Priority A % of obs

Number of species from each zone group species

already

z2 z3 z4 z23 z24 z34 non‐forest Total All Forest in z3 in z4

none

15 8

8 5

none

1 1

none

2 1

100

none

1 4

8 2

2 1

none

1 4

7 3

1 1

none

10 4

none

12 10

2 2

none

8 5

1 1

9 5

none

7 3

1 1

none

6 4

none

8 6

18 11

none

1 1

8 5

5 2

2 2

1 1

14 10

3 3

none

4 3

none

2 2

1 1

none

6 3

2 1

3 2

8 6

none

1 1

none

2 6

36 23

118

Chapter 2

Forests always in zone 1 (N = 681)

S7 Table

Page 6 of 11

Dist

BLH

TRE

Forest name

Decid

Nørre Nebel Skov

100

Golfbane

Oversigtsareal ved H

Årgab

Bavnebjerg

Havrendingen

Bjergeborg

Bjerregård

Holmsland Klit

Tipperne

Klægbanken mm.

Præstens ø og Tykrå

Polde

Øer i Nymindestrøm

Skjern Å

Lønborg Hede

Kærgård Plantage, s

Vejers Plantage, nor

Sig kapelbanke

Bordrup Plantage

Ho Plantage

Langli

Fyrpasserboligen

Hafniagrunden

Oles Dige

Lodder Øst for Oksb

Kikkebjerg Plantage

Torp

Sønderho

Søren Jessens Sand

Trinden og Keldsand

Engelsholm Skov

Tykhøj Krat

Refstrup Skov

Nørup Plantage

Vingsted Mølle

Randsfjord Arealer

Vognkær Enge og Hv

Børkop Vandmølle

Fårup Skov

Fire Høje

Sophienlund

Haltrup Hede

Troldhedebanen Ves

Eg Rasteplads

Tirsbjerg Plantage

Bjerge Skov

215

Tønballegård

Bankehave

122

Boller Nederskov

Boller Overskov

Klokkedal

Ustrup Bjerge

Dybdal

Dallerup Skov

Lystrup Skov

Borringholm

Sebberup Skov

Sønder Stenderup N 331

Stenderup Hage

Sønder Stenderup S 144

Skibelund

Grønninghoved Stra

Area (ha)

% already

ConifUnwood Total in z3 in z4

46 163

154 154

689 689

112 112

1963 1967

344 344

192

412 608

358

305 693

625

185 856

302

95 423

100 100

64 147 20

25 282

15 156

63 14

42 141

30 412

27 193 14

18 100

Priority A % of obs

Number of species from each zone group species

already

z2 z3 z4 z23 z24 z34 non‐forest Total All Forest in z3 in z4

5 5

none

7 5

1 1

none

5 5

21 11

6 3

none

7 4

36 21

18 13

1 2

22 13

1 4

19 13

none

1 3

20 16

2 2

17 13

20 12

none

1 1

2 1

none

1 1

2 1

none

3 2

9 5

1 1

none

3 3

none

12 6

3 2

2 2

5 2

2 22

4 2

1 48

none

1 1

4 3

none

2 1

11 6

none

13 4

8 10

none

Chapter 2

119

Page 7 of 11

Forests always in zone 1 (N = 681)

S7 Table

Dist

TRE

SDJ

Forest name

Decid

Vargårde Skov

Trommersgård Skov

Fovslet Skov

199

Hoppeshuse

Harte Skov

103

Troldhedebanen

Rastepladser ved Ny

Hakonsminde

Søballegård

Tørrepladsen

Klingebæk

Ravning Enge

Bindeballe Station

Troldhedebanen Syd

Karholm

Øvelgunde Fredskov

Fryndesholm

Græskobbel

Blommeskobbel

Rumohrsgård Dyreh

Ketting Nor

Oldenor

Augustenborg Skov

Made Skov

Arnkil Skov

Arnkil Maj

Fredskov

Mjang Dam

Hartsø Strand

Nydam Mose

115

Roden Skov

Adsbøl Dam

Sø‐ og Lystskovareal

Over‐ og Nederstjer

Buskmose Skov

Avnbøl Sned

Bøffelkobbel

Skelde Folekobbel

Skelde Kobbelskov

Opholdsarealer ved

Helligsø

Nybøl

Arealer ved Strande

Kelstrup Fredsskov

Kelstrup Plantage

147

Rode Skov

Kiskelund Plantage

Kollund Skovholm

Kruså Skov

Mølleskov

Store Okseø

Gårdbæk Skov

Waldeck Skov

Rønshovedskovene

Gammelmose Skov

Lyreskoven

Parcel i Søndermose

Kragelund Mose

Vejbæk Skov

Bommerlund Planta 155

Oksekær

Skov ved Bjerndrup

Bøghoved

Area (ha)

% already

ConifUnwood Total in z3 in z4

50 14

14 260

100 167

78 240

3 100

100

31 100

20 22 13

44 106

128 128

23 145

4 98

75 10 10

130

54 331

4 15 68

368

151 674

Priority A % of obs

Number of species from each zone group species

already

z2 z3 z4 z23 z24 z34 non‐forest Total All Forest in z3 in z4

none

1 1

10 5

1 1

10 8

6 4

1 1

none

1 1

4 4

none

100

5 2

3 2

8 5

4 2

4 1

1 100

2 1

1 75

9 5

none

1 1

6 4

2 2

none

3 3

1 17 17

1 1

100

11 6

2 2

none

1 1

4 1

6 2

none

1 1

none

3 2

1 5

10 3

11 6

7 4

none

2 1

1 1

100

none

2 1

none

2 1

none

4 2

none

2 5

11 4

none

1 1

2 1

120

Chapter 2

Forests always in zone 1 (N = 681)

S7 Table

Page 8 of 11

Area (ha)

% already

Dist Forest name

Decid ConifUnwood Total in z3 in z4

SDJ Areal syd for Sønder

SDJ Hostrup Krat

SDJ Kæmpehøj i Hostrup

SDJ Arealer på Varnæs H

SDJ Årtoft Plantage

72 134

43 250

SDJ Torp Plantage

38 209

SDJ Hjordkær

SDJ Årup Skov

195

55 285

11 104

SDJ Sønderskov, Aabenr

SDJ Hjælm

50 14

SDJ Vestermark

14 120

SDJ Langbjerg Skov

SDJ Søst Skov

SDJ Nørreskov, Aabenra

SDJ Rhedersborg Skov

SDJ Rugbjerg Plantage

33 112

19 164

SDJ Rundemølle

5 38 13

SDJ Jagtprøvebane

SDJ Strangelshøj

SDJ Hjarup Mose

SDJ Arealer ved Potterhu

SDJ Hop Sø

SDJ Tormaj

SDJ Fredshule

SDJ Abkær Mose

SDJ Femhøje

71 147 34

SDJ Haderslev Søndersko 70

SDJ Hjelmvrå

SDJ Teglholt

40 12

SDJ Tørning Mølle

SDJ Sandkule

47 58 40

SDJ Ladegård eng

SDJ Elkær dambrug

SDJ Vesterskov

151

20 187 12

SDJ Nautrupgård Skov

SDJ Tamdrup Høj

SDJ Årø Skov mm

SDJ Keldet Skov

SDJ Loft Skov

SDJ Revsø Skov

137

34 213

SDJ Gravhøj i Sommerst

SDJ Areal ved Råde Stran

STS Ovstrup Skov

159

40 221

STS Sønder Kohave

STS Gedser Fyr

STS P‐pladser på Falster

STS Albuen

STS Langødyssen og Bav

STS Majbøllereservatet

STS Enehøje

97 100

STS Teglværksskoven

STS Vildmarksskoven

STS Krogsbølleskoven

STS Krukholm Skov

STS Hyllekrog Fyr

STS Krukholm Lille Skov

STS Øer på søterritoriet

STS Mark ved Bursø

STS Udby Skov

38 105

STS Hegnede Skov

STS Nyord

136 136

STS Areal ved Hjelm Bug

STS Bakkely Skov

Priority A % of obs

Number of species from each zone group species

already

z2 z3 z4 z23 z24 z34 non‐forest Total All Forest in z3 in z4

1 1

5 2

none

2 2

8 4

1 1

none

5 3

3 2

6 2

2 2

1 13

2 1

4 3

3 3

1 1

none

1 1

3 2

none

1 1

none

3 2

3 1

4 2

1 46

6 3

2 17

3 1

1 93

2 1

1 1

10 5

none

11 8

none

8 4

none

8 6

none

10 6

none

9 4

6 5

5 1

none

1 1

none

1 1

none

3 3

11 7

none

Chapter 2

121

Page 9 of 11

Forests always in zone 1 (N = 681)

S7 Table

Dist Forest name

Decid

STS Tårnborggård

STS Galgebakke og Hash

STS Højbjerg Skov

STS Kobæk Skov

STS Glumsø Statsskov

STS Areal ved Sandvig

STS Prins Carls Skole

STS Gåsetårnet

STS Faksinge Skov

STS Vrangstrup Enge v. S

STS Anneliselunden

STS Stevns Fyr

STS Avnø

STS Rønnebæk fælled

STS Ladby Skov

STS Fælleseje Skov

STS Vridsløse Skov

STS Even Skov

STS Fakse Kalkbrud

STS Fodsporet

STS Øer på søterritoriet

BON Arealer ved Arnager

BON Areal ved Sose Odde

BON Areal ved Nordbakke

BON Areal ved Gubbegår

BON Egeby fortidsmindeo

BON Areal ved Hundsemy

BON Udkæret

BON Kærgården

BON Skovholt

BON Ringborgen

BON Arealer ved Vandmø

BON Areal ved Stammers

BON Byggehøj

BON Rø Præsteskov

BON Øster Borregårds Sk

BON Areal ved Bobbeå

BON Salene Bugt

HST Trørød Hegn

HST Kohave Skov

HST Ermelunden

HST Mikkelborg

HST Bredelte

HST Hørsholm Slotshave

HST Bistrup Hegn

HST Stumpedysse Hegn

HST Sjælsø Lund

VSJ Grønnehave Skov

147

VSJ Annebjerg Skov

VSJ Nakke Skov

VSJ Højby Sø

VSJ Skansehage

VSJ Øer på søterritoriet

VSJ Nygård Sø

192

VSJ Ulkerup Skov

VSJ Stokkebjerg Skov

187

VSJ Grevinge Skov

VSJ Hønsehals Skov

VSJ Bognæs Skov

VSJ Mosemark Skov

VSJ Øer på søterritoriet

VSJ Ellinge Indhegning

VSJ Jyderup Skov

Area (ha)

% already

ConifUnwood Total in z3 in z4

76 112

207 211

100

4 27

32 203

66 291

12 231

8 126

131

12 213

Priority A % of obs

Number of species from each zone group species

already

z2 z3 z4 z23 z24 z34 non‐forest Total All Forest in z3 in z4

5 3

none

1 1

8 6

5 2

none

4 2

none

32 16

2 1

none

3 2

2 2

3 3

none

11 5

7 5

2 1

none

1 1

none

2 1

1 83

none

3 1

2 1

8 4

none

1 1

6 3

none

5 4

4 3

6 4

18 13

none

9 8

none

4 3

9 6

1 1

4 3

6 5

2 1

none

1 1

5 4

122

Chapter 2

Forests always in zone 1 (N = 681)

S7 Table

Page 10 of 11

Priority A % of obs

species

already

All Forest in z3 in z4

Dist

VSJ

NSJ

Forest name

Decid

Bjergene

129

Vollerup Skov

Klosterskov

Overby Lyng

Stenstrup Troldstue

Lærkereden

Veddinge Bakker

Sydspids af Sejerø

Gravhøj ved Svebøll

Vestborgen

Stenhus i Kalundbor

Øer på søterritoriet

Vrangeskov

Fællesfolden

Svallerup Strand

Langesø Eng

Langdysse ved Vielst

Knud Lavards Kapel

Tadre Mølle

Øer på søterritoriet

Fugledegaard

Pedersted Skov

Stubberup Skov

Kattinge Søerne

Gershøj

Arealer ved Ejby

Arealer ved Gamme

Høng Skov

Benløse Skov

Skovrejsning Slagels

Brødemose skov

Avderød skov

Holstrupgård

Fuglsanggård

Kanalerne

Nordhuse

Arrenæs‐arealer

Statens tørvemose

Hyttegården

Hovgårds pynt

Holløse bredning

Alsønderup enge

Solbjerg enge

Lyngby mose

Ullerup skov

Sandflugtsplantagen

Hyllingbjerg

Stejlepladserne

Skansen

Sandflugtsmonumen

Helenekilde

Strandbjerggård

Vieholmgård

Rågegården

Gilbjerggård

Valby Hegn

267

Høbjerg hegn

137

Nejede vesterskov

Æbelholt klosterruin

Skævinge

Gørløse

Søborg Slotsruin

Klosterhus

Area (ha)

% already Number of species from each zone group

ConifUnwood Total in z3 in z4 z2 z3 z4 z23 z24 z34 non‐forest Total

35 205

35 122

none

1 1

none

1 1

16 9

none

1 1

none

7 4

none

2 2

3 2

none

3 1

7 6

none

6 4

none

2 2

158 175

8 7

none

17 7

11 4

16 4

none

20 101

21 12

none

2 2

none

1 1

none

2 2

1 9

36 23

30 360

1 6

16 11

6 151

6 4

7 107 49 48

13 5

none

143 219

2 2

101 129

none

Chapter 2

123

Page 11 of 11

Forests always in zone 1 (N = 681)

S7 Table

Dist

NSJ

OSJ

Forest name

Decid

Esrum Kanal

Parcel i Nødebo

Sørup Havn

Fredensborg Havn

Nødebo Holt

Gadevang Skov

Selskov

Mose i Gadevang

Tirsdagsskoven

Brøde Skov

143

Lille Hestehave

Snevret

Horneby Sand

Risby Vang

Krogenberg Hegn

154

Munkegårds Hegn

Endrup Hegn

Kelleris Hegn

Dageløkke Skov

Skipperholm

Kovang

Knorrenborg Vang

Grønholt Vang

164

Grønholt Hegn

183

Stasevang

Farum Sø

Nyvang

Skydebanearealerne

Ganløse Forte

Brudehøje

Kollekolle

Snubbekorsskoven

Karlstrup Skov

Øer i Køge Bugt

Regnemark

Ramsø

Greve Skov

Solhøj Fælled

Tune Skov

Kildebrønde Skov

Sønderskov

Lystrup Skov

138

124

Uggeløse skov

Krogenlund

Bastrup sø

Hammergård m.m.

Sperrestrup skov

Grønlien skov

Hørup Skov

Gulddysse Skov

Himmelev Skov

total

14274

Area (ha)

% already

ConifUnwood Total in z3 in z4

49 96

14 160

14 179

37 23 76

67 19 24

25 251

10 59 19

30 109

45 236

8 12

48 273

125 125

59 16

84 104

154 208

38 111

104 113

44 17

43 199

20 162

31 116

40 121

84 140

12525 28070 54869

Priority A % of obs

Number of species from each zone group species

already

z2 z3 z4 z23 z24 z34 non‐forest Total All Forest in z3 in z4

none

8 1

1 100

14 4

2 1

none

7 3

3 3

3 33 33

4 1

1 1

2 1 2

11 6

2 2

none

7 3

16 7

1 4

7 2

1 2

5 2

10 5

12 11

6 60

none

1 1

15 10

28 17

3 3

7 2

none

1 1

none

12 5

4 4

14 10

6 1

19 15

10 8

none

3 3

1 1

5 4

8 6

124

Chapter 2

S8 Table

S8 Table. Results of scenarios.

Scenario

area

25.9

24.3

28.2

26.9

23.6

29.2

39.3

38.2

39.3

38.2

37.1

20.3

25.1

38.3

19.7

25.6

57.8

13.7

15.2

58.2

13.4

15.6

25.9

24.3

29.5

26.9

23.6

30.3

39.3

40.3

39.3

40.6

37.1

20.3

27.7

38.3

19.7

28.1

57.9

13.1

18.8

58.2

13.0

19.5

36.7

20.3

28.6

38.1

19.7

27.3

26.0

24.2

29.3

35.8

24.3

23.5

36.7

20.3

28.6

58.0

13.0

19.1

26.0

24.2

29.3

35.8

24.3

23.5

36.7

20.3

28.6

58.0

13.0

19.1

57.2

15.5

15.2

46.5

9.4

19.4

46.5

area

21.7

20.3

22.5

17.5

16.5

13.3

12.8

20.4

19.2

20.4

20.1

14.9

14.0

10.2

9.4

14.4

15.0

20.5

16.4

14.4

9.9

20.5

16.4

14.4

9.9

12.1

24.7

53.5

cost%

annual

61.3

61.4

68.7

53.0

53.6

36.9

37.1

53.9

53.5

58.4

45.6

45.9

32.6

32.4

46.1

46.5

50.3

42.7

45.5

30.5

42.9

36.3

38.1

25.5

33.1

48.1

58.9

(ha)

22,933

21,541

23,797

23,807

18,502

17,424

14,094

13,519

21,573

20,302

21,577

21,241

15,803

14,810

10,763

9,964

15,281

15,878

21,693

17,338

15,281

10,524

21,693

17,338

15,281

10,524

12,771

26,119

56,679

(n)

203

ATA

0.83

0.84

0.87

0.86

0.87

0.91

0.83

0.84

0.87

0.86

0.87

0.92

0.87

0.88

0.85

0.90

0.87

0.91

0.85

0.90

0.87

0.91

0.93

Target met

(of 304

species)

217

215

199

229

232

257

217

215

199

229

232

261

262

231

230

221

255

231

258

221

255

231

258

166

Lines in bold

are scenarios with target set at 3 representations instead of 5. See table 4 and 7 for

other assumptions for each scenario. ATA = average target achievement for the priority A species

included in the scenario. z = zone. NA = not applicable. All area measures deal with woodland

area, i.e. excluding unwooded areas like grasslands etc.

Chapter 2

Gap species of scenario Q (N = 73)

Threat

geography

Taxon

Species name

Mammalia

Barbastella barbastellus

Coleoptera

Allecula morio

Denmark

Coleoptera

Ampedus hjorti

Globally

Coleoptera

Crepidophorus mutilatus

Denmark

Coleoptera

Gnorimus nobilis

Denmark

Coleoptera

Ischnodes sanguinicollis

Denmark

Coleoptera

Lucanus cervus

Denmark

Coleoptera

Stictoleptura scutellata

Denmark

Coleoptera

Tachyta nana

Denmark

Diptera

Caliprobola speciosa

Denmark

Diptera

Chalcosyrphus valgus

Denmark

Diptera

Cheilosia frontalis

Denmark

Diptera

Criorhina floccosa

Denmark

Diptera

Cyrtopogon lateralis

Denmark

Diptera

Epistrophe grossulariae

Denmark

Diptera

Eumerus ornatus

Denmark

Diptera

Laphria ephippium

Denmark

Diptera

Microdon analis

Denmark

Diptera

Xanthogramma festivum

Denmark

Diptera

Xylota abiens

Denmark

Hymenoptera Formicoxenus nitidulus

Globally

Lepidoptera

Boloria euphrosyne

Denmark

Lepidoptera

Brachionycha nubeculosa

Denmark

Lepidoptera

Calliteara abietis

Denmark

Lepidoptera

Cosmia affinis

Denmark

Lepidoptera

Herminia tarsicrinalis

Denmark

Lepidoptera

Nudaria mundana

Denmark

Lepidoptera

Nymphalis polychloros

Denmark

Lepidoptera

Xestia ditrapezium

Denmark

Orthoptera

Chorthippus dorsatus

Denmark

Araneae

Araneus alsine

Denmark

Araneae

Midia midas

Denmark

Ascomycota

Gyromitra gigas

Denmark

Basidiomycota Camarophyllopsis atropuncta Denmark

Basidiomycota Clavaria flavipes

Denmark

Basidiomycota Crepidotus cinnabarinus

Denmark

Basidiomycota Entoloma aprile

Denmark

Basidiomycota Entoloma formosum

Denmark

Basidiomycota Entoloma lampropus

Denmark

Basidiomycota Entoloma queletii

Denmark

Basidiomycota Exidia cartilaginea

Denmark

Basidiomycota Flammulaster limulatus

Denmark

Basidiomycota Gloeoporus dichrous

Denmark

Basidiomycota Hemipholiota heteroclita

Denmark

Basidiomycota Hypholoma ericaeum

Denmark

Basidiomycota Inocybe sambucina

Denmark

Basidiomycota Lactarius albocarneus

Denmark

Basidiomycota Lactarius hysginus

Denmark

Basidiomycota Phellinus laevigatus

Denmark

Basidiomycota Polyporus melanopus

Denmark

Basidiomycota Ramaria fennica

Denmark

Basidiomycota Rugosomyces chrysenteron Denmark

Basidiomycota Russula rhodopus

Denmark

Basidiomycota Sarcodon scabrosus

Denmark

Basidiomycota Tomentella lateritia

Denmark

Basidiomycota Trametes suaveolens

Denmark

Basidiomycota Tricholoma aurantium

Denmark

Basidiomycota Tricholoma inamoenum

Denmark

Basidiomycota Xylobolus frustulatus

Denmark

Lichen

Anisomeridium polypori

Denmark

Lichen

Cladonia incrassata

Denmark

Lichen

Cladonia novochlorophaea Denmark

Lichen

Cladonia sulphurina

Denmark

Lichen

Melanohalea elegantula

Denmark

Lichen

Mycocalicium subtile

Denmark

Tracheophyta Bromus racemosus

Denmark

Tracheophyta Cypripedium calceolus

Tracheophyta Dianthus armeria

Denmark

Tracheophyta Melampyrum cristatum

Denmark

Tracheophyta Melampyrum nemorosum

Denmark

Tracheophyta Pulmonaria officinalis

Denmark

Tracheophyta Sorbus hybrida

Denmark

Tracheophyta Viola epipsila

Denmark

Red

list

125

Page 1 of 1

Forests

gap_

Saproxylic?

Group

Zone (N) Match Q?

Non‐saproxylic Open woodland

3 gap1

Saproxylic

Open saproxylic

0 gap1

Saproxylic

Open saproxylic

3 gap2

Saproxylic

Open saproxylic

3 gap2

Saproxylic

Open saproxylic

0 gap1

Saproxylic

Open saproxylic

0 gap1

Saproxylic

Open saproxylic

0 gap2

Non‐saproxylic Open woodland

4 gap1

Saproxylic

Open saproxylic

0 gap1

Saproxylic

Woodland saproxy 4

1 gap1

Saproxylic

Woodland saproxy 4

2 gap2

Non‐saproxylic Open woodland

0 gap1

Saproxylic

Woodland saproxy 4

4 gap1

Non‐saproxylic Open woodland

1 gap1

Non‐saproxylic Open woodland

2 gap2

Non‐saproxylic Open woodland

1 gap1

Non‐saproxylic Conifer obligate

1 gap3

Saproxylic

Open saproxylic

4 gap1

Non‐saproxylic Open woodland

3 gap2

Saproxylic

Woodland saproxy 4

2 gap1

Non‐saproxylic Conifer obligate

3 gap2

Non‐saproxylic Open woodland

4 gap1

Non‐saproxylic Open woodland

0 gap1

Non‐saproxylic Conifer obligate

0 gap3

Non‐saproxylic Open woodland

3 gap1

Non‐saproxylic Open woodland

3 gap1

Non‐saproxylic Conifer facultative 23

3 gap1

Non‐saproxylic Open woodland

3 gap2

Non‐saproxylic Open woodland

0 gap1

Non‐saproxylic Open woodland

1 gap3

Non‐saproxylic Open woodland

1 gap1

Non‐saproxylic Open woodland

0 gap1

Non‐saproxylic Conifer obligate

1 gap2

Non‐saproxylic Open woodland

0 gap1

Non‐saproxylic Open woodland

4 gap1

Saproxylic

Woodland saproxy 4

0 gap1

Non‐saproxylic Open woodland

2 gap2

Non‐saproxylic Open woodland

4 gap1

Non‐saproxylic Conifer facultative 23

2 gap1

Non‐saproxylic Untouched decido 4

0 gap2

Saproxylic

Woodland saproxy 4

3 gap1

Saproxylic

Woodland saproxy 4

3 gap1

Saproxylic

Woodland saproxy 4

2 gap3

Saproxylic

Woodland saproxy 4

4 gap1

Non‐saproxylic Open woodland

0 gap1

Non‐saproxylic Conifer obligate

1 gap1

Non‐saproxylic Conifer obligate

2 gap3

Non‐saproxylic Conifer obligate

1 gap1

Saproxylic

Woodland saproxy 4

1 gap1

Saproxylic

Woodland saproxy 4

4 gap1

Non‐saproxylic Other decidous

1 gap1

Non‐saproxylic Conifer obligate

1 gap1

Non‐saproxylic Conifer obligate

3 gap1

Non‐saproxylic Conifer facultative 23

3 gap1

Saproxylic

Woodland saproxy 4

1 gap2

Saproxylic

Woodland saproxy 4

1 gap1

Non‐saproxylic Conifer facultative 23

3 gap2

Non‐saproxylic Conifer obligate

1 gap2

Saproxylic

Woodland saproxy 4

1 gap1

Non‐saproxylic Open woodland

3 gap1

Non‐saproxylic Conifer facultative 23

0 gap1

Non‐saproxylic Conifer facultative 23

2 gap1

Non‐saproxylic Open woodland

4 gap1

Non‐saproxylic Open woodland

0 gap2

Non‐saproxylic Open woodland

0 gap1

Non‐saproxylic Open woodland

2 gap1

Non‐saproxylic Open woodland

1 gap1

Non‐saproxylic Open woodland

3 gap1

Non‐saproxylic Open woodland

3 gap1

Non‐saproxylic Open woodland

1 gap1

Non‐saproxylic Open woodland

2 gap2

Non‐saproxylic Open woodland

4 gap1

Non‐saproxylic Open woodland

0 gap2

S9 Table

De‐

Prio‐

cline? rity Main affinity

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Conifer obligate

yes

Deciduous

yes

Deciduous

yes

Deciduous

Conifer obligate

yes

Deciduous

yes

Conifer obligate

Deciduous

yes

Deciduous

yes

Conifer facultative

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Conifer obligate

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Conifer facultative

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Conifer obligate

yes

Conifer obligate

yes

Conifer obligate

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Conifer obligate

yes

Conifer obligate

yes

Conifer facultative

yes

Deciduous

yes

Deciduous

yes

Conifer facultative

yes

Conifer obligate

yes

Deciduous

yes

Deciduous

yes

Conifer facultative

yes

Conifer facultative

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

yes

Deciduous

126

CHAPTER 3

Identifikation af arter og naturtyper

i 2020 biodiversitets målene

- Med særligt henblik på Naturstyrelsens arealer

Erik Buchwald & Jacob Heilmann-Clausen

(September 2016)

128

Previous page: Lepus europaeus (photo by Erik Buchwald)

Chapter 3

129

UNIVERSITY OF COPENHAGEN

NATURAL HISTORY MUSEUM OF DENMARK

CENTER FOR MACROECOLOGY, EVOLUTION AND CLIMATE

Identifikation af arter og naturtyper

i 2020 biodiversitets målene.

Med særligt henblik på Naturstyrelsens arealer.

Erik Buchwald

Jacob Heilmann-Clausen

(Title)

(Subtitle)

September 2016

130

Chapter 3

Titel

Identifikation af arter og naturtyper i 2020 biodiversitets målene.

Med særligt henblik på Naturstyrelsens arealer.

Kontekst

Rapporten er udarbejdet som led i Erhvervs-PhD-projektet: "Analyse og prioritering

af fremtidig indsats for biodiversitet - med særligt henblik på Naturstyrelsens arealer".

Projektet løber 2015-18 og har til formål at undersøge og analysere, hvor og hvordan

man i Danmark kan gøre en effektiv indsats for at hindre tab af biodiversitet – særligt

på de ca. 2.000 kvadratkilometer af Danmark, som forvaltes af Naturstyrelsen.

Forfattere

Erik Buchwald, Erhvervs-phd studerende

Jacob Heilmann-Clausen, Lektor, vejleder

Dato

20. september 2016

Sted

Center for Makoøkologi, Evolution og Klima

Statens Naturhistoriske Museum, Københavns Universitet

Universitetsparken 15, bygning 3

2100-København Ø.

Layout

Lotte Nymark Busch Jensen, Erik Buchwald

Forsidefoto

Engperlemorsommerfugl på blåhat, taffeland, leverurt og kystklint på Romsø (alle

fotos: Erik Buchwald)

Sideantal

Chapter 3

131

Indholdsfortegnelse

INDLEDNING ...................................................................................................................... 4

Opgave afgrænsning ...................................................................................................................................................... 4

FN- OG EU- 2020-MÅL RELEVANTE FOR IDENTIFIKATION AF ARTER OG

NATURTYPER .................................................................................................................... 5

Natura 2000 - fugle, arter og naturtyper ......................................................................................................................... 5

Truede arter .................................................................................................................................................................... 6

Invasive arter .................................................................................................................................................................. 7

Mål, som ikke bidrager til identifikation af arter og naturtyper ..................................................................................... 7

Proces ............................................................................................................................................................................. 7

ARTER OG NATURTYPER, SOM KAN PÅVIRKE OPFYLDELSEN AF EU'S 2020

BIODIVERSITETS MÅL, OG SOM KAN FORVALTES PÅ NST AREALER ...................... 8

ARTER PÅ HABITATDIREKTIVET ........................................................................................................................... 8

Konklusion vedr. arter fra habitatdirektivet ................................................................................................................. 10

NATURTYPER PÅ HABITATDIREKTIVET ............................................................................................................ 11

FUGLEBESKYTTELSESDIREKTIVETS ARTER ................................................................................................... 11

Konklusion vedr. arter fra fuglebeskyttelsesdirektivet................................................................................................. 13

TRUEDE ARTER, SOM KAN INFLUERE PÅ OPFYLDELSEN AF FN'S 2020

BIODIVERSITETS MÅL, OG SOM KAN FORVALTES PÅ NST AREALER .................... 14

GLOBALT TRUEDE ARTER ...................................................................................................................................... 14

NATIONALT TRUEDE RØDLISTEARTER ............................................................................................................. 17

Tilbagegang eller ej...................................................................................................................................................... 19

EU arter ........................................................................................................................................................................ 19

Marine arter, sø og å .................................................................................................................................................... 20

Konklusion vedr. nationalt truede arter ........................................................................................................................ 20

BILAG................................................................................................................................ 21

132

Chapter 3

Indledning

Denne rapport er udarbejdet som led i Erhvervs-PhD-projektet: "Analyse og prioritering af

fremtidig indsats for biodiversitet - med særligt henblik på Naturstyrelsens arealer". Projektet løber

2015-18 og har til formål at undersøge og analysere, hvor og hvordan man i Danmark kan gøre en

effektiv indsats for at hindre tab af biodiversitet – særligt på de ca. 2.000 kvadratkilometer af

Danmark, som forvaltes af Naturstyrelsen (NST).

Politisk har de fleste af verdens lande, herunder Danmark, ved aftaler under Biodiversitets-

Konventionen forpligtet sig til at standse tilbagegangen i biodiversitet. Der er i den forbindelse

opstillet specificerede mål for år 2020 både på EU-niveau og globalt i FN-regi. Målene omhandler

bl.a. indsats for truede arter, så deres udryddelse forebygges. Truede arter listes i den såkaldte

rødliste fordelt på tre kategorier CR (kritisk truet), EN (moderat truet) og VU (sårbar), afhængigt af

hvor truet arten vurderes at være. Desuden er kategorien RE (regionalt uddød) relevant, i det

omfang sådanne arter dukker op igen.

Projektet vil især for Naturstyrelsens arealer analysere og prioritere de truede arter og deres

konkrete levesteder i forhold til viden om arternes økologiske behov, udbredelse og klimakrav,

relation til driftsformer, samt hidtidige udvikling. Fokus er på arter, som ser ud til at være af særlig

betydning for, at Danmark kan nå det politisk vedtagne 2020-mål om at standse tabet i biodiversitet.

Nærværende rapport identificerer som en del af processen, hvilke arter og naturtyper, som er af

betydning for, at Danmark kan nå 2020-målene. Det sker populært sagt ved at regne baglæns fra

målene - i det omfang det kan lade sig gøre. Hvordan der skal prioriteres mellem disse arter og

tilknyttede naturtyper for at nå 2020-målene vil være et væsentligt emne i resten af PhD-projektet.

Opgave afgrænsning

Arter omfattes, som er truede globalt, nationalt eller på Natura 2000 direktivernes bilag (ynglefugle

bilag 1; habitat bilag 2, 4 og 5). Erhvervs-PhD-projektet og dermed denne opgave ser dog kun på de

mere eller mindre terrestriske arter og naturtyper. Dvs. fuldt akvatiske arter i hav, sø og å er udeladt.

Akvatiske arter, som tidvis er på land (amfibiske arter) medtages, såfremt en eller flere arter i

familien er medtaget på habitatdirektivets bilag. Derfor er fx fisk, hvaler, slørvinger og døgnfluer

ikke behandlet, mens bl.a. sæler, frøer, salamandre, vandkalve og guldsmede behandles.

Chapter 3

133

FN- og EU- 2020-mål relevante for identifikation af arter

og naturtyper

Danmark har politisk forpligtet sig til at arbejde for at standse tilbagegangen i biodiversitet inden

2020, dels i FN regi, dels i EU regi. I

FN regi

drejer det sig mere konkret om COP 10 vedtagelsen

af 20 Aichi mål i Nagoya i 2010. I

EU regi

gælder det biodiversitetsstrategien fra 2011

indeholdende 6 specificerede mål, som skal sikre at EU-landene lever op til det overordnede globale

mål og til de 20 Aichi mål.

Mange af målene er overordnede, generelle eller fokuserede på andre emner, så de ikke direkte kan

relateres til konkrete arter eller naturtyper. Det vurderes at gælde de mål, som listes sidst i dette

afsnit. Nedenfor tages derfor udgangspunkt i de mål, som er mere konkrete i relation til arter og

naturtyper.

Natura 2000 - fugle, arter og naturtyper

EU regi

gælder det biodiversitetsstrategiens mål nr. 1 og 3, som handler om inden 2020 at

forbedre status for Natura 2000 naturtyper, arter og fugle jf. følgende tabel (jf EU's impact

assessment til strategien, dokument SEC(2011) 540 final af 3/5 2011):

Delmål for gunstig status

Basis

(2004 - 2007)

Natura 2000 naturtyper

Natura 2000 arter

Fuglearter

17%

52%

33%

25%

80%

100%

50%

Mål 2020

Stigning, afrundet

For Natura 2000

naturtyperne

er målet således, at der i 2020 skal være 100% flere typer med god

eller forbedret status sammenlignet med vurderingen i 2007. Det betyder at:



Tallet skal stige fra 17% til 33% for Natura 2000 naturtyperne.

Det svarer ifølge EU til 100% forbedring efter afrunding.

134

Chapter 3

For Natura 2000

arter og fugle,

er målet, at der i 2020 skal være 50% flere med god eller forbedret

status sammenlignet med de forrige vurderinger (i 2004 for fugle henholdsvis 2007 for andre arter).

Det betyder at:



Tallet skal stige fra 17% til 25% for Natura 2000 arter, og

Stige fra 52% til 80% for fugle.

Begge ændringer svarer ifølge EU til 50% forbedring efter afrunding.

Alle tallene er opgjort i de biogeografiske regioner, og ikke pr land. Det medfører, at tallene ikke

direkte kan oversættes til nationalt niveau, men i praksis vil være forventet minimum for hvert

enkelt land, idet alle, eller i hvert fald flertallet af lande, er nødt til at opnå positiv status for at EU

samlet kan få det på regionsniveau.

EU's mål 3, om at der bl.a. skal laves driftsplaner for offentlige skove inden 2020, som skal bidrage

til at forbedre status for arter og naturtyper omfattet af mål nr 1, understøtter, at det er arter og

naturtyper fra habitat- og fugledirektiverne, målopfyldelsen skal vurderes på.

Truede arter

FN regi

går Aichi mål nr. 12 ud på at undgå uddøen af truede arter (dvs rødliste kategorierne CR

kritisk truet, EN moderat truet og VU sårbar) inden 2020, og at forbedre arternes status: "By 2020,

the extinction of known threatened species has been prevented and their conservation status,

particularly of those most in decline, has been improved and sustained".

Mål nr. 12 har nogle valgfri forslag til milepæle (teknisk baggrundsdokument

COP/10/INF/12/Rev.1):



Inden 2012 vurdere og opdatere information om

globalt

truede arters forekomst i landet,

samt aktivt forebygge deres kortsigtede uddøen.

Inden 2014 gennemføre en

national

rødliste vurdering.

Inden 2016 udarbejde en strategi til forebyggelse af uddøen af alle

nationalt

truede arter.

Det er således arter, som er truede på enten globalt eller nationalt niveau, der er i fokus for mål nr.

12, samt genopdukkede regionalt uddøde arter, kategori RE.

Chapter 3

135

Invasive arter

EU mål nr 5 og FN mål nr 9 går ud på at få identificeret og forvaltet invasive fremmede arter inden

2020. Invasive fremmede arter er dermed i fokus både i EU- og FN-regi. EU har efterfølgende

vedtaget en liste med invasive arter på EU-niveau. En foreløbig dansk "sortliste" arbejdes der på at

gøre endelig, mens en liste over arter opfattet invasive i forhold til Natura 2000 naturtyper har

foreligget i en årrække.

Invasive arter behandles ikke yderligere i nærværende rapport, idet der henvises til de nævnte

officielle lister (http://svana.dk/natur/national-naturbeskyttelse/invasive-arter/).

Mål, som ikke bidrager til identifikation af arter og naturtyper

Følgende EU og FN mål er ikke specifikke med hensyn til konkrete arter eller naturtyper, idet de er

mere overordnede:

EU mål nr 2 (økosystem genopretning og ydelser), 4 (fiskeri) og 6 (global indsats og handel).

FN mål nr 1 (formidling), nr 2 (plan og processer), nr 3 (tilskud), nr 4 (bæredygtig produktion og

forbrug), nr 5 (standse fald i naturareal), nr 6 (undgå overfiskeri), nr 7 (bæredygtig

skovforvaltning), nr 8 (forurening), nr 10 (koralrev og klimaforandringer), nr 11 (17% af landarealet

naturbeskyttet), nr 13 (kulturplante genetik), nr 14 (økosystemydelser), nr 15 (kulstoflagre og

klimatilpasning), nr 16 (access and benefit sharing), nr 17 (national biodiversitets strategi), nr 18

(inddragelse af indfødte folk), nr 19 (videnopbygning og -deling), samt nr 20 (finansiering).

Som følge af opgaveafgrænsningen er marine arter, herunder fisk, udeladt. Hvis marine arter havde

været med, kunne EU mål 4 og FN mål 6 om fiskeri have været analyseret nærmere for om der

eventuelt kunne identificeres relevante arter.

Proces

Udfra ovenstående mål, udredes i de følgende kapitler hvilke arter og naturtyper, der omfattes af

henholdsvis EU og FN mål, og potentielt kan forekomme på NST arealer, samt hvilke af arterne og

typerne, der ser ud til at kunne få størst indflydelse på målopfyldelsen i 2020 (ved populært sagt at

regne baglæns fra målene).

136

Chapter 3

Arter og naturtyper, som kan påvirke opfyldelsen af

EU's 2020 biodiversitets mål, og som kan forvaltes på

NST arealer

I ovenstående kapitel fremgik det, at

biodiversitetsstrategiens mål nr. 1, som handler om inden

2020 at forbedre status for Natura 2000 naturtyper, arter og fugle, er det afgørende mål i relation til

identifikation af arter og naturtyper. Det drejer sig om arter på habitatdirektivets bilag (nr 2, 4 og 5)

og om fugle omfattet af fuglebeskyttelsesdirektivet, dvs alle vilde fuglearter.

ARTER PÅ HABITATDIREKTIVET

For at give et indtryk af, hvor Danmark ligger i forhold til EU, kan følgende tal med visse forbehold

sammenlignes. De danske tal baseres på de 130 regionale artsvurderinger for 83 arter, som blev

rapporteret til EU i 2013 (tabel 1), link:

http://cdr.eionet.europa.eu/Converters/run_conversion?file=dk/eu/art17/envuqrtva/DK_species_rep

orts-131220-95922.xml&conv=354&source=remote

Tabel 1

Natura 2000

arter

EU 2007 snit

Danmark 2007

Danmark 2013

EU 2020 mål

Gunstig

Forbedringer

Moderat ugunstig

Stærkt ugunstig

Ukendt

I alt procent

Ikke opgjort

100

Ikke opgjort

18 (+24 mangler)

100

Ikke opgjort

100

Ikke opgjort

Der er (ekskl. den genudsatte bæver) 82 arter fra habitatdirektivets bilag 2, 4 eller 5, som findes i

Danmark ifølge 2013 rapporteringen til EU, og som dermed kan spille ind på opfyldelsen af EU's

2020 mål nummer 1. En række arter er rapporteret fra mere end en af Danmarks to biogeografiske

regioner, så det samlede antal vurderinger er 130.

Af de 82 arter er 63 mere eller mindre terrestriske arter (inkl. vandhulsarter og sæler), hvor indsats

kan være relevant, hvis de findes på NST arealer. De 63 arter listes artsgruppevis i rapportens bilag

Chapter 3

137

1, 2, 3 og 4 med angivelse af om de har haft rapporteret ugunstig status i 2007 eller 2013, samt om

de vurderes at forekomme på Naturstyrelsens arealer.

De øvrige er 19 fuldt akvatiske arter, som ikke omfattes af nærværende studie, jf.

opgaveafgrænsningen. Det gælder 5 fuldt marine arter og 14 limniske arter (bilag 5).

De arter, som umiddelbart influerer på EU's 2020 mål ved (jf data i bilag 1 til 4) at have haft

rapporteret ugunstig status i en eller begge regioner af Danmark i enten 2007 eller 2013, og som

med større eller mindre sikkerhed findes på Naturstyrelsens arealer er uddraget og sammenstillet i

henholdsvis tabel 2 og 3.

Tabel 2.

Arter på habitatdirektivets bilag 2, 4 eller 5, som

vides at findes

på Naturstyrelsens arealer,

og som har været rapporteret ugunstige til EU i mindst en region i enten 2007 eller 2013. I alt 29

arter. De fleste af arterne er sjældne og med få eller meget få forekomststeder, men nogle af arterne

er mere udbredte. Arter på bilag 2 er også ofte på bilag 4 (ikke vist).

Kode

1042

1037

1048

1058

1065

1081

1082

1084

1166

1188

1197

1201

1202

1203

1210

1212

1214

1261

Navn

Stor kærguldsmed

Grøn kølleguldsmed

Grøn mosaikguldsmed

Sortplettet blåfugl

Hedepletvinge

Bred vandkalv

Lys skivevandkalv

Eremit

Stor vandsalamander

Klokkefrø

Løgfrø

Grønbroget tudse

Strandtudse

Løvfrø

Grøn frø

Latterfrø

Spidssnudet frø

Markfirben

Videnskabeligt navn

Leucorrhinia pectoralis

Ophiogomphus cecilia

Aeshna viridis

Maculinea arion

Euphydryas aurinia

Dytiscus latissimus

Graphoderus bilineatus

Osmoderma eremita

Triturus cristatus

Bombina bombina

Pelobates fuscus

Bufo viridis = Bufotes variabilis

Bufo calamita = Epidalea calamita

Hyla arborea

Rana esculenta = Pelophylax esculentus

Rana ridibunda = Pelophylax ridibundus

Rana arvalis

Lacerta agilis

Biotop

vandhul

vandløb

vandhul

græsland

vandhul

skov

vandhul

græsland

Bilag

138

Chapter 3

1308

5009

1323

1341

1355

1378

1386

1409

1831

1902

1903

Bredøret flagermus

Dværgflagermus

Bechsteins flagermus

Hasselmus

Odder

Rensdyrlav

Grøn buxbaumia

Tørvemos (alle arter)

Vandranke

Fruesko

Mygblomst

Barbastella barbastellus

Pipistrellus pygmaeus

Myotis bechsteinii

Muscardinus avellanarius

Lutra lutra

Cladonia spp. (subgenus Cladina)

Buxbaumia viridis

Sphagnum spp.

Luronium natans

Cypripedium calceolus

Liparis loeselii

skov

vand

græsland

skov

mose

vand

kalkbund

græsland

Det fremgår af kolonnen Biotop, at 13 af de 29 arter hører til vandhuller, 6 arter hører til skov, 5

arter hører til græsland, og de sidste fem arter til mose, vand eller kalkbund. En lignende overvægt

af vådområder fremgår af tabel 3, hvilket viser at forvaltning af vandhuller og andre vådområder

kan være af særlig betydning. Dette mønster forstærkes, hvis man går et spadestik dybere og ser på

om status er stærkt eller moderat ugunstig (ikke vist her).

En nærmere granskning af tilgængelige kilder vil sikkert vise, at også flere af arterne i tabel 3 findes

på Naturstyrelsens arealer (målrettet eftersøgning i felten indgår ikke i projektet).

Tabel 3.

Arter på habitatdirektivets bilag 2, 4 eller 5, som

måske findes

på Naturstyrelsens arealer,

og som har været rapporteret ugunstige til EU i mindst en region i 2007 eller 2013. I alt 6 arter.

Kode

1013

1014

1343

1364

1393

1528

Navn

Kildevældsvindelsnegl

Skæv vindelsnegl

Birkemus

Gråsæl

Blank seglmos= Fedtet Krogmos

Gul stenbræk

Videnskabeligt navn

Vertigo geyeri

Vertigo angustior

Sicista betulina

Halichoerus grypus

Drepanocladus vernicosus=Hamatocaulis v.

Saxifraga hirculus

Biotop

Kilder

græsland

Kyst

Kilder

Konklusion vedr. arter fra habitatdirektivet

Arterne på habitatdirektivets bilag 2, 4 og 5 analyseres nøjere for at se, hvor de findes på

Naturstyrelsens arealer, og om drift og pleje af arealerne er passende eller kan forbedres for disse

arter. Særlig fokus vil være på arterne i tabel 2 og 3.

Chapter 3

139

For en række af arterne er der allerede gjort en stor og målrettet indsats, men der er sandsynligvis

også en række lokaliteter, hvor der kunne gøres en ekstra indsats for arterne. Det gælder nok især

for de arter, som ikke er på direktivets bilag 2, og derfor ikke har fået udpeget habitatområder eller

fået lavet Natura 2000 planer.

NATURTYPER PÅ HABITATDIREKTIVET

De naturtyper, som omfattes af EU-målet for 2020 findes på habitatdirektivets bilag 1 og er dermed

omfattet af Natura 2000 planer. Tabellen viser rapporterede vurderinger for de 60 danske

habitattyper, som ofte har to vurderinger pr. type grundet to biogeografiske regioner (fra

https://circabc.europa.eu/sd/a/ae61e78c-c1b1-46f4-abc3-ebf892b30c03/DK_20140528.pdf):

I 2013 var de få gunstige typer alle strandnære (Vadegræssamfund, forklit, strandvold og estuarie).

Alle fuldt terrestriske typer er rapporteret ugunstige til EU enten i 2007 eller 2013, hvorfor

udmøntningen af Natura 2000 planer for naturtyper bl.a. på Naturstyrelsens arealer er vigtig for at

forbedre status. De fleste af naturtyperne (bortset fra de marine) kan findes på NST arealer.

FUGLEBESKYTTELSESDIREKTIVETS ARTER

Ved seneste officielle danske rapport om fugle til EU i 2013 rapporteredes 230 poster, omfattende

oplysninger om status og udvikling for 193 danske ynglefuglearter og 37 vintergæster, idet arter

som både optræder som vintergæster og som ynglefugle (fx ederfugl) har to poster

(http://bd.eionet.europa.eu/activities/Reporting/Article_12/Reports_2013/Member_State_Deliveries).

140

Chapter 3

Da der er tale om en fælles EU-standard metode, er følgende tal sammenlignelige. De danske tal

baseres på ovennævnte 230 officielt rapporterede artsvurderinger for fugle (tabel 4):

Tabel 4

Fugle

Gunstig (stabil/fremgang)

Ugunstig (tilbagegang)

I alt procent

EU 2004 snit

100

Danmark 2004

Ikke opgjort

100

Danmark 2013

% (siden 2000)

100

EU 2020 mål

% (siden 2004)

100

I Danmark er udviklingen siden 2000, jf tabel 4, stabil eller positiv for 70 procent af de rapporterede

fugle. For resten (30 %) er udviklingen negativ siden 2000. De 30 % repræsenterer 67

artsvurderinger, som derfor vil influere negativt på 2020 målene, hvis udviklingen ikke vendes. Der

er 3 af de 67 vurderinger, som omhandler marine vintergæster (fløjlsand, ederfugl og knortegås),

hvorfor de i relation til nærværende projekt ikke er relevante.

Tilbage er 64 ynglefuglearter, som har vist tilbagegang siden 2000. Indsats for dem vil være

relevant, herunder på NST arealer. De listes i bilag 6 og 7 alt efter om de også var ugunstige på EU-

niveau i 2004 eller ej. Bilagene er derudover sorteret efter biotop og antal par.

Arter med stabil eller positiv udvikling siden 2000 vil ikke influere negativt på målene, hvis

udviklingen fortsætter, hvilket kunne forventes, idet beskyttelsesniveauet ikke er forringet. Dog er

enkelte af disse fugle truede på den danske rødliste 2010. Forebyggelse af fald i deres bestand kan

være af betydning for EUs 2020 mål: Drosselrørsanger, markpiber, mosehornugle, hvidbrystet

præstekrave, stor kobbersneppe, pirol og tinksmed. Pirol er skovfugl, de øvrige fra mere åbent land.

Det fremgår tilsvarende af bilag 6, at langt de fleste af arterne med tilbagegang både i Danmark og

EU hører til i det åbne land, idet dette også gælder de to arter med biotop angivet som "diverse". De

fire skovfugle, som er på bilag 6, hører ligesom pirol især til i lyse skove med skovlysninger.

Bortset fra et par arter er både kort- og langtidstrenden negativ i bilag 6, både i Danmark og EU.

De fleste af arterne i bilag 7 er ligesom i bilag 6 ret talrige, således at det især vil være generelle

forhold på landsplan og i EU, der vil være afgørende for deres udvikling. I tabel 5 listes de af

fuglene fra bilag 6 – 7, som omfattes af denne rapports opgaveafgrænsning ved enten at være truede

eller på fuglebeskyttelsesdirektivets bilag 1.

Chapter 3

141

Tabel 5.

Fugle som i Danmark er truede eller på fugledirektivets bilag 1, og som samtidig er

rapporteret til EU som værende i tilbagegang siden 2000 (Ekskl den uddøde urfugl). Sorteret efter

biotop og antal par.

Trend 1980-2011

Fluktuerer

Stabil

Primær biotop

skov

mose/sø/å

kyst

hede

græsland

agerland

Bilag 1-art?

Bestand (mindst)

135

420

2.400

4.500

Vendehals

Vandstær

Pungmejse

Sortterne

Sandterne

Brushane

Karmindompap

Engryle (almindelig ryle)

Fjordterne

Klyde

Havterne

Stor Tornskade

Hvid Stork

Toplærke

Kirkeugle

Arterne i tabel 5 vil i forhold til opgaveafgrænsningen være de mest oplagte at fokusere på for at nå

2020-målene. Også øvrige arter på bilag 6 og 7 er af betydning for målopfyldelsen, selvom de

hverken er truet ifølge rødlisten eller på fuglebeskyttelsesdirektivets bilag 1, og derfor udenfor

opgaveafgrænsningen. Fx er duehøg, taffeland, grønspætte og ride også så fåtallige, at konkrete

beskyttelses indsatser vil kunne forbedre landsudviklingen.

Konklusion vedr. arter fra fuglebeskyttelsesdirektivet

De mest oplagte fuglearter at gøre noget for på Naturstyrelsens arealer er listet i tabel 5, men alle

vilde fuglearter kan influere på EU's 2020-mål. Alle truede og bilag 1-arter analyseres jf.

opgaveafgrænsningen nøjere for at se, hvilken andel af landets bestand, der findes på styrelsens

arealer, hvor de findes på Naturstyrelsens arealer, og om drift og pleje af arealerne er passende eller

kan forbedres for disse arter. For en række af arterne er der allerede gjort en stor og målrettet

indsats, men der er sandsynligvis også en række lokaliteter, hvor der kan gøres ekstra indsats. De

mere talrige arter (resten af bilag 6 og 7) vil være vanskeligere at påvirke ved konkrete tiltag, idet

deres bestands udvikling vil være mest afhængig af overordnede faktorer på landsplan.

142

Chapter 3

Truede arter, som kan influere på opfyldelsen af FN's

2020 biodiversitets mål, og som kan forvaltes på NST

arealer

Første kapitel gennemgik, at af

FNs

biodiversitets mål for 2020, er Aichi mål nr. 12 direkte

relevant for artsforvaltningen, idet det går ud på at undgå uddøen af truede rødlistearter inden 2020,

og at forbedre arternes status. Målet fokuserer særligt på de arter, som har haft størst tilbagegang,

og har jf. første kapitel foreslåede milepæle for

globalt

truede arter og for

nationalt

truede arter.

Her ses derfor på henholdsvis globalt og nationalt truede arter i relation til de arealer, som

Naturstyrelsen forvalter. De truede arter er CR (kritisk truet), EN (moderat truet) og VU (sårbar).

GLOBALT TRUEDE ARTER

Aichi mål nr. 12 har en foreslået milepæl om, at hvert land vurderer og opdaterer information om

globalt truede

arters forekomst i landet, samt aktivt forebygger deres kortsigtede uddøen.

Data for globalt truede arter kendt fra Danmark er hentet d. 8/5 2015 fra naturbeskyttelses-

organisationen IUCN (permalink:

http://www.iucnredlist.org/search/link/554c4d07-c8f7a9d9

Tabel 6:

Oversigt over de 36 globalt truede arter, som er noteret fra Danmark i IUCNs globale

rødliste (maj 2015), samt velkendt forekomst i Danmark og på Naturstyrelsens arealer (F&N2015).

Status i Danmark

Antal

arter

Arter

hos

NST

Terrestrisk ynglende arter

Ferskvands ynglende arter

Marint ynglende arter

Marine ikke-ynglende arter

Reelt ikke tilstede i Danmark

I alt

Antal

steder i

Danmark

Ca. 97

Ca. 55

Ca. 152

Steder

hos

NST

Ca. 35

Ca. 14

Ca. 49

Andel

steder

hos NST

36 %

25 %

32 %

tabel 7

tabel 8

bilag 8

bilag 9

bilag 10

Mere info

Chapter 3

143

Der kan på Naturstyrelsens arealer potentielt gøres relevant indsats for de 9 forekommende

henholdsvis terrestriske og ferskvandsarter i tabel 7 og 8, men næppe for de marine eller kun

tilfældigt tilstedeværende arter, hvor mere information er listet i bilag 8, 9 og 10.

De 6 arter i bilag 8 er havfisk, som yngler i danske farvande, og som ikke findes på Naturstyrelsens

arealer. Arterne i bilag 9 er ikke-ynglende marine fugle, fisk og hvaler og derfor heller ikke

relevante på Naturstyrelsens arealer. Ålen er dog en undtagelse, men den er primært truet af en

parasiterende invasiv svømmeblæreorm, som så vidt vides ikke kan påvirkes ved arealforvaltning.

Det er ikke i Danmark muligt (eller relevant) at gøre noget for de 3 arter i bilag 10, da arterne

faktisk ikke eller kun helt tilfældigt har optrådt i Danmark.

Tabel 7:

De i maj 2015 globalt truede

terrestriske arter

med yngleforekomst i Danmark, og deres

velkendte forekomst, herunder på Naturstyrelsens arealer (NST).

Art

Latinsk navn

Global

Dansk

Forekomst i DK

Kilde: Fugle & Natur + rødlisten +

KU-mos-database (maj 2015)

Violsmælder

Limoniscus

violaceus

En art edderkop

Dolomedes

plantarius

En art smælder

Hede-

Takspinder

Blank

gæstemyre

Bølgebladet

tandsvøb

Sump-

vindelsnegl

Ampedus hjorti

Phyllodesma

ilicifolia

Formicoxenus

nitidulus

Jamesoniella

undulifolia

Vertigo

moulinsiana

Myrer er ikke

bedømt

Mosser er ikke

bedømt

Snegle er ikke

bedømt

Kun set 2 steder i 1865 og i Jelling

Skov 1904.

Fundet >49 steder, heraf 16 NST

Fundet >9 steder, heraf 5 NST

LC, livskraftig

NT, næsten truet

Fundet >27 steder, heraf 6 NST

Fundet >5 steder, heraf 3 NST

VU, sårbar

Fundet 7 steder, heraf 5 NST

RE, forsvundet

3 ex fundet 1924 i ét træ på Bognæs

kategori rødlistekategori

Det fremgår af tabel 7, at det ikke vil være muligt at gøre noget for violsmælder eller for mosset

bølgebladet tandsvøb, idet de to arter ikke er set i Danmark i ca. 100 år. Derimod findes der på

Naturstyrelsens arealer en relativt stor andel af flere af de andre arters bestande. En af arterne -

144

Chapter 3

sump-vindelsnegl – er på habitatdirektivets bilag 2, og håndteres i forbindelse med EU's 2020 mål.

Derudover vil de 4 resterende arter i tabel 7 skulle undersøges nærmere for at se på konkrete

muligheder for at hjælpe dem på Naturstyrelsens arealer.

Tabel 8:

De globalt truede

ferskvands arter

med yngleforekomst i Danmark, og deres forekomst,

herunder på Naturstyrelsens arealer (NST).

Art

Latinsk navn

(IUCN taxonomi)

Global

kategori

Dansk

rødlistekategori

Forekomst i DK

Kilde: Fugle & Natur +

rødlisten + DCE 2011

artsrapport

Holmegårds

damvandkalv

Agabus clypealis

Vandkalve er ikke

bedømt

Kun kendt 1970-81 fra

Holmegårds Mose,

Westfalerskær. Ikke NST.

Flodperlemusling

Margaritifera

margaritifera

Muslinger er ikke

bedømt

Kun kendt fra Varde Å. Senest

i 1995 iflg NST 2013. Ikke

NST.

Tykskallet

malermusling

Flad dammusling

Pseudanodonta

complanata

Bred vandkalv

Dytiscus latissimus

Unio crassus

Muslinger er ikke

bedømt

Muslinger er ikke

bedømt

Vandkalve er ikke

bedømt

Lys

skivevandkalv

Flodkrebs

Graphoderus

bilineatus

Astacus astacus

Vandkalve er ikke

bedømt

Krebsdyr er ikke

bedømt

Helt (inkl.

snæbel)

Coregonus maraena

= C. lavaretus

LC, livskraftig (VU

for snæbel)

Findes >>23 steder, heraf >>5

NST. Udbredt og stedvis talrig.

Talrig i diverse jyske åer og

fjorde (Snæbel kun i et par

sydvestjyske åer)

Findes >5 steder, heraf 3 NST

Findes >7 steder, heraf 6 NST

Fundet >4 steder, heraf 0 NST.

Fundet 15 steder, heraf 0 NST

Arterne i tabel 8 er fuldt akvatiske og derfor udenfor opgaveafgrænsningen, undtagen de tre

vandkalvearter, som er amfibiske. Af vandkalvene er det kun de nederste 2, som findes på

Naturstyrelsens arealer. De er begge på habitatdirektivets bilag 2 og håndteres derfor i forbindelse

med EU's 2020 mål.

Chapter 3

145

Den globale rødliste opdateres jævnligt. Artsudvalget fra 8/5 2015 lå til grund for dataindsamlingen

til nærværende projekt. Efter 8/5 2015 og frem til 23/8 2016 er 11 arter tilføjet jf bilag 11 (5 marine,

3 svampe og 3 fugle). De tre svampe og nordisk lappedykker var i forvejen med i projektet fordi de

er nationalt truede eller på EU-direktiv bilag. Det var taffeland og turteldue til gengæld ikke, så data

for dem er ikke indsamlet i projektet, og vil derfor heller ikke indgå i projektets kommende

analyser.

Konklusion vedr. globalt truede arter

Edderkoppen

Dolomedes plantarius

og de tre insekter

Ampedus hjorti, Phyllodesma ilicifolia

Formicoxenus nitidulus

i tabel 7 vil blive analyseret nærmere for at afklare, om de kan hjælpes på

Naturstyrelsens arealer. Derudover medtages de 3 svampe og nordisk lappedykker, som er tilføjet

som globalt sårbare siden maj 2015.

NATIONALT TRUEDE RØDLISTEARTER

Danmarks rødliste er senest opdateret i 2010, og næste opdatering forventes færdig i 2018 ifølge

bogen

"Danmarks truede arter. Den danske Rødliste"

fra den ansvarlige institution, Århus

Universitet, 2014. Bogen kaldes i det følgende for "Rødlistebogen 2014". Den beskriver og

resumerer data fra rødliste 2010, som i form af en database ligger på følgende hjemmeside:

http://bios.au.dk/videnudveksling/til-myndigheder-og-saerligt-interesserede/redlistframe/ .

Rødliste databasen har Naturstyrelsen fået tilsendt elektronisk fra DCE v/ Peter Wind d. 13. marts

2013. Databasen behandler 10.581 arter og har en række oplysninger om hver art. Der er dog ikke

en liste med konkrete fund af arterne. Den følgende gennemgang er gennemført på baggrund af

oplysningerne i den tilsendte fil, idet den er hurtigere at sortere og søge i, og ifølge DCE har

identisk indhold med net-versionen.

Der skelnes i rødliste terminologi mellem en række kategorier, og kun en delmængde heraf kaldes

for "rødlistede arter" = "rødlistearter", nemlig arter i kategorierne RE, CR, EN, VU, og NT

(forsvundet, kritisk truet, moderat truet, sårbar og næsten truet). Ved bedømmelse af arter til

146

Chapter 3

rødlisten bruges også kategorierne LC, DD, NA og NE (ikke truet, utilstrækkelige data, vurdering

ikke mulig og ikke bedømt), men de arter, som kommer i de kategorier kaldes ikke rødlistede arter.

De truede arter er en delmængde af rødlistearterne, og gælder kategorierne CR (kritisk truet), EN

(moderat truet) og VU (sårbar). Det er dem Aichi mål 12 handler om. I alt er der 1526 truede arter

på den danske rødliste, jf tabel 9 (summen af 369 CR, 496 EN og 661 VU). Desuden er kategorien

RE (regionalt uddød) relevant, i det omfang sådanne arter dukker op igen.

Tabel 9.

Oversigt over kategorier og arter i Danmarks hidtidige arbejde med rødlisten (fra

rødlistebogen 2014). Procenter for kategorierne sættes i visse kilder i forhold til bedømte arter og i

andre kilder til alle arter. Derfor vises her begge typer procenter.

Rødlistede

arter i alt

303

369

496

661

433

2262

28%

21%

5273

65%

50%

634

Bedømte

arter i alt

8169

100%

77%

2257

21%

155

Arter i

alt

10581

100%

Det hidtidige arbejde med rødlisten har forholdt sig til 10.581 af Danmarks ca 35.000 arter. De

arter, som ikke er behandlet er hovedsagelig artsgrupper med begrænset viden, herunder meget

begrænset viden om bestandsudviklingen over tid, som er essentiel for at kunne rødlistevurdere en

art. De fleste af de manglende er insekter (ca. 15.000 arter). Også mange svampe, samt alle mosser,

alger, bløddyr og en række andre dårligt kendte grupper mangler bedømmelse.

Aichi mål 12 handler derfor om at forbedre forholdene og forhindre uddøen for de 1526 truede

rødlistearter i tabel 9 (CR, EN og VU). For hver af arterne findes der i rødliste databasen

oplysninger om artens forekomst i tid og rum, samt om bl.a. levesteder og trusler.

I forbindelse med denne rapport er der taget udgangspunkt i rødliste databasens oplysninger, og

ikke indhentet supplerende oplysninger fra andre kilder. I PhD-projektet vil data fra en række andre

kilder, herunder internettet (især fugleognatur.dk, miljoeportal.dk m.fl.) og Naturstyrelsens egne

databaser blive anvendt til supplerende afklaring af, bl.a. hvor og hvornår arterne er fundet på

Naturstyrelsens arealer.

Chapter 3

147

For at kunne overskue de 1526 arter i forhold til Aichi målet er arterne indledningsvis ud fra

oplysningerne i rødliste databasen sorteret efter følgende forhold:



Om arten er i

tilbagegang

eller "blot" sjælden, hvilket fremgår af de kriterier, der har ført

til, at arten blev vurderet truet. Aichi målet fokuserer nemlig på arter med tilbagegang, især

dem med størst tilbagegang.



EU arter

herunder alle fugle er gennemgået og vurderet for sig i relation til EU's 2020 mål.

Marine arter

og andre fuldt akvatiske arter er jf. opgaveafgrænsningen frasorteret.

Tilbagegang eller ej

For hver art skal der ifølge manualen til rødlistning udfyldes hvilke kriterier, der begrunder

rødlistekategorien for arten. Kriterierne A, B1b, B2b, C1 og C2 bruges for arter, som vides eller

formodes at have været i eller kunne opleve fremtidig tilbagegang i bestand eller habitat (i større

eller mindre grad). Modsætningsvis bruges kriterie D om arter, som er så sjældne og fåtallige, at det

i sig selv gør dem truede, også selvom en aktuel tilbagegang ikke har kunnet påvises.

Rødliste databasen er ikke 100% konsistent udfyldt efter manualen, fx er kriterie ikke eller

mangelfuldt udfyldt for en række arter. I de tilfælde er teksten i databasens felter for "National

Status" og "Bestandsudvikling" brugt til at afgøre, om arten er i tilbagegang eller ej. Tilsvarende er

data for udvikling over tid ofte mangelfulde eller helt manglende.

Af Danmarks 1526 truede arter er 908 ifølge rødlistens kriterier eller tekst truet grundet

tilbagegang, mens resten (618 arter) ikke er noteret som truet af tilbagegang, men er så tilpas

sjældne og lokale, at de af den grund er vurderet truede.

Mange af de truede arter, som ikke er registreret med tilbagegang i rødlisten, har altid været meget

sjældne i Danmark eller har kun optrådt sporadisk eller uregelmæssigt. En del af dem er først fundet

i Danmark i de senere årtier og kan derfor være arter under indvandring. Det vil blive analyseret

nøjere i projektets videre faser, hvordan prioritering af arterne kan foretages.

EU arter

Alle fugle og en række andre arter håndteres af fuglebeskyttelsesdirektivet henholdsvis

habitatdirektivet. Muligheder for at hjælpe de arter på Naturstyrelsens arealer er gennemgået i

148

Chapter 3

kapitlet om EU's 2020 mål. Det gælder 60 af de 1526 truede arter, idet de både er rødlistet som

truede og omfattet af EU direktiverne. Heraf 30 med tilbagegang ifølge rødlistens oplysninger.

Marine arter, sø og å

Ifølge opgaveafgræsningen udelades fuldt akvatiske arter. Denne frasortering baseret på biotop

resulterer samlet i at 4 arter i tilbagegang udgår af analysearbejdet.

Konklusion vedr. nationalt truede arter

Der er 1526 truede arter på den danske rødliste (summen af 369 CR, 496 EN og 661 VU). Desuden

er kategorien RE (regionalt uddød) relevant, i det omfang sådanne arter dukker op igen.

Af de 1526 arter er der 874 ikke-akvatiske arter, som er vurderet til at være i tilbagegang, hvilket

ifølge Aichi mål 12 bør medføre, at de gives ekstra prioritet i forvaltningen. Det vil blive analyseret

nøjere i projektets videre forløb, hvordan oplysningerne om tilbagegang eller ej kan anvendes i

forbindelse med prioritering af indsatsen med særligt henblik på Naturstyrelsens arealer.

Hvordan der skal prioriteres mellem de identificerede nationalt, globalt og EU-truede arter og

tilknyttede naturtyper for at nå 2020-målene vil være et væsentligt emne i resten af PhD-projektet.

Chapter 3

149

BILAG

Bilag 1.

Invertebrater på habitatdirektivets bilag 2, 4 eller 5, som findes i Danmark. Ialt 15 arter. Kun hvis

status for arten hverken i 2007 eller 2013 er rapporteret ugunstig til EU angives "nej".

Kode

1013

1014

1016

1026

1034

1037

1042

1048

1058

1065

1076

1081

1082

1084

1936

Navn

Kildevældsvindelsnegl

Skæv vindelsnegl

Sumpvindelsnegl

Vinbjergsnegl

Lægeigle

Grøn kølleguldsmed

Stor kærguldsmed

Grøn mosaikguldsmed

Sortplettet blåfugl

Hedepletvinge

Natlyssværmer

Bred vandkalv

Lys skivevandkalv

Eremit

Stellas mosskorpion

Videnskabeligt navn

Vertigo geyeri

Vertigo angustior

Vertigo moulinsiana

Helix pomatia

Hirudo medicinalis

Ophiogomphus cecilia

Leucorrhinia pectoralis

Aeshna viridis

Maculinea arion

Euphydryas aurinia

Proserpinus proserpina

Dytiscus latissimus

Graphoderus bilineatus

Osmoderma eremita

Anthrenochernes stellae

Findes hos

Ugunstig? NST?

ugunstig måske

nej

ugunstig ja

nej

ugunstig ja

nej

150

Chapter 3

Bilag 2.

Padder og krybdyr på habitatdirektivets bilag 2, 4 eller 5, som findes i Danmark. I alt 12 arter. Kun

hvis status for arten hverken i 2007 eller 2013 er rapporteret ugunstig til EU angives "nej".

Kode

1166

1188

1197

1201

1202

1203

1209

1210

1212

1213

1214

1261

Navn

Stor vandsalamander

Klokkefrø

Løgfrø

Grønbroget tudse

Strandtudse

Løvfrø

Springfrø

Grøn frø

Latterfrø

Butsnudet frø

Spidssnudet frø

Markfirben

Findes hos

Videnskabeligt navn Ugunstig? NST?

Triturus cristatus

ugunstig ja

Bombina bombina

ugunstig ja

Pelobates fuscus

ugunstig ja

Bufo viridis

ugunstig ja

Bufo calamita

ugunstig ja

Hyla arborea

ugunstig ja

Rana dalmatina

nej

Rana esculenta

ugunstig ja

Rana ridibunda

ugunstig ja

Rana temporaria

nej

Rana arvalis

ugunstig ja

Lacerta agilis

ugunstig ja

Chapter 3

151

Bilag 3.

Pattedyr på habitatdirektivets bilag 2, 4 eller 5, som findes i Danmark. I alt 23 arter. Kun hvis

status for arten hverken i 2007 eller 2013 er rapporteret ugunstig til EU angives "nej".

Findes hos

NST?

næppe

måske

Kode

1308

5009

1309

1317

1312

1331

1330

1323

1314

1318

1320

1322

1327

1313

1326

1332

1341

1343

1355

1357

1358

1364

1365

Navn

Bredøret flagermus

Dværgflagermus

Pipistrelflagermus

Troldflagermus

Brunflagermus

Leislers flagermus

Skægflagermus

Bechsteins flagermus

Vandflagermus

Damflagermus

Brandts flagermus

Frynseflagermus

Sydflagermus

Nordflagermus

Langøret flagermus

Skimmelflagermus

Hasselmus

Birkemus

Odder

Skovmår

Ilder

Gråsæl

Spættet sæl

Videnskabeligt navn

Barbastella barbastellus

Pipistrellus pygmaeus

Pipistrellus pipistrellus

Pipistrellus nathusii

Nyctalus noctula

Nyctalus leisleri

Myotis mystacinus

Myotis bechsteinii

Myotis daubentonii

Myotis dasycneme

Myotis brandtii

Myotis nattereri

Eptesicus serotinus

Eptesicus nilssonii

Plecotus auritus

Vespertilio murinus

Muscardinus avellanarius

Sicista betulina

Lutra lutra

Martes martes

Mustela putorius

Halichoerus grypus

Phoca vitulina

Ugunstig?

ugunstig

nej

ugunstig

nej

ugunstig

nej

ugunstig

nej

152

Chapter 3

Bilag 4.

Planter på habitatdirektivets bilag 2, 4 eller 5, som findes i Danmark. I alt 13 arter. Kun hvis status

for arten hverken i 2007 eller 2013 er rapporteret ugunstig til EU angives "nej".

Findes hos

Ugunstig? NST?

ugunstig ja

ugunstig måske

nej

ugunstig ja

nej

ugunstig nej

ugunstig måske

nej

ugunstig ja

Kode

1378

1386

1393

1400

1409

1413

1419

1528

1614

1762

1831

1902

1903

Navn

Rensdyrlav

Grøn buxbaumia

Blank seglmos

Almindelig hvidmos

Tørvemos (alle arter)

Ulvefod (alle arter)

Enkelt månerude

Gul stenbræk

Krybende sumpskærm

Guldblomme

Vandranke

Fruesko

Mygblomst

Videnskabeligt navn

Cladonia spp. (subgenus Cladina)

Buxbaumia viridis

Drepanocladus vernicosus

Leucobryum glaucum

Sphagnum spp.

Lycopodium spp.

Botrychium simplex

Saxifraga hirculus

Apium repens

Arnica montana

Luronium natans

Cypripedium calceolus

Liparis loeselii

Chapter 3

153

Bilag 5.

Rent akvatiske arter, hvor indsatsen defineres i vand- og natura 2000 planerne. I alt 19 arter fra

habitatdirektivets bilag. Hvis status for arten hverken i 2007 eller 2013 er rapporteret ugunstig til

EU angives "nej".

Kode

1029

1032

1091

1095

1096

1099

1102

1103

1106

1109

1113

2492

2494

1145

1149

1351

2032

2618

1833

Navn

Flodperlemusling

Tykskallet Malermusling

Flodkrebs

Havlampret

Bæklampret

Flodlampret

Maj-sild

Stavsild

Laks

Stalling

Snæbel

Heltling

Helt

Dyndsmerling

Pigsmerling

Marsvin

Hvidnæse

Vågehval

Liden najade

Videnskabeligt navn

Margaritifera margaritifera

Unio crassus

Astacus astacus

Petromyzon marinus

Lampetra planeri

Lampetra fluviatilis

Alosa alosa

Alosa fallax

Salmo salar

Thymallus thymallus

Coregonus oxyrhynchus

Coregonus albula

Coregonus lavaretus

Misgurnus fossilis

Cobitis taenia

Phocoena phocoena

Lagenorhynchus albirostris

Balaenoptera acutorostrata

Najas flexilis

Ugunstig?

ugunstig

nej

ugunstig

nej

ugunstig

nej

ugunstig

nej

ugunstig

Levested

ferskvand

marin

ferskvand

marin

ferskvand

154

Chapter 3

Bilag 6.

Arter af fugle, som i 2013 blev rapporteret

i tilbagegang siden år 2000,

samtidig med at de

på EU-

niveau var ugunstige i 2004.

I alt 28 arter. Trend gælder bestandstal. "-" står for tilbagegang og "+"

for fremgang. Kilde:

(http://bd.eionet.europa.eu/activities/Reporting/Article_12/Reports_2013/Member_State_Deliveries).

Vendehals

Grønspætte

Grå Fluesnapper

Løvsanger

Sortterne

Taffeland

Dobbeltbekkasin

Sandterne

Brushane

Engryle (almindelig ryle)

Rødben

Urfugl

Stor Tornskade

Hvid Stork

Bynkefugl

Engpiber

Digesvale

Bysvale

Toplærke

Kirkeugle

Tårnfalk

Agerhøne

Vibe

Bomlærke

Tornirisk

Stær

Gulspurv

Sanglærke

Trend 1980-2011

stabile antal

Primær

biotop

skov

mose/sø/å

kyst

hede

græsland

diverse

agerland

Bilag 1-art?

Bestand

(mindst)

320

7.000

260.000

280

1.300

135

9.000

2.700

21.000

11.000

38.000

1.500

6.000

20.000

28.000

100.000

270.000

310.000

700.000

Chapter 3

155

Bilag 7.

Arter af fugle, som i 2013 blev rapporteret

i tilbagegang siden år 2000,

men som

ikke blev vurderet

ugunstige i 2004 på EU-niveau.

I alt 36 arter. Kilde og forkortelser som i bilag 6.

Trend 1980-2011

stabile antal

fluktuerende

stabile antal

fluktuerende

stabile antal

Biotop

skov

mose/sø/å

kyst

hede

græsland

diverse

agerland

Bilag 1-art?

Bestand (mindst)

270

2.100

4.700

7.500

8.000

9.500

15.000

29.000

1.100

2.000

3.600

6.800

34.000

340

420

2.400

4.500

7.000

6.000

500

1.500

4.200

15.000

25.189

50.000

80.000

130.000

23.000

Duehøg

Spurvehøg

Halemejse

Gulbug

Broget Fluesnapper

Kærnebider

Dompap

Misteldrossel

Topmejse

Fuglekonge

Vandstær

Pungmejse

Vandrikse

Sivsanger

Knopsvane

Grønbenet Rørhøne

Blishøne

Rørsanger

Karmindompap

Ride

Fjordterne

Klyde

Havterne

Strandskade

Gråsisken

Sortstrubet Bynkefugl

Husrødstjert

Sjagger

Gravand

Fiskehejre

Mursejler

Skarv

Jernspurv

Hættemåge

Gærdesmutte

Stillits

156

Chapter 3

Bilag 8.

De globalt truede marine arter med yngleforekomst i Danmark.

Art

Latinsk navn

Global

kategori

Dansk rødlistekategori Forekomst i DK

Skade

Torsk

Kuller

Gråhaj

Sildehaj

Pighaj

Dipturus batis

Gadus morhua

Melanogrammus aeglefinus

Galeorhinus galeus

Lamna nasus

Squalus acanthias

Havfisk ikke bedømt

Marin, ikke NST.

Chapter 3

157

Bilag 9.

De globalt truede arter med gæsteoptræden i Danmark uden ynglen.

Art

Latinsk navn

Global

kategori

Dansk

rødlistekategori

Forekomst i DK

Ål

Anguilla anguilla

Talrig, udbredt, men på

retur.

Alm. stør

Acipenser sturio

NA, dvs ikke

dansk

Marin, ikke forsøgt

detaljeret her.

Marin, ikke forsøgt

detaljeret her.

Marin, ikke forsøgt

detaljeret her.

Marin, ikke forsøgt

detaljeret her.

Marin, ikke forsøgt

detaljeret her.

Marin, ikke forsøgt

detaljeret her.

Marin, ikke forsøgt

detaljeret her.

Marin, ikke forsøgt

detaljeret her.

Marin, ikke forsøgt

detaljeret her.

Marin, ikke forsøgt

detaljeret her.

Gæst, ikke forsøgt detaljeret

her.

Havengel

Squatina squatina

Havfisk er ikke

bedømt

Helleflynder

Hippoglossus

hippoglossus

Havfisk er ikke

bedømt

Atlantisk tun

Thunnus thynnus

Havfisk er ikke

bedømt

Finhval

Balaenoptera physalus

Anset for

strejfgæst

Kaskelot

Physeter macrocephalus

Anset for

strejfgæst

Brugde

Cetorhinus maximus

Havfisk er ikke

bedømt

Hammerhaj

Sphyrna zygaena

Havfisk er ikke

bedømt

Fløjlsand

Melanitta fusca

Trækgæster

ikke bedømt

Havlit

Clangula hyemalis

Trækgæster

ikke bedømt

Dværggås

Anser erythropus

Trækgæster

ikke bedømt

158

Chapter 3

Bilag 10.

Globalt truede arter som faktisk ikke eller kun tilfældigt optræder i Danmark.

Art

Latinsk navn

Global

kategori

Guitarfisk

Rhinobatos rhinobatos

Dansk

rødlistekategori

Havfisk er ikke

bedømt

En hybrid

birk

Betula oycoviensis

(= B. pendula x

szaferi)

flodperle-

musling

Margaritifera

auricularia

Muslinger er ikke

bedømt

Aldrig registreret i

Danmark.

Fejlangivet fra Danmark af

IUCN.

En art

træbuk

Rosalia alpina

Ikke medtaget

Kun et tilfældigt indslæbt

fund.

Ikke medtaget

Marin, ikke forsøgt

detaljeret her.

Kunstig hybrid opstået i/ved

botaniske haver.

Forekomst i DK

Bilag 11.

Ekstra globalt truede arter listet 9/5 2015 – 23/8 2016, og som iflg IUCN optræder i Danmark.

Art

Stor ridderhat

Grøngul vokshat

Jensens vokshat

Taffeland

Turteldue

Latinsk navn

Tricholoma acerbum

Hygrocybe citrinovirens

Hygrocybe ingrata

Aythya ferina

Streptopelia turtur

Global kategori Dansk rødlistekategori

Trækgæster ikke bedømt

Havfisk ikke bedømt

Nordisk lappedykker

Podiceps auritus

Lunde

Skolæst

En art rokke

Klumpfisk

Hestemakrel

Fratercula arctica

Coryphaenoides rupestris

Leucoraja fullonica

Mola mola

Trachurus trachurus

CHAPTER 4

Muligheder på Naturstyrelsens arealer for

bedre opfyldelse af

2020-mål for truede arter

Erik Buchwald & Jacob Heilmann-Clausen

(February 2018)

160

Cetonia aurata (photo by Erik Buchwald)

Chapter 4

161

UNIVERSITY OF COPENHAGEN

NATURAL HISTORY MUSEUM OF DENMARK

CENTER FOR MACROECOLOGY, EVOLUTION AND CLIMATE

Muligheder på Naturstyrelsens arealer for

bedre opfyldelse af 2020-mål

for truede arter.

Erik Buchwald

Jacob Heilmann-Clausen

(Title)

(Subtitle)

Februar 2018

162

Chapter 4

Titel

Muligheder på Naturstyrelsens arealer for bedre opfyldelse af 2020-mål for

truede arter.

Rapporten er udarbejdet som led i Erhvervs-PhD-projektet: "Analyse og priori-

tering af fremtidig indsats for biodiversitet - med særligt henblik på Naturstyrel-

sens arealer". Projektet løb 2015-18 og havde til formål at undersøge og analy-

sere, hvor og hvordan man i Danmark kan gøre en effektiv indsats for at hindre

tab af biodiversitet – særligt på de ca. 2.000 kvadratkilometer af Danmark, som

forvaltes af Naturstyrelsen.

Erik Buchwald, Erhvervs-PhD studerende og

Jacob Heilmann-Clausen, Lektor, vejleder

14. februar 2018

Center for Makroøkologi, Evolution og Klima (CMEC)

Statens Naturhistoriske Museum, Københavns Universitet

Universitetsparken 15, 2100-København Ø.

Rapporten kan downloades: http://macroecology.ku.dk/dk/andre-publikationer/

Lotte Nymark Busch Jensen (forsidedesign) og Erik Buchwald.

Kontekst

Forfattere

Dato

Sted

Layout

Forsidefoto

Sortspætte, kejserkåbe på kærtidsel, flammeporesvamp og væltet gammel bøg

(fotos: Lassi Rautiainen, Erik Buchwald, Jacob Heilmann-Clausen og Erik

Buchwald).

Sideantal

TAK

67 sider plus bilag.

Stor tak for tilladelse til brug af data til Dansk Ornitologisk Forening, Lepidop-

terologisk Forening, Danmarks SvampeAtlas, Dansk Botanisk Forening, Fynske

Insekter og Danmarks Edderkopper, Statens Naturhistoriske Museum og Natur-

styrelsen, samt til Fugle & Natur (licens B05/2014). De mange frivillige takkes

for deres enorme registreringsindsats.

Artseksperterne Jan Pedersen, Nikolaj Scharff og Ulrik Søchting ved Statens

Naturhistoriske Museum takkes for hjælp til at finde og kvalitetssikre data. Car-

sten Rahbek, Bjørn Hermansen, Anders Højgaard Petersen, Louis A. Hansen

and Lotte Jensen ved CMEC takkes for hjælp og råd i projektet. Tak også til

Hugh Possingham og Matthew Watts for at stille Marxan programmerne til rå-

dighed. Danmarks Grundforskningsfond takkes for finansiering til CMEC

(DNRF96). Naturstyrelsen og Innovationsfonden (Erhvervs-PhD bevilling nr.

4135-00145B) takkes for finansieringen af PhD-arbejdet.

Chapter 4

163

Indhold

1.1

INDLEDNING ............................................................................................................... 5

Opgave afgrænsning -

2020-måls-arter undtagen de fuldt akvatiske

.................................................................... 5

2.1

2.2

2.3

2.4

2.5

2.6

2.7

BAGGRUND ................................................................................................................ 6

2020-mål for arter.................................................................................................................................................... 6

Beskyttelse og drift på Naturstyrelsens arealer .................................................................................................... 6

Formål og spørgsmål i Erhvervs-PhD projektet ................................................................................................... 7

Regeringens Naturpakke 2016 ............................................................................................................................... 8

Arternes økologiske behov ...................................................................................................................................... 9

Optimering af omkostningseffektivitet og forvaltning ....................................................................................... 12

Systematic conservation planning - SCP ............................................................................................................. 13

3.1

3.2

3.3

3.4

DATA ......................................................................................................................... 15

Areal- og økonomidata .......................................................................................................................................... 15

Artsoplysninger og -forekomster ......................................................................................................................... 15

Prioritering af arter fra 1 til 5, henholdsvis A og B ............................................................................................ 16

Habitatpræferencer og trusler.............................................................................................................................. 16

4.1

4.2

4.3

4.4

METODE .................................................................................................................... 18

Zoner og beskyttelsestyper for skovarter ............................................................................................................ 18

Optimering for skovarter med programmet

Marxan with zones

....................................................................... 20

Yderligere optimering ........................................................................................................................................... 23

Ikke-skovarter ....................................................................................................................................................... 24

5.1

5.2

5.3

5.4

5.5

RESULTATER OG DISKUSSION ............................................................................. 26

Oversigt over arternes status og prioritet............................................................................................................ 26

Arternes præferencer ............................................................................................................................................ 29

Marxan-optimering for skovarter ........................................................................................................................ 30

Yderligere optimering ved opdeling af skove ...................................................................................................... 37

Behov for ekstra beskyttelse af ikke-skovarter ................................................................................................... 40

FEJLKILDER OG USIKKERHEDER ......................................................................... 42

ANBEFALINGER OM INDSATSEN I SKOV ............................................................. 47

164

Chapter 4

7.1 Indsats i overgangsperioden og i anden biodiversitetsskov ................................................................................... 48

7.2 Urørt skov efter overgangsperioden ........................................................................................................................ 51

8. ANBEFALEDE INDSATSER FOR LYSÅBNE BIOTOPERS

ARTER .............................................................................................................................. 52

8.1

8.2

8.3

8.4

8.5

8.6

Supplerende oplysninger ....................................................................................................................................... 52

Justeret drift og forvaltning .................................................................................................................................. 52

Græsning ................................................................................................................................................................ 53

Høslæt ..................................................................................................................................................................... 54

Andre indsatser ...................................................................................................................................................... 55

Økonomi ................................................................................................................................................................. 56

9.1

9.2

KONKLUSION............................................................................................................ 58

Skove og skovarter ................................................................................................................................................. 58

Lysåbne biotoper og deres arter ........................................................................................................................... 59

10.

REFERENCER ....................................................................................................... 60

BILAG:

Bilag A.

Undersøgelsens datakilder og referencer til disse. Kun data for 2020-måls-arterne og fra

perioden 1991-2015 blev brugt.

Bilag B. Data for 626 skovarter.

Detaljer om status, præferencer og zoner for de 626 skovarter.

Bilag C. A priori urørt beskyttede skove.

De syv skove låst til zone 4 (Urørt) i scenarie O-Z.

Bilag D. Zone 4 skove.

Detaljer for 20 skove, som alle scenarier henførte til zone 4 (Urørt).

Bilag E. Zone 3 skove.

Detaljer for 55 skove, som alle scenarier henførte til zone 3 (Aktiv).

Bilag F. Variable skove.

Detaljer for 220 skove, som variabelt blev henført til forskellige zoner i de

forskellige scenarier.

Bilag G. Zone 1 skove.

Detaljer for 681 skove, som alle scenarier henførte til zone 1 (Normal).

Bilag H. Resultater af scenarier.

Detaljer for hvert scenarie.

Bilag I. Gap-arter for scenarie Q.

Detaljer for 73 arter, som ikke nåede den målsatte beskyttelse i

scenarie Q.

Bilag J. Skove til underopdeling.

Detaljer for 48 skove, som blev gennemgået manuelt for underop-

deling.

Bilag K. Zoneforskel mellem scenarie Q, Q1 og Q2.

Detaljer for 146 skove.

Bilag L. Gap-arter i scenarie Q og Q1.

Detaljer for 155 arter.

Bilag M. Oplagte indsatser vedr. ikke-skovarter.

De 370 prioriterede sted-art kombinationer sorteret

efter Naturstyrelsens enheder og skovnumre, og med oplysning om hver arts biotop, økologiske behov

og kendte trusler.

Bilag N. De 33 højst prioriterede ikke-skovarter.

Det gælder arter i prioritetsklasse 1 og 2.

Chapter 4

165

1. Indledning

Denne rapport over muligheder for bedre opfyldelse af 2020-mål for truede arter på Natursty-

relsens arealer er udarbejdet som led i Erhvervs-PhD-projektet: "Analyse og prioritering af

fremtidig indsats for biodiversitet - med særligt henblik på Naturstyrelsens arealer". Projektet

løber 2015-18 og har til formål at undersøge og analysere, hvor og hvordan man i Danmark

kan gøre en effektiv indsats for at hindre tab af biodiversitet – særligt i den del af Danmark,

som forvaltes af Naturstyrelsen (NST). Rapporten redegør bl.a. for metoderne brugt til at ana-

lysere og prioritere mulighederne på Naturstyrelsens arealer, med hensyn til hvor indsats kan

hjælpe de truede arter.

I projektets første rapport fra september 2016 "Identifikation af arter og naturtyper i 2020 bio-

diversitets målene" (Buchwald & Heilmann-Clausen 2016) er de politiske mål i Biodiversi-

tets-Konventionen og afledte regionale og nationale strategier gennemgået. Målene omhand-

ler bl.a. indsats for truede arter, så deres udryddelse forebygges. Truede arter udgøres af arter i

kategorierne CR (kritisk truet), EN (moderat truet) og VU (sårbar) i globale eller regionale

rødlister. Desuden er kategorien RE (regionalt uddød) relevant, i det omfang sådanne arter

dukker op igen.

Projektet har især for Naturstyrelsens arealer analyseret og prioriteret de truede arter og deres

konkrete levesteder i forhold til viden om bl.a. arternes økologiske behov og hidtidige udvik-

ling, samt relation til hidtidig drift og beskyttelse. Prioriteringen var på arter, som er af særlig

betydning for, at Danmark kan nå det politisk vedtagne 2020-mål om at standse tabet i biodi-

versitet. De højst prioriterede steder listes i bilag til denne rapport, mens lavere prioriteter

fremgår af GIS-database leveret elektronisk til Naturstyrelsen.

Projektets primære resultater er i form af en række GIS- og excel-filer afleveret og gennem-

gået for Naturstyrelsen primo november 2017, for at resultaterne kunne nå at blive anvendt til

Naturstyrelsens arbejde med at foreslå områder til udmøntning af Regeringens Naturpakke.

1.1

Opgave afgrænsning

2020-måls-arter undtagen de fuldt akvatiske

Opgaven har fokus på arter, som er truede globalt, nationalt eller på Natura 2000 direktivernes

bilag (ynglefugle bilag 1; habitat bilag 2, 4 og 5). Erhvervs-PhD-projektet og dermed denne

rapport ser dog kun på de mere eller mindre terrestriske arter og naturtyper. Dvs. fuldt akvati-

ske arter i hav, sø og å er udeladt. Akvatiske arter, som tidvis er på land (amfibiske arter)

medtages, såfremt en eller flere arter i familien er medtaget på habitatdirektivets bilag. Derfor

er fx fisk, hvaler, slørvinger og døgnfluer ikke behandlet, mens bl.a. sæler, frøer, salamandre,

vandkalve og guldsmede behandles. Samlet set kan man kalde de således afgrænsede arter for

2020-måls-arter,

idet de altså er afledt af de internationale politiske mål for biodiversitet for

året 2020.

166

Chapter 4

2. Baggrund

2.1

2020-mål for arter

Danmark har politisk forpligtet sig til at arbejde for at standse tilbagegangen i biodiversitet

inden 2020, dels i FN regi, dels i EU regi.

Med hensyn til arter er et af målene i

FN regi

at undgå uddøen af truede arter (dvs. rødliste

kategorierne CR kritisk truet, EN moderat truet og VU sårbar) inden 2020 og at forbedre ar-

ternes status, især for arter i størst tilbagegang: "By 2020, the extinction of known threatened

species has been prevented and their conservation status, particularly of those most in decline,

has been improved and sustained" (Aichi mål 12 besluttet i Nagoya i 2010).

EU regi

indeholder biodiversitetsstrategien fra 2011 bl.a. mål om, at der i 2020 skal være 50

% flere arter og fugle omfattet af habitat- og fugledirektivernes rapporteringspligt, som har

god eller forbedret status sammenlignet med rapporteringerne fra 2004 for fugle henholdsvis

2007 for andre arter.

Samlet set er det således arter, som er truede globalt, nationalt eller listet på EU-natur-

direktivernes bilag, som spiller direkte ind på de politiske 2020-mål for biodiversitet. Det er

derfor de arter, der er taget udgangspunkt i i denne rapport, og derfor de omtales som 2020-

måls-arter.

2.2

Beskyttelse og drift på Naturstyrelsens arealer

Naturstyrelsen har siden 1989 gennemført en betydelig omstilling af driften af statsskovene,

så hensynet til biodiversitet er blevet kraftigt opprioriteret (Rigsrevisionen 2016). Alle arealer

er således underlagt en række generelle beskyttelsesbestemmelser og "økologiske retningslin-

jer" til gavn for dyr, svampe og planter, så der bl.a.



alle steder skal udvikles og bevares gamle træer ("Livstræer"), som skal stå til højst mulig

alder og efterfølgende naturlig død og forfald, foruden generel øgning af mængden af dødt

ved,



generelt arbejdes på at genskabe naturlig hydrologi, i det omfang lovgivning mv tillader

det,

ikke fældes hule træer eller meget gamle træer (bøg > 200 år, eg > 300 år),

ikke bruges pesticider eller gødskning,

med naturnær skovdrift arbejdes i retning af blandede bevoksninger med flere aldre og

øget brug af hjemmehørende arter.

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167

Statsskovene er også certificeret efter både "Forest Stewardship Council" (FSC) og "Pro-

gramme for the Endorsement of Forest Certification" (PEFC). Sammen med en række andre

retningslinjer og indsatser også på Naturstyrelsens lysåbne biotoper medfører det, at beskyt-

telsesniveauet for arter på alle styrelsens arealer er hævet betydeligt i forhold til de krav, som

stilles til andre ejere. Årsagen til det hævede niveau ligger i Naturstyrelsens formål og i skov-

lovens særlige bestemmelser om, hvordan statens skove skal forvaltes (Rigsrevisionen 2016).

Ikke desto mindre er der en række arter af dyr, svampe og planter, som har så specifikke krav

for at trives, at man ikke kan være sikker på deres langsigtede overlevelse uden yderligere

tiltag. Derfor er der især siden 1992 udlagt over 8.000 hektar til urørt skov, græsningsskov og

stævningsskov, samt 4.500 ha med plukhugst på Naturstyrelsens arealer (Rigsrevisionen

2016).

2.3

Formål og spørgsmål i Erhvervs-PhD projektet

Projektet "Analyse og prioritering af fremtidig indsats for biodiversitet - med særligt henblik

på Naturstyrelsens arealer" 2015-2018 har haft til formål at undersøge og analysere, hvor og

hvordan man i Danmark kan gøre en effektiv indsats for at hindre tab af biodiversitet – særligt

på de ca. 2.000 kvadratkilometer af Danmark, som ejes af Miljøministeriet v/ Naturstyrelsen.

Det er veldokumenteret forskningsmæssigt, at biodiversiteten med dens mangfoldighed af

planter, dyr og andre organismer samt økosystemer både globalt og i Danmark er i tilbage-

gang, og at mange arter er truet af udryddelse (Ejrnæs et al. 2011, Johanssen et al. 2013, Mace

et al. 2008, Petersen et al. 2012, Wind & Ejrnæs 2014). Den hidtidige beskyttelse af natur

med høj kvalitet har derfor ikke været tilstrækkelig. I et stærkt udnyttet landskab som det dan-

ske spiller forsinket uddøen ("extinction debt") desuden en vigtig rolle, og betinger at der er

behov for en udvidelse af det beskyttede naturareal, hvis yderligere tab af arter skal undgås.

Politisk har de fleste af verdens lande, herunder som nævnt Danmark, ved aftaler under Bio-

diversitets-Konventionen forpligtet sig til at standse tilbagegangen i biodiversitet, herunder

med specificerede mål for år 2020 både på EU-niveau og globalt i FN-regi. PhD-projektet

fokuserer jf. 2020-målene på de truede arter. Truede arter listes i såkaldte rødlister fordelt på

tre kategorier afhængigt af, hvor truede de vurderes at være (Wind & Ejrnæs 2014).

For at kunne hjælpe de truede arter forvaltningsmæssigt, er det nødvendigt at kende deres

krav til levested, og hvilke trusler, der er vigtige for dem. Oplysninger herom er af Århus

Universitet samlet i en rødliste-database, hvis data dog er heterogene, ufuldstændige og af

vekslende faglig kvalitet (Wind & Pihl 2010). Derudover findes en væsentlig detail litteratur

om en mindre del af arterne og om metoder til analyse og prioritering (Ejrnæs et al. 2011,

Fordham et al. 2013, Geldmann et al. 2013, Jackson & Hobbs 2009, Mascia et al. 2014, Wind

& Ejrnæs 2014).

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Chapter 4

Der er i 2011-2013 udført overordnede analyser af den hidtidige status og indsats for dansk

biodiversitet, som bl.a. peger på behov for en tilgang til forvaltning og beskyttelse, som er

mere målrettet de truede arter, men også at skovene er af afgørende betydning for en meget

stor andel af de truede arter (Ejrnæs et al. 2011, Johanssen et al. 2013).

Hvilke drifts- og naturplejeformer, der anses for mest omkostningseffektive og hensigtsmæs-

sige, er samfunds- og forskningsmæssigt omdiskuteret i disse år, men for de enkelte truede

arter og artsgrupper kan der som regel udledes relevante oplysninger om deres økologiske

behov (Fowler et al. 2014, Kuuluvainen 2009, Nielsen 2009, Sandom et al. 2014, Stokland et

al. 2012).

De Økonomiske Råd har i relation til bevarelse af den danske biodiversitet konkluderet, at en

stor indsats i skovene bør prioriteres højt, og at der er brug for mere målrettet indsats i den

åbne natur (Petersen et al. 2012). Rådets analyser var i høj grad baseret på data for Danmark

opdelt i 10 x 10 km kvadrater. For at gøre det mere målrettet og forvaltningsegnet er der be-

hov for at komme længere ned i skala til konkrete naturarealer, herunder skove.

Erhvervs-PhD-projektet har derfor for Naturstyrelsens arealer gået dybere end de ovennævnte

overordnede analyser og vurderinger ved at analysere og prioritere de truede arter og deres

konkrete levesteder i forhold til viden om arternes økologiske behov, udbredelse, relation til

driftsformer, samt hidtidige udvikling i Danmark. Fokus er på arter, som ser ud til at være af

særlig betydning for, om Danmark kan nå det politisk vedtagne 2020-mål om at standse tabet

i biodiversitet.

2.4

Regeringens Naturpakke 2016

Ca. et år efter PhD-projektets start lancerede Regeringen d. 20. maj 2016 "Aftale om Natur-

pakke", som bl.a. fastslog at "aftaleparterne prioriterer at standse tilbagegangen i biodiversi-

tet", at der på Naturstyrelsens arealer skulle udlægges "i alt 13.300 nye ha skov til biodiversi-

tetsformål", og at en "stor effekt i forhold til at fremme forholdene for en lang række truede

arter" var en del af formålet.

Udlægningen skulle ske på den mest omkostningseffektive måde og ved inddragelse af nyeste

viden samt konsultation af relevante forskningsmiljøer. De 13.300 ha skulle fordeles med



6.700 ha urørt skov i løvskove



3.300 ha urørt skov i nåletræsplantager

3.300 ha anden biodiversitetsskov, primært i løvskove

Løvskove

nåletræsplantager

henviser til om skoven ligger i eller udenfor de gamle skov-

egne i det østlige Danmark.

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169

I lyset af Naturpakken blev PhD-projektets fokus justeret, så det kunne bidrage bedst muligt

til en videnbaseret og omkostningseffektiv udpegning af relevante skovarealer. Skovlevende

arter fik derfor endnu større fokus i projektet.

2.5

Arternes økologiske behov

For at kunne beskytte 2020-måls-arterne så godt som muligt har PhD-projektet gennemgået

alle arterne for at identificere deres overordnede økologiske behov. Behovene er derpå sat i

relation til Naturpakkens arealkategorier og forsøgt vurderet i forhold til, hvad arterne behø-

ver oveni Naturstyrelsens normale generelle beskyttelsesniveau.

Generelt er arter evolutionært udviklet igennem så mange årtusinder til årmillioner, at deres

økologiske behov og andre tilpasninger vil være sket i landskaber med ingen eller meget lille

indflydelse fra mennesker.

I princippet kan alle arter derfor klare sig i et landskab uden påvirkning fra mennesker, og

man kunne forestille sig, at alle de truede skovarter, der har overlevet i Danmark, kan trives i

skov, som man udlægger urørt. Der er dog en række årsager til, at dette ikke altid er tilfældet:

1. Arter der findes i det nordvestlige Europa, herunder Danmark, er oprindelig udviklet i

ufragmenterede naturlige landskaber med mange store græssende dyr, som væsentligt på-

virkede landskab og vegetation (Andersson et al. 1990, Dirzo 2014, Nielsen 2009, Owen-

Smith 1992, Vera 2000), hvilket afviger stærkt fra nutidens forhold.

2. Danmarks og andre nordvesteuropæiske skovlandskaber har dramatisk ændret karakter

gennem de sidste 200 år fra en overudnyttet unaturligt næringsfattig tilstand med relativt

åbne græsningsskove (Odgaard 1994, Timmermann et al. 2015, Tybirk et al. 1999, Van-

dekerkhove 2012, Vaupell 1863) til en luftforureningsbetinget unaturligt nærings- og

-beriget tilstand, som regel uden græsning. Det fører til unaturligt høj, tæt og frodig

vegetation (Kahle 2008, Timmermann et al. 2015, Tybirk et al. 1999).

3. De danske skove er ensartede og unge som følge af den hidtidige dyrkning med dominans

af ensaldrende bevoksninger med kun én træart og en del ikke-hjemmehørende træarter

(Feilberg 2004, Nord-Larsen et al. 2016). Udlæg af sådanne skove til urørt skov vil ikke

medføre urskovsagtige tilstande før efter flere hundrede år. Navnlig er der risiko for en

lang periode med tætte og mørke bevoksningsstrukturer og dermed tab af lys- og varme-

krævende arter.

4. Nogle arter har deres klimatiske udbredelsesgrænse i Danmark og risikerer alene som føl-

ge af klimaændringer at forsvinde. (Huntley et al. 2007, Settele et al. 2008, Skov et al.

2006, Svenning et al. 2008, Virkkala 2013).

5. En række andre trusler fra fx isolation/fragmentering, invasive arter og forsinket uddøen

kan betyde et tab af isolerede bestande af truede arter, hvis der ikke sættes ind med aktive

målrettede plejetiltag (Geldmann et al. 2014, Joppa 2016, Timmermann et al. 2015, Wind

& Pihl 2010).

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Chapter 4

Mange arter er dermed blevet rødlistet som truede på grund af tilbagegang i de tidligere næ-

ringsfattige åbne biotoper eller som følge af, at deres levesteder, fx gamle solbeskinnede ege-

træer, er blevet overskygget af høj skyggende tilgroning (Timmermann et al. 2015, Wind &

Pihl 2010).

Det er ikke givet, at de arter, som er defineret som "skovarter", reelt er så bundet til skov, at

de kan trives i moderne urørt skov. Definitionen af skovarter er nemlig bred og inkluderer

arter primært knyttet til lysåbne biotoper, men også tilstede i skovlysninger, fx en række dag-

sommerfugle (Petersen et al. 2016b).

Der er forskellige bud på, hvordan naturforvaltningen overordnet kan gribes an for at håndtere

disse problemstillinger. Traditionelt har meget naturforvaltning taget udgangspunkt i et statisk

eller historisk begrundet natursyn, hvor målet er at bevare nuværende værdier, typisk med

stærkt fokus på hjemmehørende arter og historiske naturtyper, inklusiv dem, som er blevet

fremmet i et historisk kulturlandskab (fx hø-enge, stævningsskov mv.). Dette natursyn gen-

nemsyrer megen lovgivning på området, og anes tydeligt i fx habitatdirektivets fokus på at

fastholde bestemte veldefinerede naturtyper, mens tab af truede arter ses som en forvalt-

ningsmæssig fiasko.

I modsætning hertil står et dynamisk natursyn, som anerkender, at naturen er under konstant

forandring som konsekvens af naturlige (fx forsinket spredning af arter efter istiden) og men-

neskeskabte processer (fx klimaændringer, eutrofiering, flytning af arter), og at det derfor ofte

er både dyrt og uhensigtsmæssigt at arbejde for at forvalte naturen statisk. I stedet sætter den

dynamiske naturforvaltning fokus på behovet for plads til naturen og eventuelt for genopret-

ning af de naturlige processer, som mennesket har afbrudt, men som har haft afgørende be-

tydning for arternes evolution og indbyrdes konkurrence over mange årtusinder. I dette per-

spektiv bliver de enkelte arter i højere grad set som indikatorer for de aktuelle biologiske pro-

cesser, end som mål i sig selv, og nyindvandrede arter vil indgå på lige fod med hjemmehø-

rende arter i evalueringen af succes i naturforvaltningsprojekter.

I nærværende arbejde har vi valgt en pragmatisk tilgang til problemstillingen, men med ud-

gangspunkt i naturens uomtvisteligt dynamiske karakter og med respekt for at der er et stærkt

artsfokus i de politiske 2020-mål for biodiversitet. Med dette udgangspunkt kan vi konstatere,

at alle truede skovarter i Danmark på kort sigt med stor sandsynlighed ikke kan få deres øko-

logiske behov opfyldt i urørt skov, og at der derfor er behov også for mere aktive tiltag som

en slags nødløsning for lokalt at forsøge at kompensere for de overordnede negative menne-

skeskabte forandringer, som skader arternes muligheder for overlevelse.

Urørt skov er ifølge den videnskabelige litteratur et af de vigtigste redskaber til at gavne man-

ge skovarter, men bevaring af arterne kan også kræve behov for aktiv indsats (fx Bernes et al.

2015, Boch et al. 2013, Brunet et al. 2010, Flensted et al. 2016, Lindenmayer & Franklin

2002, Lindhe et al. 2005, Lundström et al. 2016, Peterken 1996, Vera 2000)

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171

Afgrænsningen mellem skovarter og ikke-skovarter er i denne sammenhæng central. Mange

arter trives nemlig bedst i gradvise overgange mellem forskellige biotoper, "ecotones" (Risser

et al. 1995), fx mellem åbent land og skov som er naturlige på vores breddegrader, både som

følge af gradienter mod kyst, mose og særligt næringsfattig jordbund, og som konsekvens af

naturlige forstyrrelser fra stormfald, ild og græsning. Den slags overgange er blevet unaturligt

sjældne i det moderne landskab - i høj grad en konsekvens af fredskovsforordningen fra 1805,

som skarpt og kunstigt opdelte Danmark i skov og ikke-skov, hvor skov havde fokus på tøm-

merproduktion, mens skovgræsning blev ulovliggjort. Den danske naturforvaltning og termi-

nologi har haft og har stadig svært ved at komme ud over denne unaturlige og skarpe opdeling

mellem skov og ikke-skov. Det har desværre også i nærværende projekt vist sig umuligt at

undgå en sådan opdeling.

Derudover er der et skisma i forhold til truede arter knyttet til ikke-hjemmehørende arter, samt

hvordan disse arters økologiske behov håndteres. Det er fx ikke entydigt, om rødgran må an-

ses for hjemmehørende i Danmark og dermed om arter afhængige af rødgran anses for hjem-

mehørende (Buchwald et al. 2013, Feilberg 2004, Hartvig 2015, Skipper 2017). I denne sam-

menhæng har vi for at sikre konsistens til den danske rødliste (Wind & Pihl 2010) valgt en

biogeografisk i stedet for lande-politisk tilgang, således at truede arter hjemmehørende i en af

de to biogeografiske regioner, som Danmark tilhører (den Atlantiske og den Kontinentale)

medtages, uanset om de anses for hjemmehørende i Danmark eller ej.

Problemstillingen er særlig relevant i forhold til arter med behov for nåletræer eller nåleskov.

Nåleskovene er hovedsagelig plantede bestande og ofte med fremmede træarter fra andre kon-

tinenter (Feilberg 2004, Nord-Larsen et al. 2016). Skovfyr er eneste hjemmehørende træart

(Feilberg 2004), hvis man ser bort fra rødgranen og de mere buskagtige ene og taks. Der eksi-

sterede ingen nåleskov i Danmark i lang tid frem til introduktionen af rødgran til skovbruget

omkring 1763, fordi de sidste naturlige individer af skovfyr blev fældet og dermed udryddet

nogen tid før år 1750. Skove af skovfyr forsvandt endnu tidligere (Feilberg 2004). Udviklin-

gen har siden 1800-tallet gået stærkt den anden vej, og skovfyr dækker nu ca. 5 % af det dan-

ske skovareal og rødgran omkring 50 % (Nord-Larsen et al. 2016). Obligate nåleskovsarter

må derfor have været forsvundet helt eller delvis omkring år 1750, men en del arter er siden-

hen genindvandret eller har bredt sig, hvilket bl.a. er veldokumenteret for fugle (Dinesen et al.

2016).

Den manglende skovtypekontinuitet og en ofte intensiv drift betyder dog, at de danske nåle-

skove rent biodiversitetsmæssigt er en fattig afglans af de naturlige nåleskove, der kan ses i

vores nabolande (Feilberg 2004). Nåleskovenes truede arter er ofte tilknyttet lysningerne eller

åben fyrreskov og pionersamfund på meget næringsfattig sandbund (Flensted et al. 2016, Pe-

tersen et al. 2018). Nåleskov er blevet prioriteret relativt højt i den hidtidige skovindsats.

28 % af de hidtidigt udlagte urørte skove er således nåleskov (Johannsen et al. 2013) og i re-

geringens naturpakke er 33 % af det planlagte urørte skovareal henlagt til nåleskovegnene.

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Chapter 4

De økologiske behov for både skovarter og andre arter kan bruges til at målrette naturforvalt-

ningsindsatsen og udvikle arternes levesteder til en tilstand, som matcher, samtidig med at

begrænse kendte trusler i det omfang, det er muligt. Klimaforandringer er et eksempel på en

for nogle arter afgørende trussel, som ikke kan begrænses lokalt. Hvis biotop og andre økolo-

giske behov optimeres vil arternes overlevelsesmuligheder øges – måske tilstrækkeligt til at

kunne klare også klimapres.

Nogle truede arter kan ud over i beskyttede områder få deres økologiske behov opfyldt helt

eller delvist i den ubeskyttede eller generelt beskyttede "matrix", hvilket understøtter opret-

holdelse af arterne (Brunet et al. 2010, Wilson et al. 2010). Det gælder ikke mindst for arter,

der ikke primært er truet af skovdrift, men af andre faktorer, fx klimaændringer eller eutrofie-

ring, men kvaliteten af matrix som levested spiller naturligvis også en afgørende rolle. Som

ovenfor nævnt er den generelle naturbeskyttelse på Naturstyrelsens arealer øget væsentligt de

sidste ca. 25 år med mere dødt ved, livstræer, naturpleje, forbedret hydrologi mv for at forbed-

re mulighederne for, at flere truede arter kan få deres økologiske behov opfyldt også i "ma-

trix", dvs. overalt i statsskovene.

Metodikken for fastlæggelse af arternes økologiske behov beskrives nærmere i afsnit 4.

2.6

Optimering af omkostningseffektivitet og forvaltning

Ud over ovenstående biologiske argumenter for valget af indsats kan det tænkes, at nogle

skovarters økologiske behov kan tilgodeses med

billigere

tiltag end fx urørt skov, jf. Natur-

pakkens beslutning om at en del skove skal udlægges som anden biodiversitetsskov, hvor der

fortsat kan ske en vis (nedsat) økonomisk udnyttelse. Det kan fx gælde skovarter med præfe-

rence for halvåbne eller lysåbne biotoper.

I lyset af Naturpakkens beslutning har projektet inddraget økonomien for skovarterne for at

optimere omkostningseffektiviteten. Økonomien for ikke-skovarter er derimod kun belyst

oversigtligt.

Forskning om biodiversitet i skov anbefaler til supplement af urørt skov en vifte af forvalt-

ningstiltag inklusive genopretning af naturlige processer, videreførelse af gamle driftsformer

som græsnings- og stævningsskov, ikke-traditionel aktiv naturforvaltning for at fremme dødt

ved og andre urskovsagtige strukturer, samt målrettet artsforvaltning. Derudover beskrives det

som vigtigt med et vist minimum af beskyttelse også i det generelle skovlandskab (fx Göt-

mark 2013, Timmermann et al. 2015, Watson et al. 2016). Valget af konkrete tiltag kan be-

grundes ud fra normative principper (fx et statisk eller dynamisk natursyn), faktisk evidens

samt økonomiske overvejelser. En række af de nævnte indsatser vil være billigere end udlæg

som urørt skov, men nogle af dem kan alt efter omstændigheder også være dyrere.

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173

I denne sammenhæng har vi som nævnt taget udgangspunkt i et pragmatisk dynamisk natur-

syn, hvor vi anerkender at aktiv forvaltning i en del tilfælde vil være mere effektivt end urørt

skov i en længere årrække. Det gælder ikke mindst for arter, som er afhængige af specifikke

successionsstadier, som grundet lille naturareal i det moderne landskab ikke kan opretholdes i

urørt skov indenfor de tidshorisonter, som er nødvendige for arten (Bernes et al. 2015, Göt-

mark 2013, Vera 2000). En del af de nødvendige tiltag kan indarbejdes i Naturpakkens rela-

tivt rummelige forståelse af urørt skov, mens andre med tiden kan erstattes af naturlige dyna-

mikker i større beskyttede landskaber, hvor der er genoprettet dynamikker, som skaber en

naturlig balance mellem lysåben natur og skov. Her er der endnu begrænset praktisk evidens i

det mindste fra vores breddegrader (Fløjgaard et al. 2017).

I princippet bør et alternativ til urørt skov vælges, hvis det er enten billigere med samme ef-

fekt eller beskytter arten bedre. Tilsvarende gælder det, at det alt andet lige er mest omkost-

ningseffektivt at udlægge urørt skov og anden biodiversitetsskov på arealer med lavest øko-

nomisk værdi, såfremt beskyttelseseffekten ikke kompromitteres.

For at nå frem til en omkostningseffektiv udlægning af skove er der derfor behov for at kunne

identificere, hvor truede arter, som primært kan gavnes med urørt skov findes, i forhold til

hvor der er arter, som kan trives med, eller ligefrem behøve andre beskyttelsesindsatser. Der-

udover er der behov for viden om den økonomiske værdi af skovproduktion på skovarealerne.

2.7

Systematic conservation planning - SCP

Forskningen anbefaler at bruge optimerings algoritmer (som en del af en ”systematic conser-

vation planning” - SCP) til at kombinere biologiske og økonomiske oplysninger for at opnå

omkostningseffektiv beskyttelse af biodiversitet (Margules & Pressey 2000, Moilanen et al.

2009, Venter et al. 2014, 2017, Watson et al. 2016). SCP-algoritmer er blevet udviklet i løbet

af de seneste årtier, så de nu er i stand til at levere god vejledning til beslutninger om, hvor og

hvordan man effektivt kan nå givne bevaringsmålsætninger, givet at de nødvendige data er

tilgængelige (Beyer et al. 2016, Kukkala & Moilanen 2013, Moilanen et al. 2009).

Der er publiceret mange SCP-studier (Kukkala & Moilanan 2013), men kun en meget lille

andel af dem har været anvendt i reel naturbeskyttelse (Knight et al. 2008). En hovedårsag til

kløften mellem studier og reel implementering er, at virkeligheden er meget kompleks i for-

hold til de teoretiske modelstudier og derudover karakteriseres af mange begrænsninger af

politisk og socioøkonomisk art. En anden medvirkende årsag kan være, at mange SCP-studier

kun skelner mellem "beskyttet" og "ubeskyttet" natur i planlægnings-algoritmerne, hvilket

oftest er for simpelt, ud over at det groft kan overestimere de økonomiske omkostninger ved

naturbeskyttelsen (Watts et al. 2009, Wilson et al. 2010). Hvis man gik ud fra, at kun fuldt

naturbeskyttede områder bidrager til arternes beskyttelse, fandt Wilson et al. (2010) fx, at de

økonomiske omkostninger blev overvurderet med en faktor 15 og arealet med behov for na-

turbeskyttelse med en faktor på næsten 50.

174

Chapter 4

Overordnet er det svært at svare på, hvor meget beskyttelse, der skal til for at sikre arternes

langsigtede opretholdelse (Tear et al. 2005). Selv for de bedst kendte enkeltarter er det svært

at beregne behovet, men det kan tilnærmes ved brug af "population viability analyse - PVA".

Metoden kræver dog meget omfattende og detaljeret viden om arten og dens bestande, repro-

duktion, metapopulationsstruktur mv., som det normalt er umuligt at skaffe uden meget stor

forskningsindsats. Med mange arter i spil er det i praksis umuligt at bruge PVA-metoden. I

stedet bruges ofte et mere arbitrært fastsat mål i form af, at hver art ønskes beskyttet mindst 1

sted, eller evt. flere steder. Alt andet lige forventes sandsynligheden for langsigtet oprethol-

delse af en art at stige med antallet af forekomststeder og bestande.

I PhD-projektet blev tilgængelige SCP-metoder og software-systemer screenet og

Marxan

with Zones

(MxZ) (Watts et al. 2009) valgt til optimeringsopgaven. Valget skyldtes Marxan-

programmets 1) gode dokumentation, 2) udbredte anvendelse, 3) evne til at arbejde effektivt

med flere forskellige beskyttelsestyper samtidig (zoner), 4) evne til at løse optimeringsopga-

ven selv med mange hundrede arter forekommende i mange hundrede forskellige skove, og 5)

mulighed for let samspil med GIS for at understøtte en iterativ interaktiv planlægningsproces

(Segan et al. 2011, Watts et al. 2008a, Wilson et al. 2010).

Måltallet for antal repræsentationer blev sat til henholdsvis 3 og 5 for at øge sandsynligheden

for arternes levedygtighed i forhold til 1 repræsentation og for at kunne vurdere analysernes

følsomhed for denne parameter.

Chapter 4

175

3. Data

3.1

Areal- og økonomidata

Data for arealer og økonomi (status 2016) blev leveret af Naturstyrelsen. Det samlede areal

forvaltet af styrelsen var 202.402 ha (4,7 % af Danmarks landareal) fordelt på 976 administra-

tive polygoner varierende i størrelse fra mindre end 0,1 ha og op til 6.399 ha. Arealerne om-

fattede ud over skov også bl.a. søer, heder, veje og andet. Skovbevoksning udgjorde 105.919

ha (52,3 %), heraf 41 % løvtræ- og 59 % nåletræ-domineret.

For hver administrativ polygon leverede Naturstyrelsen økonomiske data i form af årlig po-

tentiel nettoindkomst fra høst af henholdsvis løvtræ og nåletræ, med bemærkning om at talle-

ne var baseret på modelberegninger med pris- og omkostnings-niveau for året 2016. De abso-

lutte værdier svarer derfor ikke nødvendigvis til virkeligheden i hver enkelt skov eller år, men

overordnet set skulle tallene være egnede til sammenligning af værdier og til at estimere årligt

nettotab i indkomst, hvis en skov udgår af forstlig drift og overgår til urørt skov.

For 18 polygoner med negativ nettoindkomst blev tallet trunkeret til nul, idet de negative

værdier var små og næppe realistisk ville blive gennemført i praksis. Andre 327 polygoner

havde nettoindkomst på nul i forvejen, fordi de enten var totalt beskyttede skove eller andre

biotoper uden relevant forstlig produktion. Det var fx søer forvaltet af Naturstyrelsen med

træer langs bredderne.

3.2

Artsoplysninger og -forekomster

Identifikationen af 2020-måls-arterne fremgår mere detaljeret af Buchwald & Heilmann-

Clausen (2016), og omfatter de truede arter, som er afgørende for om de politiske 2020 mål

for biodiversitet kan nås. I alt drejer det sig i Danmark om potentielt 1932 arter, når man in-

kluderer alle arter, som er listet på mindst én af de artslister, som 2020 målene omhandler.

Taksonomi og navngivning blev strømlinet med standard artslisten "allearter.dk" (DANBIF

2016).

For hver art har projektet sammenstillet oplysninger om bl.a. forekomster, taksonomi, syno-

nymer, levested, trusselskategori og status rapporteret til EU, med henblik på at kunne vurde-

re hver arts status og behov på lands- og på lokalitetsniveau. Forekomster blev sammenstillet

på grundlag af datakilderne listet i bilag A. Funddata blev kvalitetssikret som supplement af

dataleverandørernes egen kvalitetssikring gennem en række semi-automatiske procedurer.

Derved blev en række fund udeladt som følge af usikkerhed i relation til enten artsidentifika-

tion eller lokalitet. Øvrige oplysninger blev hentet i DCEs rødlistedatabase (Wind & Pihl

2010), i databasen allearter.dk (Skipper 2017), CMECs interne databaser, officielle EU-

rapporteringer (EIONET 2013a, EIONET 2013b), DCE rapporter (Fredshavn et al. 2014, Sø-

gaard & Asferg 2007), suppleret med mere specifikke datakilder for de enkelte artsgrupper.

176

Chapter 4

Arternes forekomststeder blev samkørt i GIS med Naturstyrelsens arealer (GIS-fil af 14. april

2016 fra Naturstyrelsen) samt med Danmarks forskellige typer af naturbeskyttelse, herunder

Natura 2000 områder, fredninger, § 3 områder, reservater, driftplanbeskyttelse i statsskov,

aftaler om skovbeskyttelse osv.. Beskyttelsestyperne blev henført til IUCNs internationale

kategorier I til VI, hvor kategori I er den stærkeste beskyttelse (Dudley 2008).

3.3

Prioritering af arter fra 1 til 5, henholdsvis A og B

For at tage højde for fokus i Aichi mål 12 på truede arter

i tilbagegang,

og for den af Wilson

et al. (2010) anførte betydning af matrix for mange arter, blev der i projektet skelnet mellem

arter med og uden påvist tilbagegang. Arter uden påvist tilbagegang må antages langt hen ad

vejen at få deres økologiske behov opfyldt ved det nuværende beskyttelsesniveau, således at

deres behov for yderligere beskyttelse alt andet lige er mindre end for arter i tilbagegang.

På baggrund af de indsamlede data om trusselsgrad og bestandsudvikling blev hver art således

tildelt en prioritet fra 1 til 5 i forhold til de politiske 2020-mål om at standse tabet af biodiver-

sitet (Buchwald & Heilmann-Clausen 2016):

1) Truet globalt ifølge IUCNs redlist (IUCN 2015).

2) EU art rapporteret ugunstig i 2013 (EIONET 2013a, 2013b)

3) Andre truede arter i tilbagegang jf. Rødliste 2010, inklusive regionalt uddøde (Wind & Pihl

2010).

4) Truede arter uden rapporteret tilbagegang i hverken EU rapporter eller Rødliste 2010.

5) Arter, som ikke matcher prioritet 1 til 4 og kun er listet på slægtsniveau på habitatdirekti-

vet.

Prioritet 1 til 3 er vigtige i relation til FN-målet, mens prioritet 2 er vigtigst for det heraf afled-

te EU-mål. Såfremt arterne i prioritet 4 og 5 ikke skifter til at være i tilbagegang, påvirker de

ikke 2020 målet. Prioritet 1 til 3 blev i visse analyser samlet til A-prioritet, mens prioritet 4 og

5 blev samlet til B-prioritet.

3.4

Habitatpræferencer og trusler

Præferencer, trusler og økologiske behov for hver art blev i første omgang baseret på oplys-

ninger i Rødliste 2010 databasen (Wind & Pihl 2010), som har indsamlet relevante data og

erfaringer fra detail litteratur og artseksperter. For nogle få arter, hvor oplysningerne mangle-

de eller var åbenlyst utilstrækkelige, blev supplerende artsspecifik litteratur konsulteret.

Arterne opdeltes først i obligate henholdsvis fakultative skovarter samt ikke-skovarter, med

underopdeling af skovarterne til løvskov, nåleskov og løv-nål blandet skov. Opdelingen skete

ved hjælp af en tidligere anvendt og kvalitetssikret database hos CMEC (Petersen et al. 2012,

Chapter 4

177

Petersen et al. 2016b). For arter, som ikke var med i databasen, er der efter samme metode

taget udgangspunkt i oplysningerne om levested i Rødliste 2010 databasen (Wind & Pihl

2010), således at arter med kun skov anført som levested er henført til obligate skovarter, arter

med både skov og lysåbne biotoper som levested er henført til fakultative skovarter, og arter

uden skov nævnt som levested er henført til ikke-skovarter. Skovarter er således opfattet bredt

og inkluderer en række arter, hvis primære biotoper er lysåbne. Der er desuden en glidende

overgang mellem skovarter og ikke-skovarter, idet en række arter, som tidligere omtalt, trives

bedst i overgangs- eller blandingszoner mellem skov og lysåbne biotoper.

Arter blev endvidere noteret som saproxyliske (medvirkende til, eller afhængige af nedbryd-

ning af dødt ved), hvis de var anført som fakultativt eller obligat saproxyliske i en af de til-

gængelige lister over saproxyliske arter relevante for Danmark (Alexander 2002, Köhler

2000, Nieto & Alexander 2010, Stokland & Meyke 2008, Wind & Pihl 2010). Derudover blev

deres afhængighed af lysåbne miljøer vurderet, ligesom tilknytning til henholdsvis løv- og

nåletræer blev noteret.

De resulterende trusler og økologiske behov blev derefter gennemgået og kvalitetssikret af

forfatterne for de artsgrupper, som de havde stor ekspertise i (svampe: Jacob Heilmann-

Clausen. Planter, sommerfugle, guldsmede og vertebrater: Erik Buchwald). For tilsvarende at

få kvalitetssikret oplysningerne for skovarter i andre artsgrupper blev yderligere artseksperter

konsulteret, og det lykkedes at få bidrag fra følgende eksperter ved Statens Naturhistoriske

Museum og Biologisk Institut: Jan Pedersen (Biller) og Ulrik Søchting (Laver).

Kvalitetssikringen af arternes trusler og økologiske behov medførte præciseringer for 111 af

de 1378 arter (8 %), men der blev ikke fundet fejl. Det vurderes derfor ikke problematisk, at

det for knap 8 % af arterne (spindlere, årevinger, tæger, græshopper og fluer) ikke lykkedes at

få eksterne artseksperter ind over.

Baseret på ovenstående blev samtlige skovarter til sidst inddelt i grupper med forskellige for-

valtningsbehov ud fra deres tilknytning til a) dødt ved (saproxyliske arter), b) lysåben eller

skygget habitat (lys behov), c) nåletræer versus løvtræer, samt d) længe urørt skov (Se næste

afsnit).

178

Chapter 4

4. Metode

Som følge af Naturpakkens konkrete beslutning om udlæg af store skovarealer til biodiversi-

tetsskov, blev skovarter underkastet andre og mere omfattende analyser end de arter, som ikke

er tilknyttet skov. De følgende tre underafsnit omhandler derfor skovarterne, mens øvrige

arter omtales i afsnit 4.4 derefter.

4.1

Zoner og beskyttelsestyper for skovarter

For at kunne beregne og omkostningsoptimere beskyttelsen af skovarterne blev forskellige

forvaltningsindsatser samlet i fire kategorier, baseret på de enkelte arters præferencer og øko-

logiske behov, og samtidig relateret til Naturpakkens kategorier og til forvaltningsindsatser-

nes omkostningsniveau.

Efter denne forenkling af kategorier af beskyttelse eller forvaltning til fire "zoner", blev zo-

nerne brugt i optimeringsprogrammet

Marxan with Zones

(Watts et al. 2009), som er specielt

udviklet til optimering af valg af områder til naturbeskyttelse.

Zone 1 har som default en speciel betydning ved at være tilgængelig for normal drift, og ud-

gør derved den "ubeskyttede" matrix med omkostning 0. De tre øvrige zoner blev valgt og

afgrænset, så de så vidt muligt også kan relateres til internationale forsknings- og erfaringsba-

serede skovbeskyttelsestyper (Boch et al. 2013, Buchwald 2005, Götmark 2013, Peterken

1996):

Zone 1

Zone 2

Zone 3

Zone 4

Normal statsskov uden ekstra beskyttelse,

Ekstra beskyttelse af arter knyttet til nåletræer/nåleskov,

Aktiv ekstra beskyttelse af løvskov inkl. gamle driftsformer, og

Urørt løvskov.

Zone 1 indebærer ingen ekstra beskyttelse ud over den generelle beskyttelse, som jf. afsnit 2.2

gælder for alle statsskove. Zone 4 svarer til Naturpakkens urørte skov i løvskovsegne, hvor

forstlig økonomisk drift efter en overgangsperiode skal ophøre, mens pleje kan ske i mindre

omfang, fx i form af græsning.

Zone 2 og 3 blev afgrænset til at skulle svare til beskyttelse målrettet arter med andre eller

mere specifikke økologiske behov end, hvad der normalt kan forventes tilgodeset ved normal

drift eller umiddelbar overgang til urørt skov, særligt behov for blomsterrige lysninger, over-

gangsnaturtyper mellem skov og åbent land eller for specifikke værtstræer som fyr eller eg,

der i nordvest Europa ofte bukker under i løbet af en årrække, hvis de overlades til fri konkur-

rence i urørt skov uden græsning (Götmark 2013, Lindhe et al. 2005, Skov- og Naturstyrelsen

1994, Vera 2000).

Chapter 4

179

Zone 2 og 3 vil delvis svare til Naturpakkens "Anden biodiversitetsskov", men også til en

vifte af andre typer beskyttelse, fx græsningsskov, stævningsskov og andre typer mere aktiv

skovbeskyttelse i statsskovene, samt med dele af styrelsens beskyttede lysåbne biotoper, hvor

der også ofte er træer og buske.

Bemærk, at arter henført til zone 3 (Aktiv) inkluderer arter med helt lysåbne biotoper som

primært levested, fx græsland eller hede, hvis et skovlevested er nævnt i rødlistedatabasen i

form af skovbryn, skoveng eller skovlysning, selvom skovlevestedet kan være helt marginalt

for arten. Det gælder fx sommerfuglen lille køllesværmer (Zygaena

viciae),

blomsterne kost-

nellike (Dianthus

armeria)

og kvast-høgeurt (Pilosella

cymosa),

samt billen klint-oldenborre

(Omaloplia

nigromarginata).

Mange af de bedste lokaliteter for zone 3 (Aktiv) arter er således lysåbne biotoper omgivet af

skov eller med spredte træer, så der er et varmt mikroklima i læ. Det vil åbenlyst være øde-

læggende for disse arter, hvis de lysåbne dele, som arterne fordrer, gror helt til med træer.

Eksempler er solelskende insekter som svirrefluer, visse biller og de fleste dagsommerfugle,

som både har behov for rige nektarkilder fra blomster og solbeskinnede skovbryn eller veteran

træer. Tab af lysåbne biotoper som følge af tilgroning med træer anses for en væsentlig trussel

for disse arter (Wind & Pihl 2010).

Det følger heraf, at forvaltningen af zone 3 (Aktiv) ikke nødvendigvis bør have fokus på

skovbeskyttelse i traditionel forstand. Den skal, afhængigt af artsindholdet have fokus på op-

retholdelse af de relevante lysåbne biotoper i samspil med omgivende skov eller gamle træer

som, igen afhængigt af artsindholdet, i nogle tilfælde kan kombineres med en større eller

mindre ved- eller biomasseproduktion, evt. som del af en aktiv habitatpleje. Det medvirker til,

at zone 3 kan have relativt lavere omkostninger end zone 4 (Urørt).

Da det bortset fra zone 1 og 4 er vanskeligt mere præcist at estimere de økonomiske konse-

kvenser / tab, blev optimeringsprogrammet kørt med flere forskellige forudsætninger for om-

kostninger i zone 2 og 3 (Tabel 1).

Tabel 1. De fire valgte zoner med de undersøgte værdier for økonomisk tab.

Zone

Kort navn

Normal

Nål

Aktiv

Urørt

Beskrivelse

Ingen ekstra beskyttelse (Marxans "available" zone)

Skov med ekstra beskyttelse af arter tilknyttet nåletræer

Aktiv ekstra beskyttelse i løvskov

Urørt løvskov

Tab

10 eller 25 %

50, 75 eller 100 %

100 %

Skovarter blev ud fra deres økologiske behov og artspræferencer som udgangspunkt fordelt til

otte grupper (Tabel 2) som kunne relateres til en eller to af de fire Marxan zoner. Arter med

snævre præferencer blev relateret til én zone, mens arter med mindre snævre krav til levested

blev relateret til to zoner, hvis begge zoner vil kunne tilgodese arternes behov.

180

Chapter 4

Tabel 2. Karakteristika for artsgrupper brugt til zone fordeling. Hver art blev henført til den

første gruppe fra oven som matchede. Arter med fakultativ eller obligat præference for nåle-

træ blev udelukket fra gruppe 1 til 5.

Grup-

174

102

147

Gruppenavn

Lysåben

saproxylisk

Lysåben

skov

Saproxylisk

skovart

Urørt

løvskov

Anden

løvskov

Saproxylisk

nåletræsart

Obligat

nåletræsart

Fakultativ

nåletræsart

Skovarter

Zone(r)

3+4

2+4

2+3

Habitat præferencer / behov / tilknytning

Saproxyliske arter tilknyttet løvtræer i lysåbne biotoper,

men også i skov.

Ikke-saproxyliske arter tilknyttet lysåbne biotoper, men

også løvskov.

Saproxyliske arter kun tilknyttet løvskov og ikke nogen

lysåbne biotoper.

Ikke-saproxyliske arter tilknyttet urørt løvskov, men ikke

tilknyttet nogen lysåbne biotoper.

Ikke-saproxyliske arter tilknyttet løvskov, men hverken

tilknyttet urørt skov eller lysåbne biotoper.

Saproxyliske arter med præference for nåletræ.

Ikke-saproxyliske arter obligat tilknyttet nåletræer.

Andre nåletræs-tilknyttede arter end gruppe 6 og 7, fx

arter med behov for en blanding af nåletræer og løvtræer

eller tilknyttet begge slags træer.

Total

626

Zonetilhøret jf. tabel 2 blev justeret inden Marxan optimeringen for 15 % of de 626 skovarter

i forbindelse med ovennævnte ekspertkonsultation, idet 94 arter fik ændret zonetilhør i for-

hold til udgangspunktet: 71 arter fik en ekstra zone, 5 arter en ændret zone og 18 arter en zone

mindre for at matche artens præferencer bedre. De endelige zoner anvendt i Marxan optime-

ringen fremgår for hver skovart i bilag B sammen med andre artsspecifikke oplysninger.

4.2

Optimering for skovarter med programmet

Marxan with zones

Opgaven med at finde en omkostningseffektiv måde at bevare skovarterne på svarer til at løse

et "minimums-sæt" optimeringsproblem (Possingham et al. 2006). Det går ud på at nå kvanti-

tative naturbevaringsformål billigst muligt ved at optimere hvilke områder, der skal udvælges

til beskyttelse. I lyset af Aichi mål 12 blev det overordnede formål om at sikre levedygtige

bestande af arterne kvantificeret til, at der på Naturstyrelsens arealer skulle sikres mindst 3

eller mindst 5 områder for hver 2020-måls-art med en beskyttelsestype (zone), som matchede

artens økologiske behov.

Der er som nævnt i afsnit 2.7 ingen garanti for, at hverken 3 eller 5 områder vil være nok til at

sikre en arts langsigtede overlevelse. For mange af de truede arter udgør 3 eller 5 områder dog

100 % af de kendte forekomster hos Naturstyrelsen, og for de fleste mindre sjældne arter vil

antallet af beskyttede forekomster blive væsentligt højere end måltallet (Cabeza & Moilanen

2001, Justus et al. 2008, Petersen et al. 2016a, Tear et al. 2005). Derudover har mange af ar-

Chapter 4

181

terne også levesteder udenfor Naturstyrelsens arealer, herunder i fredede eller på anden måde

naturbeskyttede områder.

For at kunne vurdere optimeringens følsomhed i forhold til usikkerheder for en række af de

indgående data, blev der kørt en række scenarier med forskellige kombinationer af forudsæt-

ninger. Tabel 3 er en oversigt over forudsætninger og input for hvert af 26 scenarier. Ikke alle

kombinationer af forudsætninger blev kørt som følge af det store antal (2*2*3*2*2*2*2=192)

mulige kombinationer. I stedet blev scenarier kørt som successive parvise sammenligninger,

hvor forudsætninger som gav relativt dyre, ineffektive eller urealistiske løsninger efterhånden

blev sorteret fra.

Tabel 3. Overblik over forudsætninger i 26 kørte scenarier.

Scenarie

Mål

antal

skove

Tab

Zone 2

(%)

Tab

Zone 3

(%)

Prioritets

arter med

Nåletræs

tilknyttede

arter

med?

Nej

Antal

arter

med

Tab

som

penge

eller

areal?

Penge

Areal

Penge

Areal

Penge

Areal

Penge

Areal

Penge

Areal

Penge

Areal

Penge

Areal

Penge

Areal

Penge

Areal

Penge

Syv

forvejen

urørte

skove

låst

Nej

Ja til z4

Fem

Tre

Fem

Tre

Fem

Tre

Fem

Tre

Fem

Tre

Fem

Tre

100

A og B

Kun A

A og B

Kun A

A og B

Kun A

A og B

Kun A

626

516

304

626

516

304

626

304

626

304

Tabet blev altid sat til 100 % i zone 4 (Urørt) og til 0 % i zone 1 (Normal).

182

Chapter 4

Skovområder, som i forvejen var beskyttede som urørt skov, blev a priori låst til zone 4

(Urørt) i scenarie O – Z, hvis skoven levede op til følgende kriterier: 1) 100 % af statsskovens

totalareal i forvejen besluttet som urørt skov OG det skovbevoksede areal udgør mindst 5 ha,

ELLER 2) Mindst 100 ha skovbevokset i forvejen beskyttet som urørt OG det udgør mere end

45 % af statsskovens totalareal. Afskæringskriterierne 5 ha og 45 % blev valgt for at sikre, at

store vigtige i forvejen urørte skove kom med, uden a priori at låse en lang liste af skove til

zone 4. Syv skove levede op til kriterierne (Bilag C).

For at fungere kræver Marxan at alle områder har en omkostning større end nul, hvilket ikke

var tilfældet i basistallene. Derfor blev alle omkostningstal for statsskove tillagt 1000 kr. før

kalibreringer og kørsler. Samme beløb blev efter kørslerne fratrukket hver skovs beregnede

tab.

For at levere valide robuste resultater og køre scenarier effektivt kræver Marxan desuden et

antal standard kalibreringer vedrørende "arts-straf-faktor" (Species Penalty Factor, SPF), antal

"runs" (gennemløb) og antal iterationer (Ardron et al. 2010, Segan et al. 2011, Watts et al.

2008a, 2008b, 2009, 2011). Som følge af at alle opstillede mål ikke var mulige at nå samtidig

blev kalibrerings metoderne i Watts et al. (2008a) anvendt (Watts et al. 2011).

Kalibreringsprocessen førte til at en standard på 100 gennemløb blev valgt ligesom i Watts et

al. (2008a), idet optimeringen ikke forbedredes ved at øge antallet yderligere. Optimeringen

blev til gengæld forbedret i form af billigere løsninger helt op til lidt under 10 millioner itera-

tioner, hvorfor det antal blev valgt trods øget tidsforbrug til kørslerne. Principielt kan kalibre-

ringen af "arts-straf-faktorer" (SPF) være forskellig for penge- og areal-scenarier, men reelt

påvirkede forskellen på den kalibrerede SPF for penge og for areal (henholdsvis 7,188 og

7,75) ikke optimeringen, hvorfor gennemsnitstallet på 7,47 blev anvendt. Alle tre kalibrerede

faktorer blev holdt konstant gennem alle scenarier.

Hvert af de 26 scenarier tog ca. 6 minutter procestid og leverede et antal filer med detaljer om

bl.a. skovenes tildelte zone, omkostningen derved, målopfyldelse pr. art og skov, samt scena-

riets "Objective function score". Sidstnævnte er Marxan-algoritmens samlede tal for optime-

ringen, hvor lave værdier er bedst.

Scenariernes succes blev ud over "Objective function score" vurderet i forhold til

1) økonomisk tab (årligt tab af nettoindkomst fra skovdrift), og

2) hvor mange af de 304 prioritet A arter, der når de konkrete måltal.

For arter med forekomst i færre statsskove end måltallet (3 eller 5) blev målet trunkeret til alle

statsskove med forekomst af arten. Som et supplement og for at vurdere hvor langt fra målop-

fyldelse hvert scenarie var, blev den

gennemsnitlige målopfyldelse

(GM) for hver af de 304

prioritet A arter i hvert scenarie beregnet. Den gennemsnitlige målopfyldelse er lig 1, hvis alle

Chapter 4

183

arter når deres mål. Arter, som ikke nåede deres måltal, benævntes

gap-arter,

jf. det engelske

ord "gap" (”hul”).

Eftersom Marxan optimeringen blev kørt på grundlag af de 976 administrativt fastlagte stats-

skovs-polygoner, kunne den kun levere resultater for dele af større skove, hvis disse dele hav-

de en særskilt statsskovs-polygon. Det var fx tilfældet for Gribskov-komplekset, som var op-

delt i flere mindre polygoner, mens de fleste andre skove i deres helhed omfattes af kun en

polygon.

4.3

Yderligere optimering

Skovene i Marxans mest optimale scenarier blev efterfølgende gennemgået manuelt for at se

på muligheder for yderligere optimering. Det viste sig nemlig umuligt ved Marxan optimerin-

gen at få opfyldt målene for alle arter samtidig, idet en række skove rummede for mange

sjældne arter med modstridende økologiske behov.

Den manuelle optimering startede med at undersøge den geografiske fordeling af prioritet A

arternes behov i skove med gap-arter. Hvis behovene var grupperet på en sådan måde, at en

underopdeling af skoven til mere end én beskyttelseszone kunne løse gap-artens behov uden

at kompromittere de arter, som Marxan havde baseret sit zone-valg på, blev dette noteret som

en mulighed.

Derudover blev gap-arternes data og økologiske behov gransket mere detaljeret ved yderligere

litteratur- og database-søgning, hvis de var registreret præcis samme sted i skoven, som andre

arter med modstridende behov, for at se om der eventuelt kunne være tale om fejl i data om

fundet eller artens økologi. Andre økologiske behov, fx affinitet til specifikke jordbundsfor-

hold, kunne i den forbindelse medvirke til forklaring.

Opdeling af skoven i mere end en zone blev valgt som anbefaling, hvor ovenstående tilgang

viste, at gap-arterne kunne tilgodeses, uden at det gik ud over andre arters målopfyldelse.

I en teoretisk optimal situation kunne man have kørt optimeringen færdig i Marxan. Det ville

have krævet, at man havde både økonomi- og artsdata i en meget finere geografisk skala end,

hvad der er tilgængeligt eller realistisk.

184

Chapter 4

4.4

Ikke-skovarter

I princippet kunne de arter, som ikke var skovarter, have været tildelt en speciel "ikke-skov"

zone i Marxan analyserne og dermed være kørt med i Marxan optimeringen. Det var dog i

praksis ikke muligt som følge af, at mange af statsskovs-polygonerne rummer både meget

store og vigtige lysåbne biotoper foruden skov, hvilket Marxan ikke ville kunne håndtere hen-

sigtsmæssigt med de foreliggende skov-niveau data. Desuden er de økonomiske omkostninger

meget forskellige for de mange varierede beskyttelsestyper for lysåbne biotoper. Så én zone

med én omkostning ville ikke give mening.

Det blev i stedet analyseret, om de steder, hvor ikke-skov-arterne var registreret, var beskyttet

gennem de hidtidige strategier for naturbeskyttelse, og om de lå på Naturstyrelsens arealer.

Der blev i den forbindelse udført en gap-analyse, der belyste, hvor godt de hidtidigt beskytte-

de arealer dækkede arterne, og det blev undersøgt, om der var væsentligt sammenfald mellem

forekomst af globale, nationale og EU-truede arter.

Det blev desuden undersøgt, hvor og hvordan ændret indsats på Naturstyrelsens lysåbne area-

ler konkret kan bidrage målrettet til 2020-målet:

1) Kan der identificeres konkrete ubeskyttede lokaliteter med truede arter i prioritet 1 til 3?

2) Kendes arternes økologiske behov godt nok til at foreskrive ændret drift?

3) Kan nødvendige ændrede driftstiltag identificeres?

4) Kan prisen for de nødvendige driftsændringer beregnes?

Det var forventningen, at der for mange truede arter kunne identificeres en række lokaliteter,

hvor den hidtidige forvaltning ikke passede til arternes økologiske behov, og der dermed kan

anbefales ændret forvaltning.

Nedenstående kriterier blev valgt til brug for en afklaring af, hvor indsats burde prioriteres

højest. Planlagt og igangværende forvaltning af Natura 2000 områderne modvirker generelt

en række af de trusler, som skader truede arter, og vil derfor ikke kun gavne de arter, som var

grundlag for udpegning af områderne, men også områdets øvrige truede arter med tilsvarende

økologiske behov. En lang række indsatser er allerede i gang i Natura 2000 områderne som

opfølgning på Natura 2000 planerne. I prioriteringssammenhæng vurderes disse steder ikke at

være dem der trænger mest til supplerende indsats. Hvor der er tale om modstridende interes-

ser for forskellige arter, vil udpegnings-grundlags arterne under alle omstændigheder have

juridisk forrang. Dette er baggrunden for kriterierne om andel af forekomsten dækket af habi-

tatområde (SAC) henholdsvis fuglebeskyttelsesområde (SPA). Tilsvarende overvejelser ligger

bag medtagelsen af kriteriet om andel allerede stærkt beskyttet areal (IUCN kategori 1-2).

Chapter 4

185

Følgende kriterier brugtes til identifikation af relativt ubeskyttede steder med

internationalt

truede

arter, dvs. truet på globalt eller EU niveau:

• Globalt truet art, og dermed af særlig betydning for 2020-målet, dvs. prioritetsklasse 1.

• Rapporteret ugunstig i 2013 for EU arter (tilbagegang for fugle), dvs. prioritetsklasse 2.

• NST ejet lokalitet.

• IUCN kat 1-2 dækker < 10 % af forekomsten af arten på lokaliteten.

• For EU-Annex II arter dækker SAC < 10 % af arten på lokaliteten.

• For fugle dækker SPA udpegningsgrundlag ikke arten på lokaliteten.

• Væsentlig forekomst, så > 1 % af DKs registreringer er på lokaliteten.

• Dog > 0 % for globalt truede arter.

• Arten er ikke i markant fremgang og ikke med sikkerhed uddød.

Som følge af sidstnævnte kriterie udgik gråsæl og odder (markant fremgang) samt urfugl (ud-

død).

Følgende kriterier brugtes tilsvarende for

dansk truede

arter:

• Dansk truet art i tilbagegang, dvs. prioritetsklasse 3.

• NST ejet lokalitet.

• IUCN kat 1-2 dækker < 10 % af forekomsten af arten på lokaliteten.

• SAC dækker < 10 % af forekomsten af arten på lokaliteten.

• Væsentlig forekomst, så > 1 % af DKs registreringer er på lokaliteten.

På basis af kriterierne blev steder på Naturstyrelsens arealer identificeret, som vurderes at

kunne have størst nytte af ændret eller ekstra beskyttelse for de 2020-måls-arter, der ikke er

tilknyttet skov.

186

Chapter 4

5. Resultater og diskussion

5.1

Oversigt over arternes status og prioritet

Fordelingen af 2020-måls-arter til artsgrupper og trussels geografi fremgår af tabel 4. Flere af

de regionalt uddøde arter er genfundet siden 2010 rødlistningen, så kategorien RE er relevant

at medtage.

Tabel 4. Fordelingen af undersøgelsens 1932 arter *.

Artsgruppe \ antal arter

Pattedyr

Fugle

Krybdyr

Padder

Insekter

Andre invertebrater

Svampe

Laver

Mos

Planter

I alt

DK_RE

142

287

DK_truet

164

Global

I alt

654

552

396

144

1932

498

528

293

108

1467

* En del arter er truet på flere geografiske niveauer, men tælles her kun med i én, nemlig den mest

globale. RE står for regionalt uddød. Truet omfatter kategorierne kritisk truet (CR), moderat truet

(EN) og sårbar (VU). EU arterne omfatter annex 1 på fuglebeskyttelsesdirektivet og annex II, IV og V

på habitatdirektivet.

I alt blev ca. 22 millioner artsregistreringer (Bilag A) tjekket for 2020-måls-arterne. Det resul-

terede i funddata fra perioden 1991-2015 for 1378 (Tabel 5) af de potentielt mulige 1932 ar-

ter. Efter kvalitetssikring var der 267.438 stedfæstede forekomster for disse 1378 arter, som

således indgik i det videre projekt.

Forskellen på 554 ikke registrerede arter udgøres hovedsagelig af 231 længe uddøde arter og

af en lang række vanskeligt bestemmelige lav-arter, biller og jordmøl, som meget få personer

kan kende, og som derfor enten ikke er eftersøgt, ikke fundet eller ikke registreret i de tilgæn-

gelige databaser.

Chapter 4

187

Tabel 5. Fordelingen til artsgruppe af 2020-måls-arter fundet i DK 1991-2015 uanset ejer.

De fem højre kolonner viser fordelingen af arterne til de anvendte prioritetsklasser.

Antal arter med fund

Pattedyr

Fugle

Krybdyr

Padder

Insekter

Andre invertebrater

Svampe

Laver

Mos

Planter

I alt

DK_RE

DK_truet

153

Global

I alt

369

520

197

122

1378

Pr1

Pr2

Pr3

Pr4

311

528

Pr5

328

510

177

107

1158

277

206

175

757

Af de 1378 fundne arter (Tabel 5) er 1066 arter svarende til 77,4 % registreret på Naturstyrel-

sens arealer (Tabel 6), hvilket er de fund, som er særligt relevante for nærværende rapport, og

som indgår i optimeringsdelen af projektet.

Tabel 6. Fordelingen af 2020-måls-arter fundet 1991-2015 på Naturstyrelsens arealer. De

fem højre kolonner viser fordelingen af arterne til de anvendte prioritetsklasser.

Antal arter med fund

Pattedyr

Fugle

Krybdyr

Padder

Insekter

Andre invertebrater

Svampe

Laver

Mos

Planter

I alt

DK_RE

DK_truet

143

Global

I alt

247

435

130

1066

Pr1

Pr2

Pr3

Pr4

255

414

Pr5

219

429

120

886

188

178

118

566

Ligesom en række jordmøl, biller og lavarter er mangelfuldt kendt og registreret, er forekomst

og udvikling over tid også usikker for en del af de andre truede arter, som er vanskelige at

registrere, eller som meget få personer kan kende. Oplysningerne, som ligger til grund for

prioriteringerne, er derfor af varierende sikkerhed og dermed også prioriteringerne. Det gæl-

der bl.a. vurderingen i rødlisten af, om en art er i tilbagegang eller ej. Dels er den udført af

forskellige arts eksperter, dels skønnet over forskellige årrækker fra 10 til 100 år afhængigt af

hver arts generationstid, som heller ikke altid kendes eksakt. Denne og andre usikkerheder, jf.

afsnit 6, bør derfor haves in mente ved tolkning af resultater og prioriteringer.

188

Chapter 4

Selvom en art evt. kun blev registreret tidligt i årrækken 1991-2015, vurderes det fortsat rele-

vant at opretholde levestedet, idet en del af arterne lever skjult og kan være overset eller duk-

ke op igen, ligesom andre truede arter ofte vil leve samme sted.

Det fremgår af figur 1, at 296 (27,8 %) af de 1066 2020-måls-arter kun er kendt fra ét af Na-

turstyrelsens polygoner, og yderligere en lang række arter kun fra 2, 3, 4 eller 5 polygoner.

Hare (Lepus

europeaus)

var den mest udbredte art, registreret fra 321 statsskovs-polygoner.

Figur 1. Fordeling af 2020-måls-arterne til Naturstyrelsens 976 arealer. (N = 1066 arter)

Chapter 4

189

5.2

Arternes præferencer

Fordelingen til skovarter og ikke-skovarter i relation til status og udviklingstrend fremgår af

tabel 7 for arterne kendt fra Naturstyrelsens arealer. De udgjordes af 626 skovarter og 440

ikke-skovarter. For skovarterne er der rapporteret tilbagegang for 48,7 % inklusive globalt

truede arter, mens det tilsvarende tal for ikke-skovarter er 70,9 %. Der er således flest arter

knyttet til skov, men en relativt mindre del af skovarterne er vurderet i tilbagegang i forhold

til arterne i andre biotoper.

Tabel 7. Fordeling af 2020-måls-arterne på skovarter og ikke-skovarter, samt prioriteter.

Prioritet

B 4+5

Total

Status og geografi

Globalt truet (IUCN redlist CR, EN

eller VU)

EU rapporteringsart jf. fugle- eller

habitat-direktiv

Dansk Rødliste 2010 (RE, CR, EN

eller VU)

Dansk Rødliste 2010 (RE, CR, EN

eller VU) eller EU rapporteringsart.

Tilbagegang

rapporteret?

Ja eller nej

Nej

Skovart

(N)

290

321

626

Ikke-

skovart (N)

276

128

440

Total

(N)

566

449

1066

Blandt skovarterne var 156 arter (24,9 %) saproxyliske, heraf 105 svampe, 50 insekter og 1

mosskorpion (Tabel 8). Af de 626 skovarter var 110 arter (18 %) mere tilknyttet nåletræer end

de var tilknyttet løvtræer.

Tabel 8. Fordeling til præferencer for 2020-måls-arter fra Naturstyrelsens arealer.

Arter (N)

Saproxyliske

Ikke-saproxyliske

Total

Obligate

skovarter

118 (16)

296 (68)

414 (84)

Fakultative

skovarter

174 (26)

212 (26)

Ikke-skovarter

440

Total

156 (16)

910 (94)

1066 (110)

I parentes vises antal arter der foretrækker nåletræer (nuller udeladt). Fakultative skovarter inklude-

rer arter, som også eller primært lever i åbne biotoper som fx vandhuller, enge, heder eller overdrev.

Figur 2 viser fordelingen på zoner for de 626 skovarter kendt fra Naturstyrelsens arealer. Zone

affiniteterne er baseret på arternes forskellige økologiske behov og præferencer efter arts-

eksperternes bidrag. Det fremgår, at en stor del af arterne vurderes at kunne få opfyldt deres

behov på mere end én måde ved fx både at kunne trives i urørt skov (zone 4) og i skov med

mere aktiv beskyttelse (zone 3). Sådanne arter noteres med begge zonenumre, fx 34. Endvide-

re fremgår det af figuren, at svampe og insekter udgør en meget væsentlig del af arterne, hvil-

ket er i overensstemmelse med, at disse artsgrupper er langt de mest artsrige generelt.

190

Chapter 4

Figur 2. Antal arter sammenholdt med behov for beskyttelse omsat til zoner. 2 = zone 2 (Nål);

3 = zone 3 (Aktiv); 4 = zone 4 (Urørt); 23, 24 og 34: både den ene og den anden nævnte zone

kan opfylde artens økologiske behov. N = 626 skovarter kendt fra Naturstyrelsens arealer.

5.3

Marxan-optimering for skovarter

I 692 (70.9 %) af de 976 polygoner, som statsskovene opdeles i, var der i perioden 1991-2015

registreret en eller flere af de 1066 forskellige 2020-måls-arter. Alle polygoner med mindst en

af de 626 skovarter blev medtaget i Marxan analyserne (670 polygoner med et samlet skovbe-

vokset areal på 103.234 ha). De øvrige 306 statsskov polygoner uden kendt forekomst af rele-

vante skovarter var fx søer, P-pladser, heder mv uden skov, eller ret små skove (samlet skov-

bevokset areal 2.684 ha). De blev manuelt ført til zone 1 (Normal).

Syv skove var hot spots med mindst 100 af 2020-måls-arterne. De var alle mindst 538 ha sto-

re. Seks af dem (86 %) var allerede delvis beskyttet på en måde som svarer til enten Aktiv

zonen (1-87 % af hver skov) eller Urørt zonen (3-24 % af hver skov). Den nåletræsdominere-

de plantage Tisvilde Hegn var den mest artsrige skov med 192 2020-måls-arter, heraf 126

skovarter. For løvskove var Klinteskov på Møn artsrigest med 179 2020-måls-arter, heraf 133

skovarter. To andre skove havde også mere end 100 skovarter, nemlig Gribskov med 120 og

Jægersborg Dyrehave med 116 skovarter.

For 450 statsskove var allokering til zone ens for alle 26 Marxan-scenarier. Når ovennævnte

306 zone 1 skove medregnes, blev i alt 756 (77.5 %) skove dermed systematisk placeret i

samme zone hver gang uanset de variable forudsætninger (Tabel 9).

Chapter 4

191

Tabel 9. Fordeling til zoner af de 976 statsskove i 26 scenarier.

Kort navn

Altid zone 4

Altid zone 3

Altid zone 2

Zone variabel

Altid zone 1

Total

Urørt

Aktiv

Nål

Normal

Skove (N)

220

681

976

Skovbevokset

areal (ha)

7.824

9.044

62.253

26.797

105.918

Penge

værdi (%)

100

Detaljer

Bilag D

Bilag E

Ingen

Bilag F

Bilag G

Detaljer om hver skov kan ses i bilag D til G (areal, skovtyper, eksisterende beskyttelse, MxZ zone,

artsantal, procent af observationer fra i forvejen beskyttede dele af skoven, og zone resultater for sce-

narie O og Q, hvis zone var variabel).

Afhængigt af scenarie endte 26-58 % af statsskovenes skovbevoksede areal i zone 1 (Nor-

mal), mens 9-24 % endte i zone 4 (Urørt) (Tabel 10 og bilag H). Også zone 2 (Nål) og zone 3

(Aktiv) med henholdsvis 0-24 % og 15-41 % var følsomme for ændringer i forudsætninger.

Tabet i form af nedgang i årlig netto indkomst fra skovdrift varierede over scenarier fra

26-69 % (Tabel 10 og bilag H).

Tabel 10. Gennemsnit for de 26 scenarier grupperet ud fra forudsætninger og antal arter, og

sorteret efter måltal og mål nået.

Mål Mål

nået

(af

304

arter)

255

259

199

218

230

231

Scenarier Antal

scen-

arier

Antal

arter

med

Tabs

forud-

sæt-

ninger

Lave

Høj -

lav

Høje

Høj -

lav

Høje

Middel

-lave

areal areal areal areal

Tabs

(ha)

(n)

GHOPVZ

CDKL

ABIJSW

EFMNR

QUY

626

304

516

626

304

36 -

26 -

58 -

43 -

46 -

38 -

17338

11565

22606

21623

16483

15281

z = zone. Alle arealer gælder skovbevokset areal, dvs. åbne områder som græsland og hede mv. er

udeladt. Max-min er kun vist for Tabs %, fordi der var meget begrænset variation omkring gen-

nemsnittene for de øvrige kolonner. Se bilag H for detaljerede resultater for hvert scenarie.

192

Chapter 4

De første 18 scenarier (A-R) var parvis ens bortset fra, at omkostningen skiftede mellem pen-

ge og areal (Tabel 3 & bilag H). I gennemsnit var løsningerne 0,16 % dyrere, hvis tabt areal

blev brugt som omkostningsparameter i stedet for økonomisk tab (samlet spænd var fra 0,72

% billigere til 1,02 % dyrere). Antal prioritet A arter med målopfyldelse var i gennemsnit 0,07

% bedre for areal-baserede scenarier (0,93 % værre til 1,29 % bedre). Eftersom alle disse for-

skelle vurderedes små og uvæsentlige i forhold til øvrige usikkerheder, og fordi det økonomi-

ske tab blev anset for mest relevant, blev de efterfølgende 8 scenarier kun kørt med økono-

misk tab.

Udelukkelse af arter med præference for nåletræer (Scenarie C, D, K, L) var 8,3 til 12,0 %

dyrere end at medtage dem, uden forbedret målopfyldelse (Tabel 10 & bilag H). Årsagen var,

at ingen skove kom i den relativt billige zone 2 (Nål), således at al beskyttelse også af arter

med relativt brede præferencer blev nødt til at ske i de dyrere zoner 3 og 4. Fx kan visse

sommerfugle trives i lysninger i både løv- og nåleskov. Samtidig fik de nåleskovs tilknyttede

arter ikke deres økologiske behov tilgodeset. Omkostningerne for zone 2 (Nål) var sat lavere

end for zone 3 (Aktiv), fordi mange af de truede zone 2 arter, fx fuglene natravn (Caprimul-

gus europaeus),

hedelærke (Lullula

arborea),

nøddekrige (Nucifraga

caryocatactes)

og per-

leugle (Aegolius

funereus)

foruden den globalt truede blank gæstemyre (Formicoxenus

nitidu-

lus)

kan trives med en mere eller mindre normal nåleskovsdrift eventuelt med mindre juste-

ringer. Af ovenstående grunde blev de nåletræs tilknyttede arter ikke udelukket i de følgende

scenarier.

Scenarier med både prioritet A og B arter (scenarie A, B, I, J, S, T, W, X) var 11-43 % dyrere

og med 1,2-7,3 % lavere målopfyldelse end scenarier baseret alene på prioritet A arter. Sænk-

ning af de forudsatte omkostninger fra fx 100 til 75 % (zone 3) eller fra 25 til 10 % (zone 2)

resulterede selvfølgelig i billigere resultater, men ændrede ikke (scenarie A/I, B/J, E/M, F/N,

Q/U, S/W, T/X, U/Y, V/Z) eller kun marginalt (O/V) på målopfyldelsen, hvilket indikerede,

at det primært var de biologiske input data, der drev zoneringen (Tabel 10 & bilag H).

Således var zone konstellationen identisk for scenarie Q, U og Y uanset, at omkostningerne

blev ændret fra 75/25 % over 75/10 % til 50/10 %. Zone konstellationen var tilsvarende næ-

sten identisk for scenarie O, V og Z. Det viste sig således generelt, at zonerings resultaterne

var meget lidt følsomme for ændringer i de økonomiske forudsætninger.

Ændring af måltallet fra 5 til 3 repræsentationer af hver art havde betydelig indflydelse på

resultaterne jf. fx scenarie G i forhold til E eller scenarie O sammenlignet med Q (Tabel 10 &

bilag H). I begge tilfælde opnår ca. 30 flere (12-13 %) prioritet A arter deres måltal med en

kraftigt sænket (-29,5 til -30,4 %) omkostning. Det må så i den forbindelse huskes, at det ab-

solutte beskyttelsesniveau er væsentligt ringere end i 5 repræsentations scenarierne (Figur 3).

Scenarie Q/U/Y med 5 repræsentationer havde fx næsten ligeså mange arter (258/258/258)

repræsenteret 3 gange som de tilsvarende 3-repræsentations scenarier O/V/Z (261/258/258)

og havde derudover mange flere arter dækket 5 gange.

Chapter 4

193

100

Måltal 3 eller 5

Scenarie

Tabs %

% opfyldelse af 5 repræsentationer

Figur 3. De pengebaserede scenarier sorteret efter måltal, relativ opfyldelse af 5 repræsenta-

tionsmålet, og endelig efter tabs %. De mest omkostnings effektive scenarier er til højre.

A priori låsning af syv i forvejen urørte skove (Bilag C) til zone 4 (scenarie Q sammenlignet

med M) medførte forbedret målopfyldelse til det højeste niveau af alle pengebaserede 5-

repræsentations scenarier, idet 231 i stedet for 229 arter nåede målet, og den gennemsnitlige

målopfyldelsesgrad GM steg 0,78 % (Tabel 10 & bilag H). Til gengæld var løsningen 1,1 %

dyrere. Forbedret målopfyldelse var modsat det forventede, eftersom de syv urørte skove,

bortset fra Vorsø, blev besluttet lagt urørt i medfør af Naturskovsstrategien i 1990'erne baseret

alene på den tids tilgængelige data og vurderinger, samtidig med at det ikke var forventet, at

en sådan låsning af Marxans valgmuligheder kunne give en bedre målopfyldelse, end den

løsning Marxan selv fandt optimal. Eksemplet demonstrerer, at Marxan algoritmerne ikke

simpelt maksimerer målopfyldelsen, men finder en nær-optimal løsning balanceret mellem

målopfyldelse og omkostningsminimering, uden at den nødvendigvis er den teoretisk optima-

le. Da målopfyldelse havde høj prioritet, og fjernelse af beskyttet urørt skov ikke var en del af

opgaven, blev alle efterfølgende scenarier kørt med de syv skove låst til zone 4 (Urørt).

Resultatet af scenarie Q med låsning af de syv skove a priori til urørt viser også, at udpegnin-

gerne af urørt skov i medfør af Naturskovsstrategien må have været biodiversitetsmæssigt

gode valg og bedre end tilfældigt, hvilket også fremgik af Rigsrevisionens undersøgelse

(2016) af Miljø- og Fødevareministeriets forvaltning af biodiversitet i statsskovene (figur 4).

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Chapter 4

Det er ikke helt ligetil at kombinere ovenstående resultater til en vurdering af, hvilken kon-

stellation af zoner og skove, der er bedst i relation til omkostningseffektiv beskyttelse af bio-

diversitet og udmøntning af Naturpakken. Det må gøres separat for henholdsvis 3 og 5 repræ-

sentations-målene, med fokus på de realistiske scenarier O til Z med de i forvejen urørte sko-

ve låst til zone 4 (Urørt).

Figur 4. High-Nature-Value (HNV) scorer for arealer, der blev udlagt som biodiversitetsskov

på baggrund af Naturskovsstrategien (rød), for Miljø- og Fødevareministeriets samlede skov-

arealer (grå), henholdsvis for arealer, der blev udlagt på baggrund af Danmarks nationale

skovprogram (blå). Biodiversitetsværdien er ud fra HNV-skovkortet udtrykt ved en HNV-

score på mellem 0 og 19, hvor 19 er det mest værdifulde. Fra Rigsrevisionen (2016).

For tre repræsentationer havde scenarie O og P bedst målopfyldelse og samtidig de tredje og

fjerde laveste økonomiske tab af alle scenarier, hvilket viser, at de var omkostnings-effektive.

Chapter 4

195

For fem repræsentationer havde scenarie Q, U og Y bedst målopfyldelse og identisk zone-

skov fordeling, samtidig med, at de havde lavest økonomisk tab af scenarier med fem repræ-

sentationer. Deres absolutte repræsentation af arter var højere end i scenarie O og P. Alle sce-

narie O skove blev også valgt i scenarie Q, U og Y. I forhold til scenario P var der blot én

zone 2 (Nål) skov, som ikke igen blev valgt i scenarie Q, U og Y. Figur 5 viser et eksempel

fra Nordsjælland på fordelingen af skove i scenarie Q.

Figur 5. Eksempel fra nordøstlige Sjælland på udfaldet til beskyttelseszoner af Marxan opti-

mering, scenarie Q. Alle arealer forvaltet af Naturstyrelsen er vist og fordelt med farve til

zoner. 1 = zone 1 (Normal). 2 = zone 2 (Nål). 3 = zone 3 (Aktiv). 4 = zone 4 (Urørt). Kystlin-

je er udeladt for ikke at skjule polygondetaljer. Helsingør er øverst til højre, Amager nederst

til højre, og Bidstrup-Hvalsø skovene nederst t.v.

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Chapter 4

Den zone-skov konstellation, som scenarie Q, U og Y var fælles om, var upåvirket af variati-

onen i de økonomiske forudsætninger og var på den måde robust. Samtidig havde den mak-

simal absolut målopfyldelse. Tabet ved scenarie Q, U og Y varierede fra 38-46 % af årlig net-

toindkomst afhængig af de økonomiske forudsætninger. Dermed var disse scenarier de mest

omkostnings-effektive scenarier med fem repræsentationer.

Zone-skov konstellationen i scenario Q, U og Y havde 50 skove i zone 4 (Urørt) med i alt

15.281 skovbevoksede hektar. Heraf har 2.256 ha været beskyttet som urørt i hvert fald siden

år 2000, ud over at 2.676 ha i forvejen er udlagt med ekstra beskyttelse for biodiversitet i stil

med zone 3 (Aktiv), fx som græsningsskov, stævningsskov eller plukhugst. Arealet allokeret

til zone 4 (Urørt) kun med hidtidig normal statsskovs beskyttelse i forvejen udgjorde således

10.349 ha sammenligneligt med Naturpakkens ambitionsniveau på 10.000 ha ny urørt skov i

statsskovene. Af ovenstående årsager toges der i det videre arbejde udgangspunkt i zone-skov

konstellationen fra scenario Q (som altså var lig scenarie U og Y).

Samtidig viste analyserne, at behovet for andre typer ny biodiversitetsskov (zone 2 og 3) er

mange gange højere end arealet på 3.300 ha anden biodiversitetsskov besluttet i Naturpakken.

En stor del af de konkrete arealer med 2020-måls-arter, som analyserne foreslog til zone 2 og

3, kunne dog ved tjek mod de detaljerede GIS-data ses at være beskyttet allerede med gamle

driftsformer, § 3-biotop eller på anden vis, eller var baseret på arter med behov for nåleskov,

som langt hen ad vejen kan tilgodeses i Naturstyrelsens almindelige drift. Det viser behovet

for at have fokus på de lys- og varmekrævende truede arter overalt på Naturstyrelsens arealer,

og af at have et fortsat højt generelt beskyttelsesniveau. Mange zone 3 (Aktiv) arter må for-

ventes at kunne få deres økologiske behov opfyldt også på arealer i almindelig drift, herunder

i bryn og bræmmer, samt lysninger og søbredder mv.

Som nævnt var der ingen scenarier, der kunne opnå målopfyldelse for samtlige arter. Selv

scenarie P, som var bedst af 3 repræsentations scenarierne, missede målet for 42 af de 304

prioritet A arter. Disse såkaldte gap-arter forekom i skove, som Marxan havde tildelt en anden

og for gap-arten uegnet zone grundet optimering for andre tilstedeværende arter med andre

økologiske behov. For at opnå en højere samlet målopfyldelse i optimeringen er det nødven-

digt med mere end én beskyttelsestype (zone) i visse skove med gap-arter med modsatrettede

økologiske behov, fx skove med en gap-art tilknyttet nåleskov i en del af skoven og andre

truede arter med behov for fx lysåben egeskov eller urørt løvskov i en anden del.

De arter, som ikke nåede deres måltal, var ens for scenarie Q, U og Y. Disse i alt 73 gap-arter

listes i bilag I med artsdetaljer, og indgik i den følgende proces med yderligere optimering

gennem opdeling af skove til flere zoner.

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197

5.4

Yderligere optimering ved opdeling af skove

Som beskrevet i afsnit 3.2 blev alle artsfund kvalitetssikret og lagt ind i Geografisk Informati-

ons System (GIS), så man kunne se arternes placering i skoven, og med hvilken præcision

lokaliteten var noteret. Hvert artsfund blev kodet med en farve repræsenterende artens præfe-

rence for zone(r). Disse detaljerede GIS data blev brugt til manuel analyse af, hvor gap-

arterne var i forhold til andre truede arter med forskellige økologiske behov, med henblik på

om opdeling af skoven til mere end en zone kunne hjælpe på målopfyldelsen (Figur 6).

Figur 6. Eksempel fra Naturstyrelsens dele af Rold Skov på meget ujævn fordeling af 2020-

måls-arter i skovene. Gap-arter, som ikke i Marxan scenarie Q har fået deres præferencer

opfyldt, vises med trekanter, mens arter, som har nået deres måltal, vises med cirkler. Zo-

ne0_obs er observationer af truede ikke-skovarter.

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Chapter 4

Det fremgår af ovenstående afsnit 5.3 om Marxan optimering for hele skov-polygoner, at det

for 73 gap-arter (Bilag I) ikke kunne lade sig gøre at opfylde artens behov i mindst 5 forskel-

lige skove. I alt 48 forskellige statsskove (Tabel 11 og bilag J) blev derfor gennemgået for

yderligere optimering, fordi de havde forekomst af en eller flere gap-arter, men var tildelt en

zone, som ikke passede med gap-artens behov. I 43 af disse skove blev den Marxan optimere-

de zone ændret til en eller flere andre zoner, mens 5 skove forblev uændret, idet nærmere

granskning viste, at den konkrete gap-arts behov var fejlagtigt eller for snævert defineret (Bi-

lag J). Derved kunne alle prioritet A arter få deres præferencer opfyldt mindst 5 gange eller

alle steder, hvis de kun kendtes fra mindre end 5 statsskove.

Tabel 11. Antal skovpolygoner, som blev vurderet i den manuelle proces. Øverste titelrække

er de zoner, som Marxan scenarie Q tildelte, mens tallene i venstre kolonne beskriver hvilke

zoner, skovpolygonen kunne opdeles i for at tage hensyn til gap-arter. I visse skovpolygoner

blev der yderligere tilføjet et 0 som indikation af, at der yderligere var forekomst af truede

ikke-skovarter, jf. bilag J.

Zoner manuelt

Zone 3

Zone 4

Zone 3 / 2

Zone 4 / 2

Zone 4 / 3

Zone 4 / 3 / 2

I alt

Zone 2 (Nål)

skove

Zone 3 (Aktiv)

skove

Zone 4 (Urørt)

skove

I alt

Der blev ikke beregnet økonomi på den manuelle optimering, fordi økonomi tallene ikke fore-

lå på en form, som tillod opsplitning, og fordi den manuelle gennemgang kun pegede på den

principielle mulighed for yderligere optimering, men ikke pegede præcist på, hvor og hvordan

opdeling kunne ske. Figur 7 viser som kort et eksempel fra den manuelle optimering.

Resultaterne af optimeringsarbejdet blev afleveret på elektronisk form (Excel- og ArcGis-fil)

til Naturstyrelsen i november 2017 til brug for styrelsens udarbejdelse af forslag til udmønt-

ning af Naturpakken. Styrelsen fik samtidig leverancer fra Københavns og Århus Universite-

ter (Johannsen & Schmidt 2017, Petersen et al. 2017) med bl.a. strukturbaserede analyser,

som ved at indgå i styrelsens videre proces styrker beslutningsgrundlaget for hvilke skove, der

er mest egnede til udpegning som fremtidigt urørte skov og anden biodiversitetsskov. Ved at

kombinere analyser baseret på skov-, landskabs- og habitatstrukturer samt skovhistorie kan

der nemlig opnås en uafhængig proxy, som kan bruges til forudsigelse af, hvor der med en vis

sandsynlighed kan forventes forekomst af truede arter (Johannsen et al. 2015).

Til den videre regeringsproces med udpegning af urørt skov og biodiversitetsskov indgik også

hensyntagen til juridiske forpligtelser som fredninger, kulturmindebeskyttelse og vandløbslov

mv, samt andre hensyn som fx friluftsliv og generel omkostningseffektivitet. De endelige ud-

Chapter 4

199

pegninger vil dermed afvige fra de optimerede løsninger foreslået af Marxan og også fra den

efterfølgende manuelle deloptimering. Det er helt i tråd med internationale erfaringer og med

hele ideen med Marxan som et beslutnings understøttende værktøj (Schröter et al. 2014).

Figur 7. Eksempel fra nordøstlige Sjælland på udfaldet til beskyttelseszoner efter manuel op-

timering af scenarie Q. Alle arealer forvaltet af Naturstyrelsen er vist og fordelt med farve til

zoner. 0 = Normal zonen inklusive behov for hensyn til truede ikke-skovarter. 2 = zone 2

(Nål). 3 = zone 3 (Aktiv). 4 = zone 4 (Urørt). Kystlinje er udeladt for ikke at skjule polygon-

detaljer. Sammenlign med figur 5.

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Chapter 4

5.5

Behov for ekstra beskyttelse af ikke-skovarter

Som beskrevet i afsnit 2.5 var det desværre ikke muligt at behandle skovarter og andre arter

samlet i en fælles optimering. I nærværende afsnit omtales resultater for ikke-skovarter med

enkelte koblinger også til skovarterne, som indtil Marxan analyserne beskrevet i afsnit 4.2

blev analyseret sammen med ikke-skovarterne.

Analyserne af både skov- og andre arters geografiske forekomster viste, at der var meget stort

sammenfald mellem forekomst af globalt, nationalt og EU truede arter, idet de habitatområ-

der, som er udpeget på grundlag af habitatdirektivets annex II arter fx repræsenterede 88 % af

alle de andre truede arter og i gennemsnit dækkede 42 til 64 % af samtlige arts-forekomster.

Det på trods af at habitatområderne tilsammen kun dækker ca. 8,6 % af Danmark (marine

områder udeladt). Den hidtidige beskyttelse i form af habitatområder var desuden signifikant

og væsentligt bedre placeret end tilfældigt (Buchwald & Heilmann-Clausen, in prep.).

De nævnte analyser medvirkede til udformningen af prioriteringskriterierne i afsnit 4.4. På

basis af de opstillede prioriterings kriterier blev alle registreringer af truede arter på Natursty-

relsens arealer 1991 – 2015 gennemgået, og de sted-art kombinationer udtrukket, som mat-

chede kriterierne (Tabel 12). Selvom en art evt. kun blev registreret tidligt i årrækken, vurde-

redes det fortsat relevant at opretholde levestedet, idet en del af arterne lever skjult og kan

være overset eller dukke op igen, ligesom andre truede arter ifølge analyserne ofte vil leve

der.

Tabel 12. Fordeling til skovarter og andre arter af sted-art kombinationer, som matcher de

opstillede prioriterings kriterier. Tallene angiver antal sted-art kombinationer, hvor indsats

ud over Natura 2000 indsatsen og anden hidtidig beskyttelse vurderes højt prioriteret for at

sikre levestederne.

Truet hvor?

Globalt

I alt

Skov arter

631

676

Andre arter

306

370

I alt

937

1046

Herefter var fokus på ikke-skovarterne, idet skovarterne, som gennemgået i afsnit 5.3 - 5.4,

blev analyseret uddybende i en særskilt optimeringsproces med inddragelse af økonomien. De

370 sted-art kombinationer for andre arter dækker over 158 arter og 175 forskellige skovnav-

ne (inkl. lysåbne dele) hos Naturstyrelsen. Hver af de 175 skove rummer altså i gennemsnit

ca. to af de omhandlede 158 arter, og maksimalt ti forskellige arter. To forskellige skove

rummer hver ti arter, nemlig Høgdal ved Silkeborg og Skagen Plantage, Nordjylland.

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201

I bilag M listes de 370 sted-art kombinationer for ikke-skovarter sorteret efter Naturstyrelsens

enheder og skovnumre og med oplysning om hver arts biotop, økologiske behov og kendte

trusler. Oplysningerne er forkortet og harmoniseret, så arterne lettere kan overskues og grup-

peres, og medtager primært forhold, som er direkte forvaltningsrelevante for Naturstyrelsens

arealer. Fx er afvanding og bebyggelse ikke medtaget som trusler, da de ikke er relevante

længere grundet styrelsens generelle beskyttelsesregler.

Naturforvaltningen bør målrettes til at sikre levesteder, som matcher arternes biotop og øko-

logiske behov, samtidig med at de for arten angivne trusler begrænses i videst muligt omfang.

Klimaforandringer er et eksempel på en for nogle arter afgørende trussel, som ikke kan be-

grænses lokalt. Hvis biotop og andre økologiske behov optimeres, vil artens overlevelsesmu-

ligheder øges – måske tilstrækkeligt til at kunne klare også klimapresset.

Oversigten for ikke-skovarter blev afleveret elektronisk til Naturstyrelsen i maj 2017 med

henblik på at kunne indgå i den løbende planlægning af beskyttelse og forvaltning af arter.

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Chapter 4

6. Fejlkilder og usikkerheder

Det er vigtigt at notere sig, at data for arterne baseres på registrerede forekomster, dvs. "pre-

sence" data - i modsætning til mere optimale "presence-absence" data. Forudsat korrekt arts-

bestemmelse og kvalitetssikring af tid og sted, kan man regne med artsfundene, men man kan

ikke altid vide om manglende angivelse af en art skyldes, at den reelt mangler på lokaliteten,

eller om den blot er overset, fx grundet manglende besøg af en relevant artsspecialist på rele-

vant årstid (i faglitteraturen benævnes sådanne manglende registreringer af reelle forekomster

som ”false absences”).

Danske data for artsforekomster fra bl.a. citizen science er ikke ligeligt geografisk fordelt, idet

der er en signifikant afhængighed af undersøgelsesintensitet, som i høj grad er koblet til bl.a.

befolkningstæthed (Bladt et al. 2016, Geldmann et al. 2016). Det gælder også data i nærvæ-

rende projekt, som må formodes at have tilsvarende skævheder som figur 8, idet der er et stort

overlap i datagrundlag til Bladt et al. (2016). Af disse årsager er det i hvert fald for skjult le-

vende og vanskeligt kendelige arter urealistisk at skaffe "presence-absence" data.

Figur 8. Aktivitetsniveau for artsregistreringer baseret på et udtræk af alle artsfund fra Fugle

og Natur, Svampeatlas og Danmarks Naturdata (Bladt et al. 2016).

Chapter 4

203

Når arternes forekomstmønster ikke kendes 100 %, bliver de resulterende zone-

konstellationer sub-optimale sammenlignet med en teoretisk ideel situation. Et eksempel er

den globalt truede bille (Ampedus

hjorti),

som er lille, svær at kende og skjult levende, og

dermed kun findes af få specialister. Det gælder tilsvarende en række andre arter, herunder

mange lavarter. Falsk manglende tilstedeværelse, "false absence", er en fejlkilde (omission

error) for sådanne arter, som det er vanskeligt at kvantificere og håndtere. Derimod blev falsk

positive fejl, fx grundet fejl i stedangivelsen, og andre fejl minimeret ved omhyggelig og

langvarig kvalitetssikring af data i PhD-projektet ud over dataleverandørernes egenkontrol.

For at minimere fejl af omission error typen blev dataperioden valgt som 25 år 1991-2015,

således at den inkluderede de systematiske atlasundersøgelser af sommerfugle og karplanter

startet omkring 1991 og alle de efterfølgende atlas-undersøgelser (svampe, fugle, pattedyr,

guldsmede mv.). Hvis ingen har registreret en truet art fra en lokalitet i de nævnte 25 år, er der

større sandsynlighed for, at lokaliteten ikke er vigtig for arten. De fleste skove helt uden så-

danne registreringer er småskove eller ret ung skovrejsning. Selv skove relativt langt fra be-

folkningscentre havde ofte mange registreringer. Fx havde Nørlund Plantage i Midtjylland

mere end 100 truede arter registreret.

Da Aichis mål 12 omhandler kendte truede arter, og fordi det er svært målrettet eller effektivt

at beskytte arters "ukendte" levesteder, vurderedes de anvendte "presence" data generelt for

egnede til opgaven med prioritering af lokaliteter. Vurderingen af om en art er i tilbagegang

eller ej, som der er fokus på i Aichi målet, baseredes på oplysninger i rødlistedatabasen og

kan være af varierende sikkerhed. Dels er den udført af forskellige arts eksperter, dels skønnet

over forskellige årrækker fra 10 til 100 år afhængigt af hver arts generationstid, som heller

ikke kendes eksakt.

Mangelfuld eller mis-forståelse af arternes økologiske behov, præferencer eller udviklings-

tendenser er dermed en potentiel fejlkilde. Den blev minimeret ved at trække på de bedste

tilgængelige systematiske database- og litteratur-kilder og ved i processen at konsultere rele-

vante artseksperter. Alligevel var der nogle enkelte arter, som tilsyneladende havde andre

eller bredere præferencer end de definerede, fx var svampene

Coprinus pannucioides

Leu-

cocoprinus brebissonii

kendt fra skovrejsningsområdet Vestskoven, men ikke fra nogen urør-

te skove, selvom de var forudsat at have behov for zone 4 (Urørt). Denne problemstilling var

efter alt at dømme en undtagelse, men kan ikke udelukkes for andre relativt dårligt kendte

sjældne arter.

Grundet væsentlig usikkerhed om størrelsen af det økonomiske tab ved beskyttelsestyperne

zone 2 (Nål), og zone 3 (Aktiv), blev følsomheden belyst ved at afprøve forskellige kombina-

tioner af økonomisk tab. Heldigvis viste scenarierne, at zone-konstellations resultaterne ikke

blev påvirket væsentligt af valget af økonomisk tab, idet de forblev konstante over en bred

spændvidde af forudsatte tab. Mere detaljeret planlægning for zone 2 og 3 vil være nødvendig

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Chapter 4

skov for skov for at opnå optimal beskyttelse, og hvis der ønskes mere præcise estimater af

økonomisk tab.

For yderligere at afprøve i hvor høj grad ovenstående fejlkilder og usikkerheder kunne påvir-

ke resultaterne for især zone 4 (Urørt), udførtes til sidst tre ekstra scenarier Q1, Q2 og Q3 som

variation over scenarie Q.

Scenarie Q1 blev begrænset til arter overlappende artsudvalget i Petersen et al. (2016b), idet

det artsudvalg blev vurderet som bedst kendt og kortlagt, så der var lavest mulig risiko for

effekter af false absences. Kun 115 af de 304 prioritet A arter var medtaget i Petersen et al.

(op.cit.) og dermed fælles. Det andet ekstra scenarie, Q2, fik som forudsætning, at alle 304

prioritet A arter ville kunne trives i zone 4 (Urørt), ud over den/de zoner, som arten var hen-

ført til i forvejen. Som beskrevet i afsnit 2.5 og 5.2 er der utvivlsomt arter som vil blive nega-

tivt påvirket af en overgang til urørt skov i dogmatisk forstand, men scenariet kan kaste lys

over i hvilken grad en sådan ændring i forudsætninger kan påvirke resultaterne. Endelig fik

varianten Q3 som forudsætning, at alle arter kun havde præference for zone 4 (Urørt), for at

kunne vurdere hvor meget omkostningerne påvirkes af zonerings tilgangen.

Scenarie Q1 pegede på 33 statsskove til zone 4 (Urørt) (12.771 skovbevoksede ha) i stedet for

de 50 skove med 15.281 ha i scenarie Q (Tabel 13). Scenarie Q1 havde 138 gap-arter, jf. tabel

14, og dermed lav målopfyldelse i relation til de 304 prioriterede arter. Af markante forskelle i

valg af skove kan nævnes, at scenarie Q1 i forhold til scenarie Q zone 4 (Urørt) udelod 12

skove helt og skiftede zone for andre ni skove, herunder til zone 3 for de meget artsrige og

velundersøgte Møns Klinteskov, Buderupholm Nørreskov, Nørreskoven i Farum og Teglstrup

Hegn (Bilag K). I alt 84 scenarie Q skove blev helt udeladt fra scenarie Q1 (dvs. kom i zone

1), fx Kobskov og Østerskov m.fl. ved Silkeborg, Hald Inderø, statsskovene på Nordborn-

holm, skovene omkring Gurre Sø, Ryget og Charlottenlund Skov. Til gengæld medtog scena-

rie Q1 følgende syv skove, som ikke var med i scenarie Q: Gilbjerggård (zone 3), Krogenlund

(zone 3), Ålbæk Plantage (zone 2), Grishøjgårds Krat (zone 3), Ejerslev Vang (zone 3), Ny-

strup Plantage vest (zone 2) og Hjardemål Plantage (zone 2) (Bilag K).

Tabel 13. Sammenligning af scenarie Q1, Q2 og Q3 med Q.

Mål

nået

(af 304

arter)

231

166

Arter

Scenarie

Antal

arter

med

304

115

304

Tabs

forud-

sætninger

intermediære

Skove

z4 årligt

areal areal areal areal

tab

(ha)

(n)

15.281 50

12.771 33

26.119 69

56.679 203

NA = Ikke relevant. z = zone. Ikke angivne forudsætninger er lig scenarie Q (se tabel 3). De 146

skove, som ikke fik ens zone i disse tre scenarier, kan ses i bilag K med arealer og zoner.

Chapter 4

205

Forskellen mellem scenarie Q1 og Q tilskrives især det betydeligt mindre antal arter, som

indgik i Q1 optimeringen. Hvis man i stedet ser på om scenarie Q formåede at dække de 115

godt kendte arter fra scenarie Q1, viser tabel 14, at der var 28 gap-arter mod 16 i scenarie Q1.

Ingen af scenarierne formåede at få dækket alle de globalt truede arter eller EU-arterne. Hvis

man ser nærmere på tallene i tabel 14, fremgår det, at især laver og andre invertebrater får

mindsket andelen af gap-arter markant fra scenarie Q1 til Q og dermed repræsenterer noget

andet end de arter, der er med i Q1. I hvert fald for laverne giver det god mening, da der er

tale om en artsgruppe med komplementære habitatkrav til de fleste andre arter. Det er dog

også en gruppe, hvor datadækningen er dårlig. Omvendt er planterne helt ens hvad angår gap-

arter i de to scenarier, hvilket viser, at scenarie Q ikke er bedre for planter end Q1.

Tabel 14. Fordelingen af arter og gap-arter i scenarie Q1 i forhold til scenarie Q.

Antal arter

DK_truet

EU truet

Globalt truet

Pattedyr

Fugle

Padder

Insekter

Andre invertebrater

Svampe

Laver

Mos

Planter

I alt

288

122

304

110

115

GapQ-304

GapQ1-304

132

138

GapQ-115

GapQ1-115

De første to kolonner viser antallet af prioritet A arter, som hvert scenarie blev optimeret ud fra. De

fire højre kolonner viser, hvor mange af de 304 henholdsvis 115 arter, som ikke fik deres behov op-

fyldt det målsatte antal steder og dermed kaldes Gap-arter. De sammenlagt 155 gap-arter listes i bilag

L med detaljer.

Scenarie Q2 pegede på 69 statsskove til zone 4 (Urørt) (26.119 skovbevoksede ha) i stedet for

de 50 skove med 15.281 ha, som scenarie Q viste (Tabel 13). Det økonomiske tab var større

end for scenarie Q (48,1 % tab mod 46,1 %) og arealbehovet til urørt ca. dobbelt så stort som

Naturpakkens ambitionsniveau. I det lys er det kun lidt større indtægtstab overraskende. For-

klaringen ses af tabel 13 og bilag K, idet Scenarie Q2 har betydeligt mindre arealer og antal

skove i zone 2 og 3 end scenarie Q, hvilket altså er tæt på økonomisk at opveje de større area-

ler i zone 4.

Scenarie Q3 varianten, hvor beskyttelse kun var mulig i zone 4 (simulerende ingen zonering),

henførte 203 skove til beskyttelse med et tab på 58,9 % af årlig netto indkomst fra høst af træ,

sammenlignet med 46,1% for scenarie Q (Tabel 13). Besparelsen ved zonering svarer dermed

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Chapter 4

til 21,7 % i forhold til, hvis al beskyttelse skulle ske som urørt skov. Hvis de billigere om-

kostnings forudsætninger i scenarie U og Y for zone 2 og 3 er mulige at realisere, vil bespa-

relsen blive proportionalt højere.

Scenarie Q2 og Q3 havde ingen gap-arter, men det bør ikke tillægges vægt, idet de to varian-

ter som nævnt fik som bunden forudsætning, at alle arter kunne trives i zone 4 (Urørt) uanset

viden om at dette næppe er tilfældet under de aktuelle forudsætninger. De fleste af scenarie

Q2's ekstra 19 skove i zone 4 (Urørt) kendetegnes da også af arter og biotoper, som ikke nor-

malt forbindes med urørt løvskov, fx plantagerne Hoverdal, Fejsø, Vilsbøl, Rø og Hornbæk,

foruden Hanstholm Kystskrænt, Mols Bjerge, Markerne ved Kalø, Ølene og Vestamager.

Scenarie Q1, Q2 og Q3 viste ikke overraskende, at resultaterne påvirkes af hvor mange arter,

der indgår, og hvilke præferencer, de tillægges, men ændrede ikke på vurderingen af, at sce-

narie Q havde de mest relevante forudsætninger og resultater.

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7. Anbefalinger om indsatsen i skov

Der er i den videnskabelige litteratur undersøgt mange indsatser, som kan bidrage til beskyt-

telse af biodiversiteten i skov (review i Bernes et al. 2015), herunder mange anbefalinger af at

udlægge mere skov som urørt skov eller anden biodiversitetsskov, samt generelt øge mæng-

den af dødt ved. Hvilken type dødt ved, der er tale om, kan være afgørende og meget artsaf-

hængigt (Heilmann-Clausen & Aude 2006, Heilmann-Clausen & Christensen 2003 & 2004,

Heilmann-Clausen et al. 2014, Stokland et al. 2012).

På baggrund af den gennemgåede litteratur suppleret med upublicerede erfaringer fra ud-

møntningen af Naturskovsstrategien i 1990'erne, samt nærværende projekts omfattende

granskning af truede arters forekomst, økologiske behov og præferencer, gives i det følgende

nogle overordnede anbefalinger for indsats i overgangsperioden og for anden biodiversitets-

skov. Der er tale om forfatternes originale syntese af det samlede arbejde, og der gives derfor

ikke særskilte litteraturreferencer.

Anbefalingerne tager udgangspunkt i, at formålet er at standse tilbagegangen i biodiversitet,

jf. de nationale og internationale politiske aftaler herom. Der tages desuden udgangspunkt i

den økonomiske forudsætning i Naturpakken, at der skal fældes og sælges en betydelig

mængde træ fra skovene, inden de overgår til urørt status. Naturstyrelsen har endvidere bestilt

en særskilt rapport, som skal komme med yderligere anbefalinger til indsatsen, og som selv-

følgelig anbefales konsulteret, når den kommer.

Med regeringens Naturpakke er der besluttet en meget væsentlig forøgelse af arealet af dansk

skov beskyttet af hensyn til biodiversitet. Placeringen af de ny beskyttede skove anbefales

fastlagt på baggrund ikke kun af analyserne i nærværende projekt, men også under hensynta-

gen til bl.a. øvrige analyser leveret af Aarhus og Københavns Universiteter, samt til kulturhi-

storiske, friluftsmæssige og økonomiske værdier. Nærværende analyser bør nemlig ikke op-

fattes som et facit, men som input til beslutnings støtte.

I lyset af at Naturpakkens ambitionsniveau med hensyn til urørt skov passer godt til resulta-

terne af scenarie Q i nærværende projekt, mens niveauet for anden biodiversitetsskov areal-

mæssigt er lavere end summen af zone 2 og 3, anbefales det, at hensynene til truede arter med

præference for nål eller lysåbne forhold indarbejdes i Naturstyrelsens almindelige beskyttel-

sesindsats i det omfang, arealerne ikke udpeges som anden biodiversitetsskov. En lang række

af arterne findes allerede i generelt beskyttede delarealer i skovene eller er tilknyttet nåletræ-

er, således at de vurderes mange steder at kunne beskyttes i den almindelige arealforvaltning.

For ny urørt løvskov er der i Naturpakken fastsat en overgangsperiode på 10 år inden urørt

status, for nåletræsplantager en overgangsperiode på 50 år, mens løbende indsatser kan foregå

i anden biodiversitetsskov. Det bliver dermed afgørende, hvordan den konkrete praktiske for-

valtningsindsats implementeres i henholdsvis overgangsperioden og efterfølgende.

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Chapter 4

7.1 Indsats i overgangsperioden og i anden biodiversitetsskov

Målret indsatsen

til stedets truede arter eller artsgrupper og deres præferencer. Da arterne har

forskellige økologiske behov betyder det, at der kan være variation i tilgangen. På steder med

arter med behov for dødt ved bør mængden af dødt ved fx øges, således at mere træ forbliver i

skoven som dødt ved, og mindre træ sælges. I gamle skovbevoksninger med dødtveds af-

hængige arter bør hugst undlades eller holdes på et minimum. I skove med arter, der behøver

skovlysninger, fx truede sommerfugle, bør modsætningsvis større områder i skoven konverte-

res til lysninger, fx ved fældning og fjernelse af monotone forstlige yngre træbestande.

En væsentlig del af skovarterne trives bedst eller kun i skovlysninger eller i

bløde overgange

mellem skov og helt lysåbne biotoper. Den menneskeskabte opdeling mellem skov og lysåbne

biotoper i landskab og forvaltning er uheldig for mange arter og bør modvirkes ved i videst

muligt omfang at samtænke forvaltning af skov med lysåbne biotoper. Det kan fx ske ved

samgræsning af større områder, som rummer både træbevoksede og lysåbne arealer.

Græsning

er en essentiel funktion for økosystemet og for mange af de truede arter. Det anbe-

fales i videst muligt omfang at etablere eller videreføre naturlige græsningsniveauer i biodi-

versitetsskovene. Her er det vigtigt at notere at et hårdt græsningstryk i sommerhalvåret kan

være ligeså skadeligt som mangel på græsning for bl.a. truede sommerfugle. Som udgangs-

punkt anbefales et ret lavt helårs græsningstryk i stil med det i Tofte Skov (Lille Vildmose)

idet undersøgelser tyder på, at det er noget af det nærmeste, man i Danmark kommer et natur-

ligt græsningstryk, og det giver en god balance mellem træer/skov og lysåbne biotoper, sam-

tidig med gode forhold for en meget lang række truede dyr og planter. Se også afsnit 8.3 for

mere om græsning.

Kontinuitet

i tid og rum er tilsvarende meget vigtig. Dvs. at skovpartier med meget høje na-

turværdier som udgangspunkt bør videreføres uden væsentlig hugst eller andre store foran-

dringer (bortset evt. fra mere naturvenlig græsning), idet den hidtidige forvaltning åbenbart

har været god for arterne. For at skabe større kontinuitet i rum anbefales sådanne hotspots

understøttet ved at skabe grundlag for udvikling af tilsvarende kvaliteter på tilgrænsende area-

ler, så det samlede areal med god kvalitet øges.

En række undersøgelser har efterhånden påvist den store naturværdi ved genskabelse af mere

naturlige vandforhold i skove, så genopretning af

hydrologien

ved lukning af grøfter (også

dem, som ser tørre ud det meste af tiden) bør gennemføres konsekvent og effektivt. Vådere

forhold og større sæsonudsving i vandstand er meget vigtige for en lang række truede arter, og

medvirker desuden til mere lys og større variation i skoven og til mere dødt ved.

Det bør som udgangspunkt tilstræbes at skabe eller øge skovens

strukturelle variation,

både i

stor og lille skala, dvs. skabe variation på skov- og bevoksningsniveau i træartssammensæt-

ning, tæthed og aldre. Især i stærkt forstligt prægede bevoksninger bør der føres en meget

Chapter 4

209

markant anderledes hugst end normalt ved at modvirke den homogenisering, som den forstli-

ge praksis har medført. Sørg fx for plads nok til at enkelttræer og trægrupper kan vokse frit og

udvikle store grovgrenede kroner under lysstillede forhold. Skab større lysbrønde (diameter

større end træhøjde) i ensartede bevoksninger, for at skabe et mere varieret grundlag for frem-

tidig dynamisk og strukturel udvikling. Undlad udtynding i andre partier for at skabe fremti-

dens tykninger. Undlad at hugge afvigerne i en bevoksning, dvs. værn om de største og de

mindste træer, samt de træ- og buskarter, som der i den konkrete bevoksning er få af.

Fremme af

dødt ved

i skoven kan generelt anbefales, men undersøgelser har vist, at det især er

levende træer med dødt ved og/eller hulheder, samt selvdøde træer knækket ved roden, som

huser truede arter. Kunstigt frembragt dødt træ ved hjælp af fx motorsav eller ringbarkning er

tilsyneladende ikke et ligeså gunstigt substrat for truede arter, men erfaringsgrundlaget er be-

skedent. Kunstigt frembragte hulheder eller brandskader på levende træer har dog vist sig at

tiltrække rødlistearter.

Ved hugsten bør de

træer skånes,

som har flest synlige skader, bugtninger, grove grene eller

lignende, idet det giver biologisk værdi eller potentiale. Til gengæld kan hugsten fint udtage

økonomisk værdifulde træer, idet disse typisk har mindre biologisk værdi ved at være fri for

huller, råd og store knaster mv. De største / ældste træer i hver bevoksning bør skånes for

hugst og bevares som

Livstræer. Gamle ege,

som presses af nabokonkurrence fra fx bøg, bør

hjælpes fri af konkurrencen ved fx hugst eller ringbarkning af nabotræerne, idet gamle ege

kan have stor biodiversitets betydning, men alt for ofte dør i konkurrencen med nabotræer.

Generelt bør indsatsen værne om og fremme

buske

lyskrævende træer,

som har stor betyd-

ning for biodiversiteten, men har langt mindre udbredelse i moderne skove, end de havde i

historiske og forhistoriske mere naturlige skove. Det gælder ikke mindst insektbestøvede arter

og pionertræarter som asp, birk, eg, pil og skovfyr. Asp har i modsætning til de andre nævnte

en relativt dårlig koloniseringsevne og var stærkt forfulgt i meget lang tid, så den er tæt på

udryddet i mange skove. Det kan overvejes at udbrede den til mange flere steder, fx ved ud-

plantninger eller stiklinger med lokalt / regionalt materiale.

Bevoksninger med træarter fra

fremmede

kontinenter har normalt lav værdi for biodiversite-

ten, især hvis de tilhører slægter, der ikke findes i Europa, men kan have stor økonomisk vær-

di. Hvis ikke der er særlige artsforekomster, som kan begrunde andet, anbefales sådanne be-

voksninger afviklet i overgangsperioden, så arealet kan overgå til urørt naturlig tilgroning,

hvilket som regel i kortere eller længere tid vil skabe en skovlysning, og derefter en blandet

skov af selvsåede træer og buske. Eksempler på sådanne

fremmede træarter

er rødeg, robinie,

sitkagran, douglasgran, grandis og thuja.

Bevoksninger med

træarter fra Europa

har normalt større værdi for biodiversiteten og bør

vurderes konkret i forhold til deres betydning for stedets truede arter. Da 110 af de 626 truede

skovarter har præference for

nåletræer,

og en række af de øvrige arter trives med blandet løv-

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Chapter 4

nål skov, er der basis for at opretholde et vist kontingent af europæiske nåletræer i skovene -

ikke mindst skovfyr og rødgran, men også lærk, ædelgran og bjergfyr har tilknyttede truede

arter. Massive plantede bevoksninger af rødgran bør vurderes konkret i forhold til skovens

truede arter, idet visse arter har glæde af renafdriftsrydninger efter gran, mens andre arter, fx

den globalt truede blank gæstemyre, har behov for tuer af rød skovmyre, som typisk trives

godt ved udkanten af granskov og måske forsvinder, hvis granskoven ryddes.

Generelt anbefales et

europæisk biogeografisk perspektiv

i lyset af, at arterne ikke respekterer

politiske landegrænser og ikke har nået at udfylde deres potentielle naturlige udbredelser efter

istiden. Arternes indvandring til Danmark sydfra var en igangværende dynamisk proces også

før de tiltagende klimaændringer, men er accelererende. Etablering og udbredelse af europæi-

ske arter bør derfor ikke opfattes som et invasivt problem i skovene, men som at arterne er i

proces med

klimatilpasning.

Som følge af de accelererende

klimaændringer

er det sandsynligt, at visse arter især med

nordlig eller kontinental hovedudbredelse går tilbage eller ligefrem forsvinder fra Danmark.

Det anbefales at prioritere konkret indsats for sådanne klimamæssigt marginale arter lavere

end for andre truede arter, hvis de vides at være talrige og udbredte i nabolande mod nord og

øst.

En mindre del af de truede arter har behov for

brand

i form af brændt træ eller brandpletter

efter kvasbrænding. Generelt er brand og ild i skoven blevet meget sjældnere end i mange

tusinde år. Øget anvendelse og accept af ild og brand anbefales derfor på steder og i et om-

fang, som ikke medfører sikkerheds risici, fx ved anvendelse af små bål ved foden af enkelt-

træer for at give dem brandskade (simulerende bundbrand) og ved genoptagelse af afbrænding

af kvasbunker på afdrifter i et vist omfang. Brand har antageligt også et stort potentiale som

forvaltningsværktøj i overgange mellem skov og åbent land, hvilket bør undersøges nærmere.

For enkelte arter, fx billen eremit (Osmoderma

eremita)

vides det med sikkerhed, at

spred-

ningsevnen

er ekstremt lille i forhold til

isoleringsgraden

af eksisterende forekomster. Risiko-

en for at

indavl

eller tilfældige faktorer i sådanne tilfælde ødelægger mulighederne for lang-

sigtet overlevelse er overhængende. For sådanne arter bør indsatsen tage udgangspunkt i de

bestande og meta-populationer, der umiddelbart har størst levedygtighed, hvilket kan omfatte

undersøgelser af graden af indavl. Derudover kan man undersøge mulighederne for kunstig

udveksling af individer mellem forskellige forekomststeder under kontrollerede forhold.

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211

7.2 Urørt skov efter overgangsperioden

Det anbefales, at de ny urørte skove efter overgangsperioden ikke forvaltes dogmatisk som

fuldstændig urørte. Der kan dels være behov for indgreb for at sikre passende græsning, dels

indgreb af hensyn til at afværge trusler fra fx invasive arter eller for at medvirke til gunstig

bevaringstilstand for Natura 2000-arter eller -naturtyper. I tilfælde, hvor der er behov for an-

dre aktive forvaltningstiltag end rent sikkerheds betingede tiltag, anbefales en plan for indsat-

sen fastlagt, fx som driftsplantillæg.

Det afgørende bør derefter være, at de udpegede arealers

formål

fremadrettet er fremme og

sikring af biodiversitet. Status som urørt skov kan ses som et middel til fremme af formålet

ved at give plads og mulighed for at de økologiske naturlige processer og funktioner kan ud-

folde sig frit. De udpegede arealer kan således med andre ord og for at svare mere til interna-

tional terminologi opfattes som biodiversitets-reservater uden adgangsbegrænsning for publi-

kum, men i øvrigt af den internationale type "minimum-intervention".

Det skal i den sammenhæng fremhæves, at kombinationen af urørt skov og græsning, eller

med andre ord urørt græsningsskov, generelt bør fremmes, idet det vurderes som den mest

effektive måde at genoprette de relevante økosystemprocesser og funktioner, som har betyd-

ning for mange truede arters overlevelse og trivsel. Hvis den urørte skov er stor og både inde-

holder nåletræer, løvtræer, lysåbne biotoper og forvaltes med ekstensiv naturvenlig græsning,

vil det potentielt være muligt at tilgodese langt de fleste truede (og almindeligere) arters præ-

ferencer og behov indenfor området. Et godt eksempel er Tofte Skov området i Lille Vildmo-

se, men også en række andre gamle dyrehaver har store kvaliteter. Tidsfaktoren og arealskala

er i den sammenhæng vigtige.

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Chapter 4

8. Anbefalede indsatser for lysåbne biotopers arter

Som nævnt i afsnit 5.5 listes de mere eller mindre ubeskyttede forekomster af arter i priori-

tetsklasse 1 til 3 i bilag M, men også på beskyttede steder kan indsatsen for truede arter i en

del tilfælde forbedres. Generelt vil arter i prioritetsklasse 1 og 2 være vigtigere end de øvrige

arter i relation til 2020-målene. De 33 ikke-skovlevende arter i prioritetsklasse 1 og 2 listes

derfor i bilag N. Forekomststeder hos Naturstyrelsen for disse arter, som ikke er med i bilag

M, er allerede underlagt en eller anden form for beskyttelse og forvaltning. Det anbefales lo-

kalt at tjekke, at forvaltningen særligt sikrer gode økologiske forhold for de nævnte 33 arter

og justere indsatsen, hvis det ikke er tilfældet. Tilsvarende gælder de i 2020-sammenhæng lidt

lavere prioriterede 277 dansk truede ikke-skovarter (prioritetsklasse 3). Allerede mere eller

mindre beskyttede forekomster af dem fremgår af de digitale data, som leveredes til Natursty-

relsen som et delresultat af PhD-projektet. Det store antal arter kan gøre det vanskeligt at

overskue på landsplan, men for den enkelte skov er antallet af arter typisk lavt, så indsats kan

tilrettelægges målrettet.

Med hensyn til detaljerede anbefalinger til indsats og driftsformer kan især følgende grundige

og brede sammenstillinger anbefales, idet de også uddyber og giver dokumentation for indsat-

serne: Bunzel-Drüke et al. (2008), Buttenschøn (2007), Damgaard et al. (2007) og Fløjgaard

et al. (2017), samt for heder og klitter Stenild et al. (2016). I det følgende gennemgås nogle af

de vigtigste forhold og anbefalinger.

8.1

Supplerende oplysninger

Som følge af det store antal arter, som er truet på landsplan, er det ikke muligt at gå i større

detaljer i nærværende rapport. På lokalt niveau for den enkelte skov vil artsantallet være me-

get lavere, så Naturstyrelsens lokale enhed kan og bør søge supplerende mere detaljeret in-

formation om de enkelte arters økologiske behov, om truslerne, og hvordan de bedst kan af-

hjælpes. For langt de fleste arter er en del oplysninger fx tilgængelige ved at klikke sig frem

til den relevante art i den elektroniske rødliste på nettet: http://bios.au.dk/videnudveksling/til-

jagt-og-vildtinteresserede/redlistframe/artsgrupper/ . For hver art findes i rødlisten også hen-

visninger til yderligere kilder og litteratur. Samarbejde med vidende lokale arts entusiaster og

naturforeninger anbefales også, da det i mange tilfælde kan bidrage med vigtige oplysninger

om arternes lokale status og udviklingstendenser. Endelig kan mange oplysninger findes i

online databaserne listet i bilag A.

8.2

Justeret drift og forvaltning

I mange tilfælde vil den nødvendige forvaltning bestå i fortsættelse af hidtidig ekstensiv

græsning, slæt, afbrænding eller anden form for indsats, som har opretholdt de lysåbne bioto-

per mere eller mindre næringsfattige og forhindret tilgroning. De angivne arter er dog truede i

et omfang, som viser, at de generelt ikke trives med standard drift, som fx slåning (uden fjer-

nelse af materiale). Slåning er naturmæssigt betydeligt ringere end slæt (hvor materialet fjer-

Chapter 4

213

nes) for i hvert fald en række blomsterplanter, dagsommerfugle og andre insekter, bl.a. fordi

materialefjernelse sørger for tilpas næringsfattige forhold og modvirker landskabets generelle

eutrofiering (Buttenschøn 2007, Damgaard et al. 2007, Hartvig 2015, Stoltze 2005), jf. afsnit

2.5.

Arealet af bløde

overgange

mellem skov og lysåbne biotoper blev, jf. afsnit 2.5 og 3.4, stærkt

formindsket gennem 1800-tallet og er fortsat meget lavt til skade for en række truede arter,

som trives sådanne steder (Buchwald 2012, Bunzel-Drüke et al. 2008, Baagøe & Jensen 2007,

EEA 2013, EIONET 2013b, Hartvig 2015, Stoltze 2005, Søgaard & Asferg 2007, Wind &

Pihl 2010). Samgræs derfor så vidt muligt skov og lysninger. Opblød skovbryns skarpe linjer.

Også på andre måder er

variation

i drift og arealer gået fra småskala til storskala ved ensartet

drift af store flader, bl.a. gennem dræning, jordbearbejdning, og i nogle tilfælde ens naturpleje

af store områder, fx kratrydning på heder, græsland og kær. På steder med truede insekter vil

fremme af småskala variation typisk være en forbedring. Lad fx hjørner og kanter afvige og

undlad at græsse/slå/brænde/hugge 100 % af litra/afdeling.

Kontinuitet

i tid og rum er meget vigtig for mange arter (Nordén et al. 2014). Det indebærer

videreførelse af de forhold, som har gjort det muligt for arten at leve på stedet, foruden at op-

retholde tilstrækkelig store levesteder og spredningsmuligheder. Brud på kontinuitet, fx ophør

af græsning eller slæt, kan i løbet af kort tid (ofte et par år) føre til lokal uddøen af truede ar-

ter. Såfremt data viser stabil eller stigende lokal bestand, og driften har været stabil i en år-

række, vil det som udgangspunkt være bedst at videreføre den hidtidige drift uforandret. For-

bedring kan sådanne steder gennemføres ved udvidelse med egnede arealer i nærheden.

I det følgende gives yderligere kommentarer til nogle af de mest relevante indsatser, som kan

bruges til at forbedre eller opretholde levestederne, hvis der er tegn i data på, at den hidtidige

indsats ikke har formået at opretholde en stabil eller stigende lokal bestand. Det er forsøgt at

uddrage den mest forvaltnings relevante essens af tilgængelige kilder. Indsatsen bør i videst

muligt omfang ses i sammenhæng med skovindsatsen, så der opnås en mere holistisk tilgang.

8.3

Græsning

Ekstensiv

græsning er en meget vigtig indsats, og bør så vidt muligt baseres på at genskabe

naturlige dynamikker og funktioner af græsning, men især hvis arealerne er små, kan det være

vanskeligt at gennemføre i praksis (Assmann & Falke 1997, Bokdam & Gleichman 2000,

Buchwald 2012, Bunzel-Drüke et al. 2008, Buttenschøn 2007, Baagøe & Jensen 2007, EIO-

NET 2013a, EIONET 2013b, Fløjgaard et al. 2017, Fredshavn et al. 2014, Hartvig 2015, Ma-

roo & Yalden 2000, Nielsen 2009, Nielsen & Buchwald 2010, Vikstrøm et al. 2015, Wind &

Pihl 2010, Wind & Ejrnæs 2014). Oftest tåler de truede arter ikke den form for græsning, som

praktiseres med EU-tilskud, idet den typisk er for intensiv. En mere naturvenlig og svag

græsning er nødvendig ikke mindst på levesteder for de truede sommerfugle (og en række

andre arter). Der er for mange eksempler på lokal uddøen som følge af for hård græsning, som

214

Chapter 4

i landbrugstilskuds optik i hvert fald tidligere har været "normal græsning". Disse arter for-

svinder længe før arealet er græsset i bund. Der behøves et mere naturligt og dynamisk græs-

ningstryk, som tillader/udvikler et rigt blomsterflor af mange blomstrende arter hen over sæ-

sonen. Et tegn på succes er at væsentlige dele af græsgangen ser "pelset" eller "langhåret" ud

gennem sæsonen og at der efterlades vinterstandere af diverse urter.

Ekstensiv helårsgræsning og samgræsning af flere slags dyr anses ofte for bedst for naturkva-

liteten knyttet til lysåbne arealer, men det er vigtigt for de fleste truede arter at undgå hård

græsning, som nemt bliver resultatet bl.a. i det tidlige forår, hvis arealerne er små eller dyre-

tætheden for stor. Græsning kombineres ofte med tidvis hugst af dele af træ- og busk opvæk-

sten, så der opretholdes et mix af småkrat og træer i græsgangen uden at vedplanterne tager

overhånd. Vedplanterne har mange positive natureffekter, og bør derfor for at undgå kontinui-

tetsbrud normalt ikke ryddes længere ned end til fx 10 % dækning af arealet, hvis der er tradi-

tion for, at der har været vedplanter i græsgangen.

Heste, køer, hjortedyr, geder og bison er udmærkede forudsat passende lavt græsningstryk.

Får anses normalt for mindre egnede til naturpleje, idet de spiser blomsterne før græsset, så

mængden af blomster falder. Ekskrementer fra græsningsdyr er af essentiel betydning for flere

af de truede arter – ofte arter med "møg" eller "gødning" i navnet. På steder, hvor sådanne

arter lever, er det således afgørende ikke at afbryde græsningen et år, ligesom helårsgræsning

kan være essentiel. Tilsvarende er der en række medikamenter som husdyrene ikke bør få, da

de gør ekskrementerne golde eller giftige for insekterne (Bunzel-Drüke et al. 2008, Wind &

Pihl 2010).

8.4

Høslæt

Traditionel høslæt var igennem århundreder overordentligt udbredt i landskabet (inklusive

skovene) og har spillet en stor rolle for visse lavtvoksende karplanter og ikke mindst dag-

sommerfugles store hyppighed, indtil høslæt efterhånden nærmest ophørte i årene efter 1950

(Buchwald & Vikstrøm 1991, Buttenschøn 2007, Hansen & Vikstrøm 2005, Hartvig 2015,

Jørgensen 2005). En effekt af høslæt var skabelsen og opretholdelsen af næringsfattige blom-

sterrige arealer med meget lav forstyrrelse gennem forår og forsommer indtil selve slættet.

Årligt slæt fjerner store konkurrencestærke plantearter, dels omkring 100 kg N/ha/år, hvilket

er 5–10 gange mere end eutrofieringen fra atmosfæren, så der skabes næringsfattige lysåbne

forhold, hvilket mange truede arter har behov for (Damgaard et al. 2007, Hicks et al. 2011,

Stenild et al. 2016).

Sammen med småskalavariation i det gamle landbrugslandskab med mindre græsningsfolde

og små agre gav det gode levesteder for mange arter. På steder med truede arter af perlemor-

sommerfugle, blåfugle, pletvinger eller køllesværmere vil det være vigtigt for redning af fal-

dende bestande at sikre et tilsvarende mix af forskellige successionsstadier og driftstyper med

ikke for store flader behandlet intensivt samtidig (EEA 2013, Eskildsen 2015, Eskildsen et al.

Chapter 4

215

2015, Stoltze 2005). Bestanden kan uddø, hvis hele dens areal i løbet af kort tid gives samme

behandling, hvis behandlingen fx er intensiv græsning eller afbrænding (Wind & Ejrnæs

2014, Wind & Pihl 2010) og det er endnu uvist om ekstensiv helårsgræsning på kort sigt kan

opretholde passende levesteder, hvis udgangspunktet er det nuværende kulturlandskab. Hø-

slæt bør af ovenstående årsager ikke høste det samlede areal af en eng indenfor samme uger,

når bestandsbevarelse er formålet. Opdeling på et eller flere delarealer med tidligt slæt (typisk

2. halvdel af juni) og andre arealer med sent slæt (august – september) kan være en løsning

eller kombination med efterladen af mindre dele uslået visse år. Ifølge samtaler med gamle

bønder (Buchwald upubl., Jørgensen 2005) startede det traditionelle høslæt i tiden omkring

Skt. Hans og fortsatte i mindst 7 uger af sommeren for at nå ud til alle engene.

I gamle dage var der derfor gennem hele sommeren enge i forskellige faser af modning og

genvækst, hvilket har været gavnligt for bl.a. sommerfugle (Stoltze 2005). Maskinelt slæt i en

bræmme langs skovveje og stier kan være en relativt billig og effektiv måde til at opnå både

levesteder og spredningsveje for truede planter, sommerfugle og andre arter (Jørgensen &

Karlog 2005).

8.5

Andre indsatser

Afbrænding

fjerner ligesom høslæt meget kvælstof og andre næringsstoffer og medvirker der-

for til næringsfattige forhold (Damgaard et al. 2007, Hicks et al. 2011, Stenild et al. 2016).

Samtidig øger det pH og kan fjerne et litter / humuslag, samt fremme pyrofile arter. Hede eller

hedeagtige biotoper blev i urskovstiden skabt af brande (Odgaard 1994). Den mest naturlige

hedepleje er derfor brand, men naturbrand forekom også i andre naturtyper. Visse specielle

insekter og svampe er tilpasset til det og kan finde frem på lang afstand (Wind & Pihl 2010).

Det kan derfor være godt for en række arter at brænde arealer eller kvas med jævne mellem-

rum. Også træer såret af brand kan tiltrække særlige truede arter, hvilket bl.a. er set ved

igangværende forsøg i Gribskov, hvor små bål blev tændt ved foden af bøgetræer (egne upubl.

undersøgelser).

En række af de truede arter er tilknyttet vandhuller og andre typer af

småvande.

Danmark

mistede en meget stor del af sine vandhuller og småvande mellem 1945 og 1990, og i den

forbindelse forsvandt op til 90 % af levestederne for de mest truede paddearter (Fog 1993). Et

par årtiers naturgenopretning af småvande har vist, at truede arter som padder og guldsmede

hurtigt gavnes af ny vandhuller. Naturstyrelsens løbende indsats for naturlig hydrologi og ny

søer er derfor vigtig at holde fast i og videreføre. I den forbindelse er genskabelse af naturlige

udsving i vandstand meget vigtig, idet en række af de mest truede arter netop er afhængige af

vandstandssvingninger og den tidvis oversvømmede zone (EIONET 2013b, Fredshavn et al.

2014, Hartvig 2015, Søgaard & Asferg 2007, Vikstrøm et al. 2015, Wind & Pihl 2010).

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Chapter 4

Skovene har desuden relativt få

insektbestøvede

træer og buske i dag. Forvaltningen bør der-

for så vidt muligt fremme buske og træer med blomster til insekter (Buchwald 2012, Bunzel-

Drüke et al. 2008, Wind & Pihl 2010).

8.6

Økonomi

Det har ikke været muligt at beregne konkret økonomi på enkelte indsatser, da de lokale for-

hold er for forskellige. Omkostningerne er derfor tilsvarende variable, bl.a. som følge af for-

skelle i arealstørrelse, driftsform og naturtype, jf. tabel 15, som stammer fra den nyeste grun-

dige analyse af emnet.

Tabel 15. Oversigt over driftsøkonomiske omkostninger (netto) ved plejeformer på naturarea-

ler uden tilskud, kr./ha i 2009 prisniveau (fra Dubgaard et al. 2012). Bemærk den store af-

hængighed af arealstørrelse.

Slæt

Fersk eng

< 20 graders hældning

3 ha

6,5 ha

15 ha

Overdrev

< 20 graders hældning

3 ha

6,5 ha

15 ha

> 20 graders hældning

3 ha

6,5 ha

15 ha

Strandeng

< 20 graders hældning

3 ha

6,5 ha

15 ha

Mose

< 20 graders hældning

3 ha

6,5 ha

15 ha

Hede

< 20 graders hældning

3 ha

6,5 ha

15 ha

> 20 graders hældning

3 ha

6,5 ha

15 ha

-1.445

-1.172

-900

-1.526

-1.253

-981

-1.553

-1.280

-1.008

-1.580

-1.307

-1.035

Stude

-6.869

-6.297

-5.812

-4.532

-3.951

-3.475

-4.717

-4.145

-3.660

-3.585

-3.011

-2.516

-2.560

-1.988

-1.503

-2.375

-1.803

-1.318

-2.560

-1.226

-1.503

Ammekvæg,

hårdføre

-7.941

-7.369

-6.884

-4.775

-4.203

-3.718

-4.960

-4.388

-3.903

-3.726

-3.152

-2.657

-2.660

-2.088

-1.603

-2.475

-1.903

-1.418

-2.660

-1.562

-1.603

Ammekvæg

vækst

-6.691

-6.119

-5.634

-4.271

-3.699

-3.213

-4.455

-3.884

-3.398

-3.380

-2.806

-2.311

-2.841

-1.835

-1.350

-2.292

-1.721

-1.235

-2.477

-1.150

-1.420

Natur-

kvæg

-2.277

-1.705

-1.250

-1.945

-1.373

-888

-2.130

-1.558

-1.073

-1.821

-1.247

-753

-1.752

-1.181

-695

-1.568

-996

-510

-1.752

-1.181

-695

Får

-6.235

-5.533

-4.992

-4.059

-2.964

-2.817

-4.244

-3.542

-3.001

-3.304

-2.600

-2.050

-2.639

-1.891

-1.351

-2.454

-1.707

-1.166

-2.594

-1.423

-1.351

Slæt henholdsvis græsning med "naturkvæg" (helårs ekstensiv) er de billigste af de alternati-

ver, som blev undersøgt af Dubgaard et al. (2012), og det er samtidig dem, der som nævnt er

mest hensigtsmæssige for truede arter. Dubgaard et al. (op.cit.) fandt desuden, at græsning

Chapter 4

217

med lavt græsningstryk generelt var billigere pr. hektar end normalt græsningstryk. Tilsva-

rende forskningsbaserede tal for økonomien i brandpleje er ikke fundet, men afbrænding af

hede anses for en "ret billig plejemetode" (Miljøstyrelsen 2017). Erfaringstal for udgifterne til

afbrænding af hede ligger på mellem 700-3.000 kr./ha afhængig af arealstørrelser og mand-

skabsforbrug (Stenild et al. 2016).

Det spiller desuden en væsentlig rolle, om det er muligt at oppebære tilskud til indsatsen eller

ej. Landbrugstilskud er normalt ikke muligt for lynghede eller for småarealer under 2 hektar,

men i øvrigt ofte muligt for lysåbne arealer som eng, strandeng, overdrev og lignende. Ord-

ningerne for landbrugstilskud ændres jævnligt, herunder i hvilket omfang der kan opnås til-

skud for Naturstyrelsens arealer. Tabel 16 viser størrelsen af tilskud til slæt og græsning under

ordningen "Pleje af græs- og naturarealer" (status 2017).

Tabel 16. Tilskudssatser til slæt og græsning under ordningen "Pleje af græs- og naturarea-

ler", Landbrugs- og Fiskeristyrelsen (2017).

Tilskud der kombineres med grundbetaling

(tilsagnstype 67)

Pleje med afgræsning

Pleje med slæt

Tilskud der ikke kombineres med grundbetaling

(tilsagnstype 66)

Pleje med afgræsning

Pleje med slæt

2.600

1.050

Kr/ha/år

1.650

850

Inklusive tilskud kan standarddriften på visse større arealer balancere eller give et lille over-

skud ved naturkvæg eller slæt, mens andre især mindre arealer giver underskud.

Det skønnes, at de særlige økologiske behov for de prioriterede truede arter vil medføre behov

for meromkostninger i forhold til standardtallene i tabel 15, og at en del af arealerne vil være

for små til at være berettigede til landbrugstilskud. Derfor vurderes der for stederne i bilag M

at være behov for en noget højere hektaromkostning på skønsvis det dobbelte af de forsk-

ningsbaserede standardtal for slæt og naturkvæg, som er de driftsformer, som passer bedst

med de økologiske behov for flertallet af truede arter. Hvis det er muligt at gennemføre drif-

ten sammen med tilgrænsende arealer eller på store sammenhængende områder, kan omkost-

ningerne dog nedbringes pr hektar.

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Chapter 4

9. Konklusion

Det viste sig nødvendigt at behandle skovarter og ikke-skovarter hver for sig, selvom det ikke

er optimalt, idet der er en stor gråzone med overlap. Primær årsag var forskel i tilgængelighed

af relevante økonomi data sammenholdt med opdelinger på skov og lysåben natur i ikke

mindst lovgivning og politiske beslutninger.

9.1

Skove og skovarter

Beslutningsstøtteværktøjet

Marxan with Zones

viste sig som et godt, men stærkt datakræven-

de, redskab til på omkostningseffektiv vis at pege på forskellige løsningsmuligheder for ud-

pegning af ny beskyttede skove. Med 976 statsskovs arealer og 626 forekommende 2020-måls

skovarter var det urealistisk at opnå omkostnings optimering af valget af beskyttede skove på

andre måder.

De mest optimale af de 26 løsningsforslag fra Marxan analyserne pegede på et arealbehov for

urørt skov, som ikke afveg væsentligt fra ambitionsniveauet i regeringens Naturpakke fra maj

2016, der besluttede at udpege 10.000 ha ny urørt skov i statsskovene. På den anden side viste

analyserne, at der kan være større behov for andre typer ny biodiversitetsskove end de 3.300

ha anden biodiversitetsskov besluttet i Naturpakken. En meget stor del af arealerne med 2020-

måls-arter, som analyserne foreslog til andet end urørt, er dog allerede beskyttet som § 3-

biotoper eller på anden vis, eller er baseret på arter med behov for nåleskov, som bør kunne

tilgodeses i Naturstyrelsens drift uden nødvendigvis at blive kategoriseret som anden biodi-

versitetsskov.

Marxan resultaterne kan yderligere optimeres ved for en mindre del af den optimale Marxan

løsnings 233 skove at gennemføre forskellig beskyttelse i forskellige dele af skoven baseret på

placering og økologiske behov for skovens 2020-måls-arter. Ved fuld anvendelse af opdelt

beskyttelse i omkring 40 statsskove kan alle de 304 prioriterede 2020-måls-arter fx "få det

som de helst vil have det" i mindst 5 statsskove eller samtlige statsskove de findes i, hvis det

er mindre end 5 skove. Under alle omstændigheder viser analyserne, at udmøntning af Natur-

pakken kan blive et godt bidrag til at standse tilbagegangen i biodiversitet (jf. Aichi mål 12

under Biodiversitets Konventionen), idet ikke mindst den lange række af truede arter, som

trives i urørt skov, får plads til at øge deres bestande.

Det økonomiske tab for de analyserede scenarier udgjorde afhængigt af forudsætninger 26-69

% af statsskovenes årlige netto indkomst fra skovbrug, og 38-46 % for de mest omkostnings-

effektive scenarier. Tabet kan reduceres og målopfyldelsen øges ved opdeling af relevante

skove, så biodiversitetsmæssigt mindre vigtige dele af skovene drives uden øget beskyttelse,

mens 2020-måls-arternes levesteder beskyttes svarende til arternes økologiske behov.

Chapter 4

219

9.2

Lysåbne biotoper og deres arter

For at øge sandsynligheden for at nå de politisk vedtagne 2020-mål for de ikke skovlevende

arter anbefales indsats gennemført hurtigst muligt for 370 identificerede kombinationer af sted

og art, som kan gavnes af mere målrettet beskyttelse. Derudover bør indsats for andre fore-

komster af 33 navngivne truede og højt prioriterede arter generelt prioriteres højt på Natursty-

relsens lysåbne arealer. Mange af disse andre forekomster findes i habitatområder, fredede

områder eller andre typer eksisterende beskyttelse, hvor indsats bedre kan målrettes de truede

arters økologiske behov ved hjælp af de kvalitetssikrede data tilvejebragt i nærværende pro-

jekt.

Styrelsens naturforvaltning har mange af stederne allerede været gennemført løbende i en

årrække, hvilket kan være en væsentlig årsag til, at de truede arter overhovedet har overlevet.

Fortsættelse og yderligere målretning af indsatsen til at tage hensyn til de omhandlede truede

arter anbefales, herunder også til de 277 dansk truede ikke-skovarter i prioritetsklasse 3, som

er kendt fra styrelsens arealer. Det store antal arter kan gøre det vanskeligt at overskue på

landsplan, men for den enkelte naturforvaltningsenhed og skov er antallet af arter typisk lavt,

så indsats bør kunne tilrettelægges målrettet.

220

Chapter 4

10.

Referencer

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provisional annotated checklist. English Nature Research Report No. 467, Peterborough,

UK, 142 pp.

Andersson, L., & Appelqvist, T. (1990) The influence of Pleistocene megafauna on nemoral and bore-

onemoral ecosystems: an hypothesis with implications for nature conservation. Svensk Bota-

nisk Tidskrift, 84(6), 355-368.

Ardron, J.A., Possingham, H.P., & Klein, C.J. (eds). (2010) Marxan Good Practices Handbook, Ver-

sion 2. Pacific Marine Analysis and Research Association, Victoria, BC, Canada. 165 pages.

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Wind, P. & Ejrnæs, R. (2014) Danmarks truede arter. Den danske rødliste. Miljøbiblioteket 1, Aarhus

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228

Chapter 4

Chapter 4

229

Bilag A.

Undersøgelsens datakilder.

Data leverandør

DOFbasen, Atlas III & observationer (Birdlife

Danmark)

Fugle & Natur, naturbasen (licens B05/2014,

Fugle & Natur)

Bugbase (Lepidopterologisk Forening)

Biodiversitets kortet, HNV-skov opdatering

(Naturstyrelsen)

Biodiversitets kortet 2014 (Naturstyrelsen)

Danmarks Svampeatlas (Foreningen

Svampekundskabens Fremme)

til

Side 1 af 2

Data total

17.961.625

1.958.677

859.969

54.997

23.185

559.159

199.307

49.894

8.526

123.334

167.905

31.731

210

44.088

69.436

104

3.331

3.299

9.306

22.128.168

Brugt

110.029

57.401

23.689

22.656

20.919

20.358

5.288

2.399

1.865

1.459

971

158

267.438

Dato

hentet

17/12

2015

2/12

2015

21/12

2015

2/5

2016

9/12

2015

17/11

2015

15/1

2014

1/12

2015

3/11

2015

22/4

2014

20/1

2016

3/11

2015

19/4

2016

14/1

2016

1/12

2015

19/4

2016

19/4

2016

19/4

2016

21/12

2015

3/11

2015

Total

Reference

Bilag A

http://dofbasen.dk/

http://www.fugleognatur.dk/

http://www.bugbase.dk/

Johannsen, V. K., 2015 &

digital data 2/5 2016

Ejrnæs et al. 2014 & digital

opdatering hentet 9/12 2015

http://www.svampeatlas.dk/

Stoltze, M., 1994

Skov-

2007

Naturstyrelsen

Atlasprojektet Danmarks Dagsommerfugle

(Zoologisk Museum)

Artsdata fra § 25-skove sammenstillet af

Cowi for Skov- og Naturstyrelsen

Naturstyrelsens "Pas-paa-kort" med artsfund

Atlas Flora Danica data 1992-2013, rødliste

& skov udtræk (Dansk Botanisk Forening)

Entomologisk forening for Fyns database

"Fynske insekter"

Danmarks Edderkopper (online database)

Epiphytic lichens and bryophytes in the

forests of Lille Vildmose in 2013

Taginsekt studie 1992-2009 (Zoologisk Mu-

seum)

Digitale databaser for invertebrater (Statens

Naturhistoriske Museum)

Biodiversitet, kvistflora og kvælstofbelast-

ning, Kås Skov 2002

Epifytiske laver og mosser på eg i Tofte

Skov 2012-2013

Status bøger om Tofte & Høstemark skove-

Dansk Topografisk Botanisk Undersøgelse

online, "TBU"

Levermosser i Herbarium C (Statens Natur-

historiske Museum)

Total

Naturstyrelsen 2015

Hartvig, P., 2015

http://www.fynskeinsekter.dk

http://www.danmarks-

edderkopper.dk/

Fritz, Ô., 2014

Thomsen et al., 2015

Stein, M., 2015

Larsen, R.S., 2002

Mouridsen, M.T., 2014

Hald-Mortensen, P., 2012 &

2002

http://www.daim.snm.ku.dk/

TBU-en

http://www.daim.snm.ku.dk/

hepatics

230

Chapter 4

Bilag A.

Undersøgelsens datakilder.

Side 2 af 2

Bilag A

Referencer til bilag A:

Ejrnæs et al. (2014)

Biodiversitetskort for Danmark.

Rapp 112/2014, DCE.

Fritz, Ô. (2014)

Epiphytic lichens and bryophytes in the forests of Lille Vildmose in 2013.

Naturcen-

trum report 2014-03-04, 34 pp.

GBIF (2016) GBIF.org (5th September 2016)

GBIF Occurrence Download

http://doi.org/10.15468/dl.obr3f0. Tjekket men ej brugt, da det var dubletter.

Hald-Mortensen, P. (2002)

Høstemark - status 2001.

AVJF, 302 pp.

Hald-Mortensen, P. (2012)

Tofte Skov og Mose - status 2012.

AVJF, 387 pp.

Hartvig, P. (2015)

Atlas Flora Danica.

3 volumes. Gyldendal, København.

Johannsen, V. K. (2015)

Udvikling af et High Nature Value - HNV-skovkort for Danmark.

IGN rap-

port, nov 2015.

Larsen, R.S. (2002)

Biodiversitet, kvistflora og kvælstofbelastning. Kås Skov, 2002.

KU speciale rap-

port.

Mouridsen, M.T. (2014)

Epifytiske laver og mosser på eg i Tofte Skov. Status 2012-13.

Specialerap-

port, KU Biol. Inst. 2014.

Naturstyrelsen (2015)

Databasen "Pas-paa-kort" Naturreg.

Upubliseret.

Naturstyrelsen (2016)

Digitalt biodiversitetskort 2016,

opdaterede ArcGis filer 18. marts 2016. Upub-

liceret.

Skov- og Naturstyrelsen (2007)

Artsdata fra 2007 COWI projektet " Naturmæssigt særlig værdifulde

skove § 25".

Upubliceret.

Stein. M., in litt. (2015)

RE: Forespørgsel på invertebratdata til mit PhD.

Email 1/12 2015 fra SNHM.

Stoltze, M. (1994)

An Annotated Atlas of the Danish Butterflies.

Zoologisk Museum, 134 pp.

Thomsen, P. F., Jørgensen, P. S., Bruun, H. H., Pedersen, J., Riis-Nielsen, T., Jonko, K., Słowińska, I.,

Rahbek, C., Karsholt, O. (2015)

Resource specialists lead local insect community turnover tilknyttet

with temperature – analysis of an 18-year full-seasonal record of moths and beetles.

Journal of Ani-

mal Ecology. doi: 10.1111/1365-2656.12452.

Chapter 4

231

Data for de 626 skovarter

Artsgruppe

Pattedyr

Fugle

Padder

Biller

Arts navn

Bechsteins flagermus

Brandts flagermus

Bredøret flagermus

Brunflagermus

Damflagermus

Dværgflagermus

Frynseflagermus

Hare

Hasselmus

Langøret flagermus

Nordflagermus

Pipistrelflagermus

Skægflagermus

Skovmår

Sydflagermus

Troldflagermus

Ulv

Vandflagermus

Gulirisk

Havørn

Hedelærke

Hvepsevåge

Lærkefalk

Lille fluesnapper

Natravn

Nøddekrige

Perleugle

Pirol

Rødrygget tornskade

Sortspætte

Stor hornugle

Stor skallesluger

Svaleklire

Trane

Vendehals

Butsnudet frø

Løvfrø

Spidssnudet frø

Springfrø

Stor vandsalamander

Allandrus undulatus

Allecula morio

Ampedus hjorti

Ampedus quercicola

Azurbille

Blodrød maskebille

Bøgeløber

Egeværftbille

Eghjort

Elmeloppe

Eremit

Falsk skjoldbille

Fyrregråbuk

Glat løber

Grøn pragttorbist

Hallomenus axillaris

Hasselbuk

Hypulus quercinus

Klintoldenborre

Lasiorhynchites cavifrons

Lille langben

Magdalis armigera

Matsort træsmælder

Melasis buprestoides

Mycetophagus fulvicollis

Neomida haemorrhoidalis

Peltis ferruginea

Pissodes validirostris

Pragtsmælder

Rød skivebuk

Rødbrystet maskebille

Seksbåndet blomsterbuk

Smaragdina salicina

Side 1 af 9

Truet hvor? Rødliste TilbagegaPrioritePræference

EU anx 2 art

DD ja

Løvskov

EU anx 4 art

VU nej

Løvskov

EU anx 2 art

VU nej

Løvskov

EU anx 4 art

LC nej

Løvskov

EU anx 2 art

VU nej

Løvskov

EU anx 4 art

LC nej

Løvskov

EU anx 4 art

VU nej

Løvskov

VU ja

Løvskov

EU anx 4 art

EN ja

Løvskov

EU anx 4 art

LC nej

Løvskov

EU anx 4 art

NA nej

Løvskov

EU anx 4 art

LC nej

Løvskov

EU anx 4 art

VU nej

Løvskov

EU anx 5 art

NT nej

Nål fakultativ

EU anx 4 art

LC nej

Løvskov

EU anx 4 art

LC nej

Løvskov

EU anx 2 art

‐

nej

Løvskov

EU anx 4 art

Løvskov

LC nej

VU nej

Nål fakultativ

EU anx 1 fugl

VU nej

Løvskov

EU anx 1 fugl

NT nej

Nål fakultativ

EU anx 1 fugl

LC nej

Løvskov

EN nej

Løvskov

EU anx 1 fugl

NA nej

Løvskov

EU anx 1 fugl

LC nej

Nål obligat

RE RE

Nål obligat

EU anx 1 fugl

NA nej

Nål obligat

Løvskov

CR ja

EU anx 1 fugl

LC nej

Løvskov

EU anx 1 fugl

LC nej

Løvskov

EU anx 1 fugl

NT nej

Løvskov

VU nej

Løvskov

VU nej

Løvskov

EU anx 1 fugl

LC nej

Løvskov

EN ja

Løvskov

EU anx 5 art

LC nej

Løvskov

EU anx 4 art

LC nej

Løvskov

EU anx 4 art

LC ja

Løvskov

EU anx 4 art

LC nej

Løvskov

EU anx 2 art

LC ja

Løvskov

VU nej

Løvskov

VU ja

Løvskov

Globalt

VU nej

Løvskov

VU nej

Løvskov

VU ja

Nål obligat

VU nej

Løvskov

EN ja

Løvskov

VU ja

Løvskov

RE RE

Løvskov

VU ja

Løvskov

EU anx 2 art

EN ja

Løvskov

VU ja

Løvskov

EN nej

Nål obligat

VU ja

Løvskov

CR ja

Løvskov

VU ja

Løvskov

VU nej

Løvskov

VU ja

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU ja

Løvskov

VU ja

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN ja

Løvskov

VU nej

Løvskov

VU nej

Nål obligat

VU ja

Løvskov

VU nej

Løvskov

VU ja

Løvskov

EN ja

Nål obligat

EN ja

Løvskov

Saproxyl?

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Bilag B

Gruppe

Zone Skove Match gap?

Åben skov

3 gap1

Åben skov

321

Åben skov

Nål fakultativ

Åben skov

Anden løvskov 34

Åben skov

Nål fakultativ

Anden løvskov 34

Nål fakultativ

105

Anden løvskov 34

Åben skov

Anden løvskov 34

Nål obligat

Anden løvskov 34

Åben skov

201

Anden løvskov 34

113

Åben skov

Anden løvskov 34

Åben skov

187

Åben skov

179

Åben skov

142

Saproxyl skovart 4

Åben saproxyl

0 gap1

Åben saproxyl

3 gap2

Åben saproxyl

Nål saproxyl

Saproxyl skovart 4

Anden løvskov 34

Saproxyl skovart 4

Åben saproxyl

0 gap2

Åben skov

Åben saproxyl

Saproxyl skovart 4

Nål saproxyl

Anden løvskov 34

Åben saproxyl

0 gap1

Saproxyl skovart 4

Åben saproxyl

Saproxyl skovart 4

Åben skov

Åben saproxyl

3 gap2

Åben saproxyl

Saproxyl skovart 4

Åben skov

Nål saproxyl

Åben saproxyl

0 gap1

Åben saproxyl

Saproxyl skovart 4

Nål saproxyl

Åben skov

232

Chapter 4

Data for de 626 skovarter

Artsgruppe Arts navn

Biller

Sort blomsterbuk

Biller

Sort spidsbuk

Biller

Stor cylinderbille

Biller

Stor elmebarkbille

Biller

Stor malakitbille

Biller

Stor skovsmælder

Biller

Tachyta nana

Biller

Tetrops starkii

Biller

Tofarvet hedeløber

Biller

Urskovshvepsebuk

Biller

Xylophilus corticalis

Tovinger

Asiatisk løgsvirreflue

Tovinger

Bjerg‐svirreflue

Tovinger

Broget urtesvirreflue

Tovinger

Brun bjørnesvirreflue

Tovinger

Brun træsmuldsvirreflue

Tovinger

Dværg‐svirreflue

Tovinger

Fyrre‐træsmuldsvirreflue

Tovinger

Gul bjørnesvirreflue

Tovinger

Gul humlesvirreflue

Tovinger

Gul træhulflue

Tovinger

Gulbenet urtesvirreflue

Tovinger

Hvidbåndet rovflue

Tovinger

Jordhumle‐svirreflue

Tovinger

Lille træsvirreflue

Tovinger

Lysende svirreflue

Tovinger

Mørk myresvirreflue

Tovinger

Nyre‐træsaftsvirreflue

Tovinger

Panzers træsaftsvirreflue

Tovinger

Pragtsvirreflue

Tovinger

Skinnende træsvirreflue

Tovinger

Smuk løgsvirreflue

Tovinger

Sort hårsvirreflue

Tovinger

Sort træsmuldsvirreflue

Tovinger

Sort vedrovflue

Tovinger

Sortmundet glanssvirreflue

Tovinger

Stikkelsbær‐glanssvirreflue

Tovinger

Sump‐urtesvirreflue

Tovinger

Temnostoma meridionale

Tovinger

Tidlig ornamentsvirreflue

Tovinger

Tofarvet træsaftsvirreflue

Tovinger

Uldhåret pelssvirreflue

Tovinger

Uld‐svirreflue

Tovinger

Verralls hvepsesvirreflue

Tæger

Birkebarktæge

Tæger

Karsetæge

Tæger

Toplettet urtetæge

Årevinger

Blank gæstemyre

Årevinger

Enghumle

DagsommerfuBrun pletvinge

DagsommerfuDet hvide w

DagsommerfuEnghvidvinge

DagsommerfuGuldhale

DagsommerfuKejserkåbe

DagsommerfuKirsebærtakvinge

DagsommerfuPerlemorrandøje

DagsommerfuRødlig perlemorsommerfugl

DagsommerfuSkovhvidvinge

DagsommerfuSkovperlemorsommerfugl

Sommerfugle Askegraa lavspinder

Sommerfugle Birkespinder

Sommerfugle Blegpandet lavspinder

Sommerfugle Bøgeskovspinder

Sommerfugle Brachionycha nubeculosa

Sommerfugle Brunrods‐hætteugle

Sommerfugle Dværgspinder

Sommerfugle Grå dromedarspinder

Sommerfugle Grå landmand

Sommerfugle Gran‐nonne

Sommerfugle Højmose‐tiggerugle

Sommerfugle Hønsetarm‐glansugle

Sommerfugle Hyrdeugle

Sommerfugle Natlyssværmer

Truet hvor?

Globalt

EU anx 4 art

Side 2 af 9

Rødliste TilbagegaPrioritePræference

VU ja

Løvskov

VU ja

Løvskov

EN ja

Løvskov

EN ja

Løvskov

VU ja

Løvskov

VU nej

Nål obligat

VU ja

Løvskov

VU nej

Løvskov

VU ja

Nål fakultativ

RE RE

Løvskov

VU ja

Løvskov

VU nej

Løvskov

VU ja

Nål obligat

VU ja

Løvskov

VU ja

Løvskov

VU ja

Løvskov

VU ja

Nål fakultativ

VU ja

Nål obligat

VU ja

Løvskov

VU nej

Nål fakultativ

EN ja

Løvskov

VU nej

Løvskov

CR ja

Løvskov

EN ja

Løvskov

VU ja

Løvskov

VU nej

Løvskov

VU ja

Løvskov

EN ja

Løvskov

VU nej

Løvskov

EN ja

Løvskov

EN ja

Løvskov

VU ja

Løvskov

VU nej

Løvskov

EN ja

Løvskov

EN ja

Nål obligat

VU nej

Løvskov

VU ja

Løvskov

VU ja

Løvskov

EN ja

Løvskov

VU ja

Løvskov

EN ja

Løvskov

VU ja

Løvskov

EN ja

Løvskov

CR ja

Løvskov

VU ja

Løvskov

CR ja

Løvskov

CR ja

Løvskov

VU nej

Nål obligat

VU ja

Løvskov

EN ja

Løvskov

EN ja

Løvskov

CR ja

Løvskov

VU ja

Løvskov

EN ja

Løvskov

CR ja

Løvskov

RE RE

Løvskov

CR ja

Løvskov

RE RE

Løvskov

EN ja

Løvskov

VU nej

Løvskov

VU nej

Løvskov

EN nej

Nål fakultativ

VU ja

Løvskov

RE RE

Løvskov

VU ja

Løvskov

CR ja

Nål fakultativ

EN nej

Løvskov

EN ja

Løvskov

EN ja

Nål obligat

EN ja

Løvskov

VU nej

Løvskov

CR ja

Løvskov

NA nej

Løvskov

Saproxyl?

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Bilag B

Gruppe

Zone Skove Match gap?

Åben skov

4 gap1

Åben saproxyl

Saproxyl skovart 4

Åben saproxyl

Nål saproxyl

Åben saproxyl

0 gap1

Åben saproxyl

Nål fakultativ

Saproxyl skovart 4

Åben skov

Nål obligat

Åben skov

Saproxyl skovart 4

Nål fakultativ

Nål saproxyl

Åben skov

Nål fakultativ

Saproxyl skovart 4

Åben skov

1 gap1

Saproxyl skovart 4

2 gap1

Åben skov

Åben saproxyl

4 gap1

Åben saproxyl

Saproxyl skovart 4

1 gap1

Åben saproxyl

Åben skov

1 gap1

Anden løvskov 34

Saproxyl skovart 4

2 gap2

Nål obligat

1 gap3

Åben skov

2 gap2

Åben skov

0 gap1

Saproxyl skovart 4

Åben skov

3 gap2

Saproxyl skovart 4

4 gap1

Åben saproxyl

Åben skov

Åben saproxyl

Åben skov

Nål obligat

3 gap2

Åben skov

132

Åben skov

3 gap2

Åben skov

4 gap1

Åben skov

Anden løvskov 34

Nål fakultativ

Urørt løvskov

Åben skov

0 gap1

Åben skov

Nål fakultativ

3 gap1

Åben skov

Nål obligat

0 gap3

Åben skov

Anden løvskov 34

Åben skov

Chapter 4

233

Data for de 626 skovarter

Artsgruppe Arts navn

Sommerfugle Pilplet‐ugle

Sommerfugle Pragt grønsagsugle

Sommerfugle Ringspinder

Sommerfugle Rødbrun ordenugle

Sommerfugle Rustplettet ugle

Sommerfugle Skinnende jordfarveugle

Sommerfugle Skovbjørn

Sommerfugle Stenkløver‐køllesværmer

Sommerfugle Stribet målerugle

Sommerfugle Tjørnespinder

Sommerfugle Trapez‐glansugle

Sommerfugle Treforkugle

Sommerfugle Uldhale

Guldsmede Stor kærguldsmed

Græshopper Stor enggræshoppe

Edderkopper Araneus triguttatus

Edderkopper Dendryphantes rudis

Edderkopper Hygrolycosa rubrofasciata

Edderkopper Marmoreret hjulspinder

Edderkopper Midia midas

Edderkopper Orange hjulspinder

Edderkopper Stor pukkelhjulspinder

Mosskorpion Stellas mosskorpion

Snegle

Skæv vindelsnegl

Snegle

Sumpvindelsnegl

Snegle

Vinbjergsnegl

Sæksvampe Brun kuldyne

Sæksvampe Brun marchandiomyces

Sæksvampe Elaphomyces anthracinus

Sæksvampe Furestokket foldhat

Sæksvampe Helvella albella

Sæksvampe Kæmpe‐stenmorkel

Sæksvampe Kulsort kuldyne

Sæksvampe Queléts foldhat

Basidiesvamp Abrikos‐huesvamp

Basidiesvamp Abrikos‐koralsvamp

Basidiesvamp Ædelgran‐mælkehat

Basidiesvamp Afblegende kam‐fluesvamp

Basidiesvamp Agerhøne‐champignon

Basidiesvamp Amanita olivaceogrisea

Basidiesvamp Amaurodon cyaneus

Basidiesvamp Anis‐læderhat

Basidiesvamp Anis‐skørhat

Basidiesvamp Antrodia malicola

Basidiesvamp Askegrå sneglehat

Basidiesvamp Askehvid mørkhat

Basidiesvamp Athelidium aurantiacum

Basidiesvamp Avnbøg‐skørhat

Basidiesvamp Bæger‐tåresvamp

Basidiesvamp Bæltet korkpigsvamp

Basidiesvamp Bæltet mælkehat

Basidiesvamp Bævrekølle

Basidiesvamp Banan‐slørhat

Basidiesvamp Bestøvlet tragthat

Basidiesvamp Bitter korkpigsvamp

Basidiesvamp Blåfodet kødpigsvamp

Basidiesvamp Blålig filthat

Basidiesvamp Blålig korkpigsvamp

Basidiesvamp Bleg koralsvamp

Basidiesvamp Bleg rørhat

Basidiesvamp Bleg skærmhat

Basidiesvamp Blodplettet koralsvamp

Basidiesvamp Blodrød skørhat

Basidiesvamp Bøge‐rødblad

Basidiesvamp Børstehåret savbladhat

Basidiesvamp Børstehåret spejlporesvamp

Basidiesvamp Børstepigsvamp

Basidiesvamp Børsteporesvamp

Basidiesvamp Brændende mælkehat

Basidiesvamp Brandgul fagerhat

Basidiesvamp Bronze‐parasolhat

Basidiesvamp Bronze‐rørhat

Basidiesvamp Brun skørhat

Truet hvor?

EU anx 2 art

Globalt

EU anx 5 art

Side 3 af 9

Rødliste TilbagegaPrioritePræference

VU ja

Løvskov

EN ja

Løvskov

EN ja

Løvskov

RE RE

Løvskov

RE RE

Nål fakultativ

VU ja

Løvskov

CR ja

Løvskov

CR ja

Løvskov

VU ja

Løvskov

CR ja

Løvskov

RE RE

Løvskov

VU ja

Løvskov

VU ja

Løvskov

VU ja

Løvskov

CR ja

Løvskov

VU nej

Løvskov

VU nej

Nål obligat

VU nej

Løvskov

VU ja

Løvskov

EN ja

Løvskov

EN ja

Løvskov

EN ja

Nål obligat

‐

nej

Løvskov

‐

Løvskov

VU nej

Løvskov

‐

nej

Løvskov

VU nej

Løvskov

VU nej

Løvskov

VU nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU ja

Nål obligat

VU nej

Løvskov

VU ja

Løvskov

VU nej

Løvskov

VU ja

Løvskov

EN ja

Nål obligat

EN nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

VU nej

Løvskov

CR nej

Løvskov

EN nej

Løvskov

EN nej

Løvskov

VU ja

Løvskov

EN ja

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU ja

Nål fakultativ

VU nej

Løvskov

VU ja

Løvskov

CR nej

Løvskov

EN ja

Nål obligat

EN ja

Nål obligat

CR ja

Nål fakultativ

VU nej

Løvskov

CR ja

Nål fakultativ

VU ja

Løvskov

EN ja

Løvskov

VU ja

Løvskov

VU ja

Løvskov

VU nej

Nål obligat

VU nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

VU ja

Løvskov

EN ja

Nål obligat

EN ja

Løvskov

EN ja

Nål obligat

EN nej

Løvskov

VU nej

Løvskov

VU nej

Nål obligat

Saproxyl?

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Bilag B

Gruppe

Zone Skove Match gap?

Anden løvskov 34

Åben skov

3 gap1

Nål fakultativ

Åben skov

3 gap1

Åben skov

0 gap1

Åben skov

1 gap3

Åben skov

Nål obligat

Åben skov

0 gap1

Åben skov

1 gap1

Nål obligat

Åben saproxyl

Anden løvskov 34

101

Saproxyl skovart 4

Anden løvskov 34

Åben skov

Anden løvskov 34

Nål obligat

1 gap2

Saproxyl skovart 4

Anden løvskov 34

Saproxyl skovart 4

Anden løvskov 34

Nål obligat

2 gap3

Åben skov

Saproxyl skovart 34

Saproxyl skovart 4

Anden løvskov 34

Saproxyl skovart 4

Anden løvskov 34

Saproxyl skovart 4

Åben skov

Saproxyl skovart 4

Nål fakultativ

Åben skov

Urørt løvskov

Nål obligat

Nål fakultativ

3 gap1

Saproxyl skovart 4

Nål fakultativ

Anden løvskov 34

Åben skov

Saproxyl skovart 34

Anden løvskov 34

Nål obligat

Saproxyl skovart 4

Åben saproxyl

Nål saproxyl

Anden løvskov 34

Nål obligat

1 gap1

Åben skov

Anden løvskov 34

Nål obligat

234

Chapter 4

Data for de 626 skovarter

Artsgruppe Arts navn

Truet hvor?

Basidiesvamp Brungul fagerhat

Basidiesvamp Brungul rødblad

Basidiesvamp Brungul vokshat

Basidiesvamp Brunlig koralpig

Basidiesvamp Brusk‐bævretop

Basidiesvamp Bulliards slørhat

Basidiesvamp Chromocyphella muscicola DK

Basidiesvamp Cinnober‐muslingesvamp

Basidiesvamp Cinnober‐slørhat

Basidiesvamp Clavulinopsis microspora

Basidiesvamp Coprinopsis pannucioides

Basidiesvamp Corticium expallens

Basidiesvamp Cortinarius acetosus

Basidiesvamp Cortinarius aureocalceolatus DK

Basidiesvamp Cortinarius betulinus

Basidiesvamp Cortinarius caesiostramineus DK

Basidiesvamp Cortinarius cagei

Basidiesvamp Cortinarius catharinae

Basidiesvamp Cortinarius cisticola

Basidiesvamp Cortinarius fulvocitrinus

Basidiesvamp Cortinarius imperialis

Basidiesvamp Cortinarius insignibulbus

Basidiesvamp Cortinarius langeorum

Basidiesvamp Cortinarius luteoimmarginatusDK

Basidiesvamp Cortinarius multiformium

Basidiesvamp Cortinarius quarciticus

Basidiesvamp Cortinarius saporatus

Basidiesvamp Cortinarius selandicus

Basidiesvamp Cortinarius serratissimus

Basidiesvamp Cortinarius talus

Basidiesvamp Cortinarius tophaceus

Basidiesvamp Cortinarius urbicus

Basidiesvamp Cortinarius violaceocinereus DK

Basidiesvamp Cortinarius xantho‐ochraceus DK

Basidiesvamp Cristinia gallica

Basidiesvamp Daddelbrun slørhat

Basidiesvamp Dendrothele commixta

Basidiesvamp Djævle‐rørhat

Basidiesvamp Dråbehat

Basidiesvamp Drue‐koralsvamp

Basidiesvamp Duftende kæmpeskælhat

Basidiesvamp Duft‐ridderhat

Basidiesvamp Duft‐slørhat

Basidiesvamp Dunet fnugfod

Basidiesvamp Dunet pælerodshat

Basidiesvamp Dunstokket posesvamp

Basidiesvamp Ege‐ildporesvamp

Basidiesvamp Ege‐spejlporesvamp

Basidiesvamp Egetunge

Basidiesvamp Elegant parasolhat

Basidiesvamp Elfenbens‐mælkehat

Basidiesvamp Elfenbens‐rørhat

Basidiesvamp Elmehat

Basidiesvamp Ensfarvet læderporesvamp

Basidiesvamp Entoloma lampropus

Basidiesvamp Entoloma parkensis

Basidiesvamp Entoloma plebejum

Basidiesvamp Farvebold

Basidiesvamp Filtet korkpigsvamp

Basidiesvamp Filtet pælerodshat

Basidiesvamp Finskællet parasolhat

Basidiesvamp Finskællet skælhat

Basidiesvamp Firefarvet slørhat

Basidiesvamp Firfliget stjernebold

Basidiesvamp Flammeporesvamp

Basidiesvamp Fløjls‐mælkehat

Basidiesvamp Flosset fluesvamp

Basidiesvamp Frugt‐kalkskind

Basidiesvamp Frynset stilkbovist

Basidiesvamp Furestokket fladhat

Basidiesvamp Fyrre‐ildporesvamp

Basidiesvamp Fyrre‐vatporesvamp

Basidiesvamp Giftig rødblad

Side 4 af 9

Rødliste TilbagegaPrioritePræference

VU nej

Nål fakultativ

VU ja

Løvskov

EN ja

Løvskov

VU nej

Løvskov

VU ja

Løvskov

CR nej

Løvskov

VU nej

Løvskov

EN ja

Løvskov

VU nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

CR nej

Løvskov

EN nej

Løvskov

CR nej

Løvskov

EN nej

Løvskov

EN nej

Løvskov

EN nej

Løvskov

EN nej

Løvskov

VU nej

Nål fakultativ

EN nej

Løvskov

EN nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN ja

Løvskov

EN nej

Løvskov

VU nej

Løvskov

VU nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

EN nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

VU ja

Løvskov

VU ja

Løvskov

EN nej

Nål obligat

EN nej

Løvskov

VU nej

Løvskov

EN ja

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU ja

Løvskov

EN ja

Løvskov

EN nej

Løvskov

VU nej

Nål obligat

CR nej

Nål obligat

EN ja

Løvskov

VU ja

Løvskov

EN ja

Nål fakultativ

VU nej

Løvskov

EN nej

Løvskov

VU nej

Nål fakultativ

CR nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

VU ja

Løvskov

VU nej

Løvskov

EN ja

Nål fakultativ

EN nej

Nål obligat

VU nej

Nål obligat

VU nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU nej

Nål obligat

VU ja

Nål obligat

EN nej

Nål fakultativ

VU ja

Løvskov

Saproxyl?

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Bilag B

Gruppe

Zone Skove Match gap?

Nål fakultativ

Åben skov

4 gap1

Åben skov

Saproxyl skovart 4

3 gap1

Anden løvskov 34

Åben skov

Saproxyl skovart 4

0 gap1

Anden løvskov 34

Åben skov

Saproxyl skovart 4

Anden løvskov 34

Urørt løvskov

Anden løvskov 34

Urørt løvskov

Anden løvskov 34

Urørt løvskov

Nål fakultativ

Urørt løvskov

Anden løvskov 34

Urørt løvskov

Saproxyl skovart 4

Anden løvskov 34

Saproxyl skovart 34

Anden løvskov 34

Saproxyl skovart 4

4 gap1

Nål obligat

Urørt løvskov

Anden løvskov 34

Urørt løvskov

Åben saproxyl

Saproxyl skovart 34

Anden løvskov

Nål obligat

Åben saproxyl

Saproxyl skovart 4

Nål fakultativ

2 gap1

Anden løvskov 34

Åben skov

Nål fakultativ

Anden løvskov 34

Saproxyl skovart 34

Åben skov

Åben saproxyl

Urørt løvskov

Nål fakultativ

Nål saproxyl

Nål obligat

Åben skov

Saproxyl skovart 4

Åben skov

Nål obligat

Nål saproxyl

Anden løvskov 34

Chapter 4

235

Data for de 626 skovarter

Artsgruppe Arts navn

Truet hvor?

Basidiesvamp Giftrød skørhat

Basidiesvamp Glat ildporesvamp

Basidiesvamp Gloeocystidiellum clavuligerum

Basidiesvamp Grå kantarel

Basidiesvamp Grå troldhat

Basidiesvamp Gråbrun gråblad

Basidiesvamp Gråbrun parasolhat

Basidiesvamp Gracil slørhat

Basidiesvamp Grædende parasolhat

Basidiesvamp Grågrøn parasolhat

Basidiesvamp Grånende skørhat

Basidiesvamp Gråsort silkehat

Basidiesvamp Gråviolet mælkehat

Basidiesvamp Grenet fladhat

Basidiesvamp Grønfodet trævlhat

Basidiesvamp Grøngul pastelporesvamp

Basidiesvamp Grøngul ridderhat

Basidiesvamp Grønskællet parasolhat

Basidiesvamp Grov sejporesvamp

Basidiesvamp Grubestokket mælkehat

Basidiesvamp Grubet korkpigsvamp

Basidiesvamp Guirlande‐parasolhat

Basidiesvamp Gul ege‐rørhat

Basidiesvamp Gul trævlhat

Basidiesvamp Gul troldhat

Basidiesvamp Guldrørhat

Basidiesvamp Gulgrå køllesvamp

Basidiesvamp Gulgrøn slørhat

Basidiesvamp Gullig parasolhat

Basidiesvamp Gulnende skørhat

Basidiesvamp Gylden grynskælhat

Basidiesvamp Gylden koralsvamp

Basidiesvamp Gyldenbrun lakporesvamp

Basidiesvamp Halsbånd‐ridderhat

Basidiesvamp Hasselporesvamp

Basidiesvamp Højskællet parasolhat

Basidiesvamp Højstokket ridderhat

Basidiesvamp Honning‐skørhat

Basidiesvamp Hovformet filthat

Basidiesvamp Hul‐blækhat

Basidiesvamp Hvælvet gift‐slørhat

Basidiesvamp Hvid pigsvamp

Basidiesvamp Hvidlig skiveskorpe

Basidiesvamp Hygrophorus nemoreus

Basidiesvamp Hyphoderma macedonicum DK

Basidiesvamp Hyphoderma medioburiense DK

Basidiesvamp Hypholoma ericaeum

Basidiesvamp Inocybe margaritispora

Basidiesvamp Inocybe quietiodor

Basidiesvamp Inocybe sambucina

Basidiesvamp Isabellafarvet køllesvamp

Basidiesvamp Jod‐skørhat

Basidiesvamp Kål‐bruskhat

Basidiesvamp Kål‐fladhat

Basidiesvamp Kandis‐tåreblad

Basidiesvamp Kantet ridderhat

Basidiesvamp Kliddet parasolhat

Basidiesvamp Klidhat

Basidiesvamp Knippe‐ridderhat

Basidiesvamp Kødfarvet grenkølle

Basidiesvamp Kokosbrun mælkehat

Basidiesvamp Kølle‐guldgaffel

Basidiesvamp Køllekantarel

Basidiesvamp Kornet skørhat

Basidiesvamp Krat‐vokshat

Basidiesvamp Kromgul vokshat

Basidiesvamp Krumskællet trævlhat

Basidiesvamp Kulkantarel

Basidiesvamp Kul‐skørhat

Basidiesvamp Labyrint‐kødporesvamp

Basidiesvamp Lærke‐mælkehat

Basidiesvamp Lærke‐sneglehat

Basidiesvamp Lakrids‐ridderhat

Side 5 af 9

Rødliste TilbagegaPrioritePræference

EN nej

Løvskov

EN ja

Løvskov

EN nej

Løvskov

VU ja

Løvskov

EN nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN ja

Løvskov

VU nej

Løvskov

VU ja

Løvskov

RE RE

Løvskov

EN ja

Løvskov

EN ja

Løvskov

EN nej

Løvskov

EN nej

Nål obligat

RE RE

Nål obligat

CR nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN ja

Løvskov

CR nej

Løvskov

CR nej

Løvskov

EN nej

Løvskov

EN ja

Løvskov

CR nej

Løvskov

EN nej

Løvskov

VU ja

Nål obligat

RE RE

Løvskov

EN nej

Løvskov

VU ja

Nål obligat

EN nej

Løvskov

VU ja

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

CR ja

Løvskov

EN nej

Løvskov

VU nej

Løvskov

VU ja

Løvskov

EN ja

Løvskov

VU nej

Løvskov

EN nej

Løvskov

EN ja

Nål obligat

VU nej

Løvskov

VU nej

Nål obligat

EN nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU ja

Nål obligat

VU ja

Løvskov

VU ja

Løvskov

VU nej

Løvskov

VU nej

Løvskov

VU nej

Nål fakultativ

VU nej

Løvskov

CR ja

Løvskov

VU nej

Løvskov

VU nej

Løvskov

VU ja

Løvskov

VU nej

Løvskov

EN ja

Løvskov

VU nej

Løvskov

CR nej

Løvskov

VU nej

Nål obligat

CR nej

Nål obligat

EN nej

Nål obligat

Saproxyl?

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Bilag B

Gruppe

Zone Skove Match gap?

Åben skov

Saproxyl skovart 4

1 gap1

Saproxyl skovart 4

Anden løvskov 34

Åben skov

Anden løvskov 34

Åben skov

Urørt løvskov

Åben skov

Anden løvskov 34

Urørt løvskov

Anden løvskov 34

Saproxyl skovart 4

Anden løvskov 34

Urørt løvskov

Saproxyl skovart 4

Nål obligat

Urørt løvskov

Anden løvskov 34

Anden løvskov

Anden løvskov 34

Åben skov

Urørt løvskov

Saproxyl skovart 4

3 gap1

Anden løvskov 34

Åben saproxyl

Nål obligat

Saproxyl skovart 34

Urørt løvskov

Nål obligat

1 gap2

Anden løvskov 34

Saproxyl skovart 4

Åben saproxyl

Anden løvskov 34

Saproxyl skovart 34

Anden løvskov 34

Saproxyl skovart 4

Åben skov

0 gap1

Anden løvskov 34

Nål obligat

1 gap1

Åben skov

Nål obligat

Urørt løvskov

Åben skov

Nål obligat

Urørt løvskov

Anden løvskov 34

Åben saproxyl

Nål fakultativ

Saproxyl skovart 4

Anden løvskov 34

Urørt løvskov

Åben skov

Anden løvskov 34

Saproxyl skovart 3

Anden løvskov 34

Saproxyl skovart 34

Nål obligat

236

Chapter 4

Data for de 626 skovarter

Artsgruppe Arts navn

Basidiesvamp Lak‐skørhat

Basidiesvamp Lamel‐rørhat

Basidiesvamp Lav agerhat

Basidiesvamp Lilla skørhat

Basidiesvamp Lillebitte skørhat

Basidiesvamp Løvegul skærmhat

Basidiesvamp Løv‐tjæreporesvamp

Basidiesvamp Magisk slørhat

Basidiesvamp Maj‐rødblad

Basidiesvamp Mandel‐skørhat

Basidiesvamp Medusa‐mørkhat

Basidiesvamp Mel‐slørhat

Basidiesvamp Møllers parasolhat

Basidiesvamp Mønster‐lædersvamp

Basidiesvamp Mørk fagerhat

Basidiesvamp Mørk fnugfod

Basidiesvamp Mørk læderpigsvamp

Basidiesvamp Mørk spidshat

Basidiesvamp Musegrå posesvamp

Basidiesvamp Mycena clavata

Basidiesvamp Nubret parasolhat

Basidiesvamp Orange åresvamp

Basidiesvamp Orange korkpigsvamp

Basidiesvamp Orangebrun troldhat

Basidiesvamp Orangefodet parasolhat

Basidiesvamp Orangegul ridderhat

Basidiesvamp Orangegylden slørhat

Basidiesvamp Orangerosa skørhat

Basidiesvamp Ørnebregne‐løghat

Basidiesvamp Park‐sandporesvamp

Basidiesvamp Pelargonie‐mælkehat

Basidiesvamp Pelargonie‐skørhat

Basidiesvamp Perlehøne‐champignon

Basidiesvamp Phlebia subserialis

Basidiesvamp Pigget fluesvamp

Basidiesvamp Pigget frynsehinde

Basidiesvamp Pigget grynskælhat

Basidiesvamp Pighud

Basidiesvamp Pindsvinepigsvamp

Basidiesvamp Pjaltet læderpigsvamp

Basidiesvamp Pjusket duftpigsvamp

Basidiesvamp Pluteus exiguus

Basidiesvamp Pluteus insidiosus

Basidiesvamp Porfyrgrå rødblad

Basidiesvamp Porotheleum fimbriatum

Basidiesvamp Prægtig mælkehat

Basidiesvamp Prægtig slørhat

Basidiesvamp Psathyrella spintrigeroides

Basidiesvamp Puklet skørhat

Basidiesvamp Punktstokket vokshat

Basidiesvamp Purpur‐champignon

Basidiesvamp Purpurstokket slørhat

Basidiesvamp Purpur‐voksporesvamp

Basidiesvamp Ramaria flavescens

Basidiesvamp Ramaria krieglsteineri

Basidiesvamp Ramaria suecica

Basidiesvamp Rødbrun spidshat

Basidiesvamp Rod‐gråblad

Basidiesvamp Rødlig bruskhat

Basidiesvamp Rødmende alfehat

Basidiesvamp Rødmende silkehat

Basidiesvamp Røggrå mælkehat

Basidiesvamp Rosa fedtporesvamp

Basidiesvamp Rosa pastelporesvamp

Basidiesvamp Rosa støvbold

Basidiesvamp Rosafodet skærmhat

Basidiesvamp Rosalilla rødblad

Basidiesvamp Rosazonet trævlhat

Basidiesvamp Rundsporet slørhat

Basidiesvamp Rusporet keglehat

Basidiesvamp Russula helodes

Basidiesvamp Russula pallidospora

Basidiesvamp Russula persicina

Truet hvor?

Side 6 af 9

Rødliste TilbagegaPrioritePræference

VU ja

Nål obligat

EN ja

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

EN ja

Løvskov

EN nej

Løvskov

CR nej

Løvskov

VU ja

Løvskov

VU nej

Nål obligat

CR ja

Nål obligat

EN nej

Løvskov

EN nej

Løvskov

CR ja

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU ja

Nål fakultativ

VU nej

Nål obligat

VU ja

Løvskov

VU nej

Nål obligat

VU nej

Løvskov

VU nej

Løvskov

CR ja

Nål obligat

VU ja

Løvskov

EN nej

Løvskov

EN ja

Nål fakultativ

VU nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

VU nej

Løvskov

VU nej

Løvskov

VU nej

Nål fakultativ

EN nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

EN ja

Løvskov

CR ja

Løvskov

EN nej

Nål fakultativ

EN ja

Nål obligat

EN nej

Løvskov

EN nej

Løvskov

CR nej

Løvskov

VU nej

Løvskov

VU ja

Løvskov

EN nej

Løvskov

VU nej

Løvskov

VU ja

Nål obligat

EN ja

Løvskov

VU nej

Løvskov

VU ja

Løvskov

VU nej

Løvskov

CR nej

Løvskov

VU nej

Løvskov

VU nej

Nål obligat

VU nej

Nål obligat

EN ja

Nål fakultativ

VU nej

Løvskov

CR ja

Løvskov

VU nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

EN ja

Løvskov

VU nej

Løvskov

EN nej

Nål obligat

EN ja

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN nej

Nål obligat

CR nej

Løvskov

VU nej

Løvskov

Saproxyl?

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Bilag B

Gruppe

Zone Skove Match gap?

Nål obligat

3 gap1

Anden løvskov 34

Åben skov

Anden løvskov 34

Saproxyl skovart 4

Urørt løvskov

Åben skov

2 gap2

Nål obligat

Nål saproxyl

Urørt løvskov

Saproxyl skovart 4

1 gap1

Anden løvskov 34

Saproxyl skovart 4

Nål fakultativ

Nål obligat

Åben skov

Nål saproxyl

Åben skov

Saproxyl skovart 4

Nål obligat

Åben skov

Anden løvskov 34

Nål fakultativ

3 gap2

Urørt løvskov

Åben skov

Anden løvskov 34

Åben skov

Urørt løvskov

Nål saproxyl

Anden løvskov 34

Saproxyl skovart 4

Åben saproxyl

Nål fakultativ

Nål obligat

Saproxyl skovart 4

Åben skov

Saproxyl skovart 4

Anden løvskov 34

Urørt løvskov

Saproxyl skovart 4

Nål obligat

Åben skov

0 gap1

Åben skov

Anden løvskov 34

Saproxyl skovart 4

Anden løvskov 34

Åben skov

Nål obligat

Nål fakultativ

Saproxyl skovart 4

Åben skov

Urørt løvskov

Anden løvskov 34

Saproxyl skovart 4

Anden løvskov 34

Nål saproxyl

Urørt løvskov

0 gap2

Anden løvskov 34

Urørt løvskov

Saproxyl skovart 4

Nål obligat

Anden løvskov 34

Chapter 4

237

Data for de 626 skovarter

Artsgruppe Arts navn

Basidiesvamp Rust‐korkpigsvamp

Basidiesvamp Safrankødet slørhat

Basidiesvamp Safran‐slørhat

Basidiesvamp Sand‐skørhat

Basidiesvamp Sarcodon lepidus

Basidiesvamp Sart rødblad

Basidiesvamp Satans rørhat

Basidiesvamp Sej fedtporesvamp

Basidiesvamp Silke‐parasolhat

Basidiesvamp Silke‐ridderhat

Basidiesvamp Simocybe sumptuosa

Basidiesvamp Sirene‐slørhat

Basidiesvamp Skællet fåreporesvamp

Basidiesvamp Skællet filthat

Basidiesvamp Skællet kødpigsvamp

Basidiesvamp Skællet slørhat

Basidiesvamp Skæv rødblad

Basidiesvamp Skarlagen‐skærmhat

Basidiesvamp Skønfodet slørhat

Basidiesvamp Skorpe‐tåresvamp

Basidiesvamp Småskællet kødpigsvamp

Basidiesvamp Smuk koralsvamp

Basidiesvamp Snyltende posesvamp

Basidiesvamp Sodbrun sneglehat

Basidiesvamp Sødtduftende mørkhat

Basidiesvamp Sødtduftende parasolhat

Basidiesvamp Sølvskinnende rørhat

Basidiesvamp Sort køllesvamp

Basidiesvamp Sortanløbende trævlhat

Basidiesvamp Sortfodet stilkporesvamp

Basidiesvamp Sorthvid skørhat

Basidiesvamp Sorthvid troldporesvamp

Basidiesvamp Sortrandet skærmhat

Basidiesvamp Spatel‐filthat

Basidiesvamp Spiselig mælkehat

Basidiesvamp Steccherinum litschaueri

Basidiesvamp Steccherinum subcrinale

Basidiesvamp Stinkende fladhat

Basidiesvamp Stinkende slørhat

Basidiesvamp Stivhåret skærmhat

Basidiesvamp Stødrørhat

Basidiesvamp Stor blækhat

Basidiesvamp Stor filthat

Basidiesvamp Stor grenkølle

Basidiesvamp Stor kanelporesvamp

Basidiesvamp Stor spidshat

Basidiesvamp Strågul køllesvamp

Basidiesvamp Stridhåret rødblad

Basidiesvamp Stypella dubia

Basidiesvamp Stypella subgelatinosa

Basidiesvamp Sværtende gråblad

Basidiesvamp Sværtende kantarel

Basidiesvamp Tæge‐rødblad

Basidiesvamp Tæge‐vokshat

Basidiesvamp Teglfarvet mælkehat

Basidiesvamp Teglrød korkpigsvamp

Basidiesvamp Thallus‐navlehat

Basidiesvamp Tobaksbrun agerhat

Basidiesvamp Tofarvet foldporesvamp

Basidiesvamp Tomentella italica

Basidiesvamp Tomentella lateritia

Basidiesvamp Tomentella pilosa

Basidiesvamp Tomentella umbrinospora

Basidiesvamp Topporesvamp

Basidiesvamp Tør ridderhat

Basidiesvamp Tragtformet læderpigsvamp

Basidiesvamp Trechispora silvae‐ryae

Basidiesvamp Trefarvet tragtridderhat

Basidiesvamp Tricholoma basirubens

Basidiesvamp Trompetkølle

Basidiesvamp Tværåret rødblad

Basidiesvamp Tvefarvet rødblad

Basidiesvamp Tvefarvet sneglehat

Truet hvor?

Side 7 af 9

Rødliste TilbagegaPrioritePræference

EN ja

Nål obligat

VU ja

Nål obligat

VU nej

Løvskov

EN nej

Nål obligat

CR nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN ja

Løvskov

EN nej

Løvskov

VU ja

Løvskov

VU nej

Løvskov

VU nej

Løvskov

CR ja

Løvskov

VU nej

Løvskov

VU ja

Nål obligat

CR nej

Løvskov

EN ja

Løvskov

EN ja

Løvskov

RE RE

Nål obligat

VU nej

Løvskov

EN ja

Nål obligat

EN ja

Løvskov

VU nej

Løvskov

CR ja

Nål obligat

EN nej

Nål fakultativ

EN nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

VU ja

Løvskov

VU ja

Løvskov

CR nej

Nål obligat

VU ja

Nål obligat

VU ja

Løvskov

EN ja

Nål fakultativ

EN nej

Løvskov

EN ja

Løvskov

VU nej

Løvskov

VU nej

Nål obligat

VU nej

Løvskov

CR nej

Nål obligat

VU nej

Løvskov

VU ja

Løvskov

VU ja

Løvskov

CR ja

Løvskov

EN nej

Nål fakultativ

VU ja

Løvskov

EN nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN ja

Nål obligat

VU nej

Løvskov

EN ja

Løvskov

EN ja

Løvskov

EN ja

Nål obligat

CR nej

Løvskov

EN ja

Løvskov

EN nej

Løvskov

EN ja

Løvskov

EN nej

Løvskov

VU ja

Løvskov

VU nej

Løvskov

VU nej

Løvskov

CR ja

Løvskov

VU ja

Nål obligat

VU ja

Nål obligat

VU nej

Løvskov

CR nej

Løvskov

CR nej

Løvskov

VU nej

Løvskov

CR nej

Løvskov

VU nej

Løvskov

EN nej

Løvskov

Saproxyl?

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Bilag B

Gruppe

Zone Skove Match gap?

Nål obligat

Urørt løvskov

Nål obligat

Anden løvskov 34

Åben skov

Åben saproxyl

Urørt løvskov

Anden løvskov 34

Saproxyl skovart 4

Urørt løvskov

Anden løvskov 34

Saproxyl skovart 4

Nål obligat

Urørt løvskov

Saproxyl skovart 4

Saproxyl skovart 34

Nål obligat

Saproxyl skovart 4

Nål obligat

Anden løvskov 34

Nål obligat

Nål fakultativ

Urørt løvskov

Anden løvskov 34

Åben skov

Anden løvskov 34

Saproxyl skovart 4

4 gap1

Urørt løvskov

Nål obligat

Nål saproxyl

Saproxyl skovart 4

Nål fakultativ

Saproxyl skovart 4

Anden løvskov 34

Nål obligat

Saproxyl skovart 4

Nål saproxyl

Åben saproxyl

Anden løvskov 34

Saproxyl skovart 4

Åben saproxyl

Nål fakultativ

Åben skov

4 gap1

Åben skov

Saproxyl skovart 4

Nål obligat

Anden løvskov 34

Åben skov

Nål obligat

1 gap1

Anden løvskov 34

Åben skov

Saproxyl skovart 4

2 gap3

Saproxyl skovart 4

1 gap2

Saproxyl skovart 4

Saproxyl skovart 34

Nål obligat

Saproxyl skovart 4

Anden løvskov 34

Urørt løvskov

Åben saproxyl

Åben skov

Anden løvskov 34

238

Chapter 4

Data for de 626 skovarter

Artsgruppe Arts navn

Basidiesvamp Tykbladet gråblad

Basidiesvamp Tykbladet rødblad

Basidiesvamp Tyndstokket korkpigsvamp

Basidiesvamp Ulden slørhat

Basidiesvamp Ved‐navlehat

Basidiesvamp Ved‐posesvamp

Basidiesvamp Vedtragthat

Basidiesvamp Vellugtende læderpigsvamp

Basidiesvamp Vellugtende læderporesvamp

Basidiesvamp Vellugtende skælhat

Basidiesvamp Vifteblad

Basidiesvamp Vinrød parasolhat

Basidiesvamp Violblå fagerhat

Basidiesvamp Violblå slørhat

Basidiesvamp Violet køllesvamp

Basidiesvamp Violet koralsvamp

Basidiesvamp Violetbrun duftpigsvamp

Basidiesvamp Violetflaget slørhat

Basidiesvamp Violetknoldet slørhat

Basidiesvamp Violetkødet mælkehat

Basidiesvamp Violetstokket slørhat

Basidiesvamp Xenasma pulverulentum

Basidiesvamp Zone‐skørhat

Lav

Almindelig bogstavlav

Lav

Almindelig kantskivelav

Lav

Almindelig lungelav

Lav

Almindelig rødskivelav

Lav

Almindelig skæglav

Lav

Almindelig slørkantlav

Lav

Askegrå rensdyrlav

Lav

Bark‐blegskivelav

Lav

Bitter prikvortelav

Lav

Bleg blegskivelav

Lav

Bøge‐kantskivelav

Lav

Bredfliget svampelav

Lav

Brungrøn bægerlav

Lav

Busket skæglav

Lav

Elegant skållav

Lav

Filtrandet kantskivelav

Lav

Flotows hulfrugtlav

Lav

Gele‐skivelav

Lav

Glinsende kernelav

Lav

Grå dugskivelav

Lav

Grågrøn bægerlav

Lav

Grøn bogstavlav

Lav

Grønlig porina

Lav

Grønpudret bogstavlav

Lav

Gul trådkantlav

Lav

Gulgrøn bægerlav

Lav

Gulgrøn kantskivelav

Lav

Hede‐rensdyrlav

Lav

Indsænket kernelav

Lav

Kliddet bægerlav

Lav

Kruset skjoldlav

Lav

Kvist‐kantskivelav

Lav

Lakrød bægerlav

Lav

Liden skæglav

Lav

Naegelis tensporelav

Lav

Nåleprikket bogstavlav

Lav

Oliven‐bogstavlav

Lav

Opblæst bægerlav

Lav

Poelts væggelav

Lav

Pudret bægerlav

Lav

Rendet grenlav

Lav

Rødbrun gammelskovslav

Lav

Rødbrun nyrelav

Lav

Rosenrød stilav

Lav

Ru prikvortelav

Lav

Skæglav slægten

Lav

Skælklædt bægerlav

Lav

Skov‐punktlav

Lav

Skurvet prikvortelav

Lav

Sølv‐kantskivelav

Truet hvor?

EU anx 5 art

Side 8 af 9

Rødliste TilbagegaPrioritePræference

EN nej

Løvskov

VU ja

Løvskov

CR nej

Nål obligat

VU nej

Løvskov

EN nej

Løvskov

EN nej

Løvskov

VU nej

Løvskov

EN ja

Nål fakultativ

EN ja

Løvskov

CR nej

Løvskov

VU nej

Løvskov

VU nej

Løvskov

VU ja

Løvskov

VU nej

Løvskov

VU nej

Løvskov

EN ja

Løvskov

CR ja

Nål obligat

CR nej

Løvskov

VU nej

Løvskov

EN ja

Løvskov

VU nej

Løvskov

EN nej

Løvskov

CR nej

Løvskov

VU ja

Løvskov

VU ja

Nål fakultativ

VU ja

Løvskov

VU ja

Løvskov

VU ja

Løvskov

VU ja

Løvskov

LC ja

Løvskov

VU ja

Løvskov

VU ja

Løvskov

EN ja

Løvskov

EN ja

Løvskov

EN ja

Nål fakultativ

VU ja

Nål fakultativ

VU ja

Løvskov

EN ja

Løvskov

VU ja

Løvskov

CR ja

Løvskov

VU ja

Løvskov

EN ja

Løvskov

CR ja

Løvskov

VU ja

Nål fakultativ

EN ja

Løvskov

VU ja

Løvskov

EN ja

Løvskov

VU ja

Løvskov

EN ja

Nål fakultativ

EN ja

Løvskov

LC ja

Løvskov

EN ja

Løvskov

EN ja

Nål fakultativ

VU ja

Løvskov

VU ja

Løvskov

VU ja

Nål fakultativ

VU ja

Løvskov

EN ja

Løvskov

VU ja

Løvskov

EN ja

Løvskov

VU ja

Løvskov

VU ja

Løvskov

VU ja

Løvskov

RE RE

Nål fakultativ

RE RE

Løvskov

CR ja

Løvskov

CR nej

Løvskov

VU ja

Løvskov

VU ja

Løvskov

VU ja

Løvskov

EN ja

Løvskov

EN ja

Løvskov

VU ja

Løvskov

Saproxyl?

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Bilag B

Gruppe

Zone Skove Match gap?

Anden løvskov 34

Åben skov

Nål obligat

Urørt løvskov

Saproxyl skovart 4

Åben saproxyl

Nål fakultativ

Saproxyl skovart 4

1 gap1

Saproxyl skovart 34

Saproxyl skovart 4

Urørt løvskov

Anden løvskov 34

Urørt løvskov

Åben skov

Anden løvskov 34

1 gap1

Nål obligat

Urørt løvskov

Anden løvskov 34

Saproxyl skovart 4

Urørt løvskov

Anden løvskov 34

Nål fakultativ

Anden løvskov 34

Åben skov

Anden løvskov 34

Åben skov

Anden løvskov 34

Nål fakultativ

2 gap1

Anden løvskov 34

Åben skov

0 gap2

Anden løvskov 34

Åben skov

Anden løvskov 34

Nål fakultativ

Anden løvskov 34

Nål fakultativ

Åben skov

Anden løvskov 34

Nål fakultativ

Åben skov

Nål fakultativ

Anden løvskov 34

Åben skov

Anden løvskov 34

Åben skov

4 gap1

Åben skov

Nål fakultativ

Anden løvskov 34

Åben skov

Anden løvskov 34

Åben skov

Anden løvskov 34

Chapter 4

239

Data for de 626 skovarter

Artsgruppe

Lav

Mosser

Karplanter

Arts navn

Soral‐fuscidea

Sprække‐punktlav

Stjerne‐pletlav

Tørve‐bægerlav

Turners blegskivelav

Tynd prikvortelav

Ved‐nålesvamp

Voksgul orangelav

Vorte‐blegskivelav

Almindelig hvidmos

Almindelig tørvemos

Brodspids‐tørvemos

Frynset tørvemos

Grøn buxbaumia

Trindgrenet tørvemos

Udspærret tørvemos

Almindelig ulvefod

Blåtoppet kohvede

Cypres‐ulvefod

Eng‐hejre

Femradet ulvefod

Finsk røn

Flad ulvefod

Flueblomst

Foldfrø

Forskelligblomstret viol

Fruesko

Hjortetunge

Hvidplettet lungeurt

Kantet kohvede

Kost‐nellike

Krat‐snerre

Kvast‐høgeurt

Langsporet gøgelilje

Lav rapgræs

Otteradet ulvefod

Rød hullæbe

Rød skovlilje

Skærm‐elm

Storblomstret hullæbe

Sværd‐skovlilje

Tørve‐viol

Truet hvor?

EU anx 5 art

EU anx 2 art

EU anx 5 art

EU anx 2 art

EU anx 5 art

Side 9 af 9

Rødliste TilbagegaPrioritePræference

VU nej

Løvskov

VU ja

Løvskov

VU ja

Løvskov

CR ja

Nål fakultativ

VU ja

Løvskov

CR ja

Løvskov

CR ja

Løvskov

VU ja

Løvskov

EN ja

Løvskov

‐

nej

Nål fakultativ

‐

ukendt B

Løvskov

‐

ukendt B

Løvskov

‐

ukendt B

Løvskov

‐

nej

Løvskov

‐

ukendt B

Løvskov

‐

ukendt B

Løvskov

LC nej

Nål fakultativ

VU ja

Løvskov

EN ja

Løvskov

VU ja

Løvskov

LC nej

Nål fakultativ

CR ja

Løvskov

CR ja

Løvskov

CR ja

Løvskov

EN nej

Løvskov

VU ja

Løvskov

VU nej

Løvskov

VU nej

Løvskov

VU ja

Løvskov

VU ja

Løvskov

VU ja

Løvskov

CR ja

Løvskov

VU ja

Løvskov

EN ja

Løvskov

CR ja

Løvskov

LC nej

Nål fakultativ

VU nej

Løvskov

EN nej

Løvskov

EN ja

Løvskov

VU nej

Løvskov

EN nej

Løvskov

CR ja

Løvskov

Saproxyl?

Ej‐saproxyl

Bilag B

Gruppe

Zone Skove Match gap?

Anden løvskov 34

Åben skov

3 gap1

Anden løvskov 34

Nål fakultativ

0 gap1

Anden løvskov 34

Åben skov

0 gap1

Åben skov

Anden løvskov 34

Nål fakultativ

Anden løvskov 34

Nål fakultativ

Åben skov

1 gap1

Åben skov

2 gap1

Nål fakultativ

Åben skov

4 gap1

Åben skov

Anden løvskov 34

Åben skov

1 gap1

Åben skov

2 gap2

Åben skov

3 gap1

Åben skov

3 gap1

Anden løvskov 34

Åben skov

Nål fakultativ

Åben skov

Urørt løvskov

Åben skov

Urørt løvskov

Åben skov

0 gap2

Bilag B. Artsdata. Detaljer om status, præferencer og zoner for de 626 skovarter.

Truet hvor?: De fleste af de internationalt truede arter er også truet nationalt, men kun den mest internationale vises

Rødliste: Global kategori for globalt truede. Dansk rødliste kategori i øvrigt (aldrig EU rødliste kategori).

Tilbagegang: Ifølge rødlistens information (for at undgå inkonsistenser noteres EU rapporterings trend ikke her).

Zone: Artens tilknytning eller præference for en eller flere af de definerede beskyttelses typer (z1= Normal, z2 = Nål, z3 = Aktiv, z4 = Urørt).

Skove: Antal statsskove med registreret forekomst af arten 1991‐2015.

Match: Antal statsskove henført til en matchende zone for arten i scenarie Q. (Kun noteret for prioritet A arter).

gap_Q?: Gap mellem målet (5 repræsentationer eller 100%) og match i scenarie Q.

240

Skove a priori låst til zone 4 (Urørt)

Antal arter fra hver zone gruppe

Prioritet A arter

% skov allerede i MX zoner

Areal (ha)

Nål Lysåben Total zone 3 zone 4 valgt

z2 z3 z4 z23 z24 z34 ikke‐skov Total

Alle

Skov

186

47 335

1 4

7 3 3

100

1 4

366 565

100

16 10 5

100

3 4

3 2

100

1 4

ingen

100

1 4

ingen

46 157

100 1 3 4

Side 1 af 1

Bilag C

% obs allerede

i z3

i z4

100

Enhed og skov

SHL Velling Skov

SHL Vorsø mm.

VAD Draved Skov og Kongens Mose

FYN Kasmose Skov

SDJ Midtskov

SDJ Rønhave Skov

SDJ Bolderslev Skov

Løv

103

198

109

100

Kriterier for a priori at låse til zone 4 ( Urørt):

Chapter 4

Enten 100 % af total areal urørt i forvejen OG > 5 ha skovbevokset urørt

eller > 100 ha skovbevokset urørt OG > 45% af total areal urørt

Allerede urørt inkluderer nåleskovspartier i Velling Forest, som i 1994 blev besluttet inkluderet som urørt efter en overgangsperiode til fjernelse af nål.

Skove altid i zone 4 (N = 20)

Side 1 af 1

Bilag D

% obs allerede

i z3

i z4

Chapter 4

Enhed og skov

Løv

HIM Nørreskov

181

SHL Dalgård

SHL Klostermølle mm.

KJY Ajstrup Strand

VAD Draved Skov og Konge 198

SDJ Søgård Skov

STS Klinteskov

250

HST Ravneholmene

435

HST Jægersborg Dyrehave

HST Ordrup Krat

NSJ Arresødal skov

NSJ Esrum Møllegård

NSJ Gribskov

1943

NSJ Søskoven

498

NSJ Store Dyrehave

614

NSJ Harager Hegn

259

NSJ Horserød Hegn

381

NSJ Gurre Vang

146

OSJ Farum Lillevang

OSJ Store Hareskov

181

total

5442

Areal (ha)

Nål Lysåben

227

366

286

452

1180

722

188

100

352

121

166

258

2382

2802

% skov allerede MX zoner

Antal arter fra hver zone gruppe

Prioritet A arter

Alle

Skov

Total zone 3 zone 4 valgt

z2 z3 z4 z23 z24 z34 ikke‐skov Total

472

2 14 13 4 2 42

6 1 2

7 1 2

565

16 10 5

160

4 1 1

538

1 33 32 12

46 179

129

2 6 1 2 2

895

1 30 30 7 1 47

31 147

1 3

5 7 1

3 1

3845

7 28 19 16 5 45

43 163

785

1 13 4 6 2 24

1087

1 7 10 4 3 17

359

6 2 3 1 8

616

1 3 5 2 1 13

442

11 2 2 1 8

106

3 4

295

1 10 6 2

10625

241

242

Chapter 4

Skove altid i zone 3 (N = 55)

Enhed og skov

Løv

HIM Gravlev ådal

HIM Navn Sø

HIM Rebild Bakker

VJY Tihøje Hede

SHL Østerskoven, østlige d 92

SHL Bjørnholt

SHL Hulbakken

THY Hvidbjerg Plantage

108

THY Svinkløv Plantage

132

KJY Mols Bjerge

KJY Hestehave Skov

126

KJY Kalø Hovedgård

MJY Nørlund Plantage

112

VAD Årup Hede

VAD Rømø Strand

FYN Bobakkerne

FYN Sollerup Skov

FYN Svanninge Bakker

FYN Rødme Svinehaver

FYN Thurø Rev

FYN Voderup Klint

FYN Lille Rise

BLH Skallingen

BLH Fanø Plantage

TRE Skærsø

TRE Skovhave M.M.

TRE Ussinggård Sønder og 47

TRE Drenderup Skov

TRE Svanemosen

TRE Østergård

SDJ Sønderskov

278

SDJ Rise Skov

SDJ Kalvø mv.

STS Hannenov Skov

144

STS Halskov Vænge

STS Holtug Kalkbrud

BON Almindingen

916

BON Ølene

BON Hammersholm og Ham 38

BON Slotslyngen

BON Borrelyngen

HST Strandparken

HST Kalvebod Fælled

VSJ Kongsøre Skov

183

VSJ Sonnerup Skov

NSJ Vinderød skov

NSJ Lyngby skov

NSJ Ll. lyngby mose

NSJ Grønnæsse skov

NSJ Præstevang

NSJ Hellebæk Skov

171

NSJ Egebæksvang Skov

NSJ Fredensborg Skovene

OSJ Tokkekøb Hegn

409

OSJ Ganløse Eged m.m.

129

total

3918

Areal (ha)

Nål Lysåben

114

100

107

210

138

149

740

835

332

253

715

1435

4171

254

2003

620

580

1097

406

169

153

173

221

108

113

5126 13266

Side 1 af 1

%skov alleredeMX zoner

Total

i z3 i z4 valgt

119

100

108

100

217

379

1682

717

836

213

2982

4173

189

320

2003

1254

124

102

409

124

314

2420

207

162

182

247

287

100

403

119

630

167

22310

Bilag E

Antal arter fra hver zone gruppe

Prioritet A arter%obs allerede

z3 z4 z23 z24 z34 ikke‐skov Total Alle

Skov

i z3 i z4

11 21

15 24

1 1 1

8 18

100

3 21

10 17

23 37

13 3 5 1 2

15 44

25 1 6 2 13

59 108

23 57

57 107

6 12

26 31

22 28

6 1

2 13

11 2 3

9 30

10 18

13 20

8 16

30 37

22 39

14 1 8

20 48

15 1 1

6 25

9 28

100

7 14

29 4 2 2 22

48 116

19 51

35 56

25 61

10 21

9 1 1

3 27

9 11

13 24

2 10

8 1

13 24

16 3 1

12 39

6 3

2 14

100

4 33

10 1

9 22

Chapter 4

243

Skove med variabel zone (N = 220)

Enhed og skov

HIM Skindbjerglund

HIM Præstens Plantage

HIM Smidie Kalkgruber

HIM Vilsted Sø

HIM Fællesskov

HIM Livø

VJY Klosterheden Plantag

VJY Stråsø Plantage

VJY Arealer ved Ferring og

VJY Harboøre Tange

VJY Hoverdal Plantage

VJY Fejsø Plantage

VJY Husby Plantage

VJY Arealer i Ringkøbing k

VJY Fjand Enge

VJY Vest Stadil Fjord

VJY Blåbjerg Plantage

VJY Sandfær

SHL Kobskov Øst

SHL Nordskov

SHL Snabegård Skov

SHL Velling Skov

SHL Vorsø mm.

SHL Trustrup Høje

SHL Naturstien Stenballe S

SHL Thorsø Bakker

SHL Sønderskov

SHL Himmelbjerget

SHL Høgdal

SHL Kallehave

SHL Odderholm

SHL Mariedal

SHL Ny Vissingkloster

SHL Lysbro Skov

SHL Vesterskov

SHL Gjern Bakker

SHL Kobskov Vest

VSY Læsø Plantage

VSY Skagen Plantage

VSY Bunken Plantage

VSY Råbjerg Plantage

VSY Ålbæk Plantage

VSY Tversted Plantage

VSY Uggerby Plantage

VSY Lilleheden Plantage

VSY Tornby Plantage

VSY Rubjerg Plantage

VSY Slotved Skov

VSY Blokhus Plantage

VSY Tranum Plantage

VSY Hune Plantage

THY Lodbjerg Plantage

THY Lyngby Hede

THY Stenbjerg Plantage, ve

THY Stenbjerg Plantage, ø

THY Tvorup Syd

THY Nystrup Plantage, øst

THY Nystrup Plantage, ves

THY Vandet Plantage

THY Vilsbøl Plantage

THY Tved Plantage

THY Hanstholm Vildtreserv

THY Hanstholm Kystskræn

THY Østerild Plantage

THY Madsbøl Plantage

THY Lild Plantage, vestlige

THY Lild Plantage, østlige d

THY Vester Thorup Plantag

THY Fosdal Plantage

THY Slettestrand

THY Langdal Plantage

KJY Indskovene

KJY Hjermind Skov

KJY Lysnet

Løv

Areal (ha)

NålLysåben

848

307

273

4462 1090

760

942

1114

1039

408

658

731

710

256

253

1108

337

375

529

607

188

186

107

205

229

375

786

714

698

732

484

690

345

525

132

592

127

427

276

233

280

237

104

414

701

1408 2243

340

616

73 1196

385

544

526

317

933

982

833

311

234

612

326

473

603

829

434

80 3208

245

923

450

325

436

631

584

367

651

1059

328

232

140

549

122

461

113

skov alleredMX zoner

Total i z3 i z4 valgt

50 82 12

3 4

1 2

1 3

861

1 3

592 32

2 3 4

334 14 11

3 4

6399

2 1 2 3

1917

1 2

1 3

1114

1 3

1562

2 3 4

1588

3 1 2 3

1066

1 2

1 3

257

1 3

1108

1 3

802

1 3

569

1 3

3 4

986 17 11

2 3

135 10

1 3

335 23 54

1 4

1 3

1 3 4

171

1 2

684 35

3 4

154

1 3

2 4

1 3

149

7 14

3 4

653 29 27

3 4

33 69 14

1 3

77 14

3 4

1875

3 4

1474

2 3 4

1221

6 1 2 4

409

1 2

736

1 2

812

1 2

786

1 2

388

1 2

560

2 3

2 3 4

141 22

1 3

1167

1 2

3922

4 1 3 4

1 2

1031

1 2

1270

1 2

941

1 3 4

931

9 1 3 4

2016

1 3

1239

1 2

863

1 2

444

1 4

1218

3 4

1444

2 3 4

3288

2 1 2 3

252

3 4

1595

1 3 4

773

3 4

1287

1 2

1083

1 2

1504

7 1 2 3

520

3 4

549

1 3

176

3 4

825 13

2 4

265 12

3 1 2 3 4

1 3

Side 1 af 3

Bilag F

187

847

216

115

200

100

191

103

250

184

375

272

101

143

181

222

117

147

276

Antal arter fra hver zone gruppe

Prioritet A arter%obs allerede

MX zone resulta

z2 z3 z4 z23 z24 z34 ikke‐skov Total Alle

Skov

i z3

i z4 O3

9 5 2 1 9

1 27

11 14

1 6 3 1

3 24

1 7

11 27

4 10

29 52

14 8

12 1 2

20 57

17 21

12 10 2 9 1 9

30 73

12 5

10 33

2 7

4 1

17 31

9 11

21 27

7 15

1 7

9 23

20 8 2 12 2 16

16 76

8 21

7 3 3

6 27

6 17

100

1 2 2 4

6 32

15 24

1 12 1 5

20 44

9 16

2 4 1

5 15

3 6 7 7

9 48

1 1 1 3

1 16

9 11 2 8

38 76

11 9 1 11 2 4

38 76

13 12 1 9

50 89

5 5

27 40

6 8

23 44

4 5

2 1 2

22 36

1 6

25 35

2 5

14 25

2 7

27 39

6 1

5 2 4

4 22

4 5

13 25

2 10

5 1 1

31 50

5 2

4 14

1 9

4 1 3

28 46

9 17

8 16

1 5

11 25

2 10

24 41

7 7

7 1 3

10 35

7 7

11 33

4 4 1 2

4 15

2 13 2 6 1 7

20 51

11 9

29 64

1 10

42 61

8 2

18 28

3 7 1 8 1 10

19 49

1 4

14 22

1 3

2 1 3

11 21

2 6

3 1 7

38 57

7 9

4 1 2

23 46

4 8 2 6

22 51

9 12

1 1 1 1

2 10 2 2

11 36

1 3 1 2

4 14

244

Chapter 4

Side 2 af 3

Skove med variabel zone (N = 220)

Enhed og skov

KJY Helgenæs Syd

KJY Dragsmur

KJY Ørnbjerg Mølle

KJY Gjerrild Strand

KJY Viborg Plantage

KJY Hald Hovedgård

KJY Stanghede

KJY Hald Sø

KJY Inderø Skov

KJY Ringelmose Skov

KJY Markerne

MJY Sønderskoven

MJY Rydhave Skov

MJY Mønsted Kalkgruber

MJY Alheden Skov

MJY Kompedal Plantage

MJY Haunstrup Brunkulsle

MJY Bøllingsø

MJY Hastrup Plantage

MJY Kilderne

MJY Palsgård Skov

MJY Gludsted

VAD Hønning Plantage

VAD Lovrup Skov

VAD Soldaterskoven

VAD Magisterkog

VAD Klaskeroj Skov

VAD Barsbøl Skov

VAD Åtte Bjerge

VAD Stursbøl Hegn

VAD Lindet Skov

VAD Stensbæk Plantage

VAD Kirkeby Plantage

VAD Vråby Plantage

VAD Tingdal Plantage

FYN Kasmose Skov

FYN Topgården

FYN Frøbjerg Bavnehøj

FYN Flyvesandet

FYN Nordlangeland

FYN Ristinge

FYN Sydlangeland

BLH Nyminde Plantage, no

BLH Nyminde Plantage, sy

BLH Kærgård Plantage, no

BLH Vrøgum Plantage

BLH Vejers Plantage, sydlig

BLH Ål Plantage

BLH Oksby Plantage

TRE Gødding Skov

TRE Frederikshåb Plantage

TRE Gyttegård Plantage

TRE Randbøl Hede

TRE Grund Skov

TRE Stagsrode Skov

TRE Kærskov

TRE Skablund Skov

TRE Rold Skov

TRE Sønder Stenderup Mi

TRE Tapsøre Statsskov

TRE Kongens Kær

SDJ Nørreskov

SDJ Oleskobbel

SDJ Lambjerg Indtægt

SDJ Midtskov

SDJ Rønhave Skov

SDJ Dybbøl

SDJ Dyrehaven

SDJ Rinkenæs Skov

SDJ Skodsbøl Skov

SDJ Hønsnap Skov

SDJ Frøslev Plantage

SDJ Frøslev Mose

SDJ Areal ved Assenholm

Løv

132

221

119

251

243

149

181

120

179

145

160

308

106

109

176

200

148

546

192

160

Areal (ha)

NålLysåben

174

275

201

198

342

388

640

185

511

2062

378

126

575

404

140

362

720

235

2597

689

256

148

150

227

244

191

365

320

166

235

120

333

255

351

541

332

398

285

681

133

387

284

377

127

431

292

181

504

220

205

252

710

122

198

343

327

Bilag F

538

skov alleredMX zoner

Antal arter fra hver zone gruppe

Prioritet A arter%obs allerede

MX zone resulta

Total i z3 i z4 valgt

z2 z3 z4 z23 z24 z34 ikke‐skov Total Alle

Skov

i z3

i z4 O3

209

3 4

8 2

14 27

1 3

20 26

97 37

1 4

3 1 1

5 10

1 2

5 12

437

3 1 2 3

4 10

502

6 34

3 4

8 3 2

9 38

249

3 4

6 1 10

13 36

342

1 3 4

78 82 63

3 4

2 21

193

3 4

5 1 1

13 32

390

2 3 4

1 4

5 12

920

1 2

2 4

6 16

31 61 30

1 3

3 4

6 2

4 13

843

1 2

1 4

7 18

2683

1 2 4

5 7 1 8 1 1

14 37

236

1 2

630

1 2

1 9

16 31

631

1 3

2 8

4 1 4

5 24

456 16

1 3

28 47

1104

1 2 3 4

1 4

23 40

3467

1 2 3

8 8

22 53

524 10

1 2

3 7

10 26

382 32

1 4

1 4 1 2

3 13

173

1 3

4 13

227

1 3

20 26

118 11

1 3

90 15

1 2

31 86

1 3

465

1 2 3

3 7

6 19

397

1 4

2 7 1 3

2 22

767

1 2

2 9

5 1 3

27 47

406

1 2 3

1 3

1 1 1

17 24

154

1 2 3

1 6

9 17

1 3

8 12

20 44 28

3 4

3 2

1 16

1 3 4

1 3

1 3 4

1 2 1

7 12

391

3 4

5 2 1

2 12

1 3

11 14

447

3 4

2 15 2

21 52

630

1 3

1 5

21 32

920

1 2

3 4

20 32

754

22 1 2 4

1 5 1 4

22 35

896

1 2

2 2

13 19

691

2 3

2 4

17 28

599

1 3

1 6

12 22

748

1 2

3 3

16 27

294

1 3 4

1 2

804

1 3

3 9

11 28

491

1 3 4

1 9

5 1 6

37 59

712

1 3

1 9

33 48

154

3 4

6 2 1

1 14

218

3 4

1 10 5 1

1 58

1 2

1 4

1 3

241

1 4

2 4

232

1 3

736

7 1 3 4

3 20

1 3

2 1

1 1 3 4

6 10

100

1 4

none

100

1 4

none

198

4 1 2 4

5 1

111

4 21 1 3 4

4 3 1

3 17

275

1 3

1 9 1

5 21

1 3

1041

1 2

2 7

2 1 7

14 33

335

3 4

1 10 2 2

34 56

18 72

1 3 4

14 19

Chapter 4

245

Skove med variabel zone (N = 220)

Enhed og skov

SDJ Bolderslev Skov

SDJ Jørgensgård Skov

SDJ Lerskov Plantage

SDJ Hytterkobbel

SDJ Pamhule Skov

SDJ Østerskov

SDJ Slivsø

STS Bangsebro Skov

STS Bøtø Nor

STS Lysbro Mose ved Hejr

STS Ulvshale Skov

STS Kongskilde

STS Hørhaven

STS Dybsø

BON Kilen

BON Blykobbe Plantage

BON Ejendommen Lassen

BON Pedersker Skov

BON Hammerknuden

BON Del af Ringebakkerne

BON Tuleborg

BON Rø Plantage

HST Jægersborg Hegn

HST Søllerød Kirkeskov

HST Geel Skov

HST Enrum Skov

HST Rygård

HST Charlottenlund Skov

HST Kongelunden

HST Vestamager

HST Folehave

HST Rude Skov og Frihede

HST Vestvolden

VSJ Rørvig Sandflugtsplan

VSJ Korshage

VSJ Hov Vig

VSJ Høve Skov

VSJ Kårup Skov

VSJ Sanddobberne

VSJ Røsnæs

VSJ Diesbjerg

VSJ Bidstrup Skovene

VSJ Myrdeskov

VSJ Boserup Skov

VSJ Hyrdehøjskoven

NSJ Sonnerup Skov

NSJ Arresø

NSJ Tisvilde hegn

NSJ AP‐Møllergrunden

NSJ Aggebo og Græsted H

NSJ Freerslev hegn

NSJ Stenholt Vang

NSJ Esrum Sø

NSJ Nakkehoved

NSJ Hornbæk Plantage

NSJ Teglstrup Hegn

NSJ Klosterris Hegn

NSJ Nyrup Hegn

NSJ Danstrup Hegn

NSJ Krogerup Skovene

NSJ Lave Skov og Stejlepla

OSJ Ryget

OSJ Nørreskoven

OSJ Furesø

OSJ Terkelskov

OSJ Ganløse Ore

OSJ Bøndernes Hegn

OSJ Lille Hareskov

OSJ Jonstrup Vang

OSJ Vestskoven

OSJ Ravnsholt

OSJ Slagslunde skov

Løv

109

145

234

145

213

426

145

208

236

185

321

646

168

338

144

210

198

105

306

204

109

197

115

169

216

110

174

556

150

188

Areal (ha)

NålLysåben

198

100

308

285

136

170

320

150

1730

120

130

289

136

241

166

195

181

4102

1280

375

1734

138

100

122

103

154

932

150

183

604

123

Side 3 af 3

Bilag F

skov alleredMX zoner

Antal arter fra hver zone gruppe

Prioritet A arter%obs allerede

MX zone resulta

Total i z3 i z4 valgt

z2 z3 z4 z23 z24 z34 ikke‐skov Total Alle

Skov

i z3

i z4 O3

157

100 1 3 4

2 11

100

168

3 4

2 1

140

1 2

1 4

1 2

3 11

137

1 3 4

353 24

3 4

11 35

185

1 3

198

1 3

13 17

3 4

3 2

111

1 3

15 28

1 3 4

3 1

410

5 23

2 4

2 17 5 6 1 13

36 80

324

3 4

14 6

12 39

1 3

136

1 2

12 16

1 2

204

6 18

2 4

3 5 1 4

4 19

1 3

197 51

2 3

2 17

32 64

1 2

4 12

1 4

1 1

593

2 3 4

6 11

6 29

554 15

3 4

1 17 13 7

18 78

3 4

1 1

1 10

1 14

193

2 3 4

1 6 5

2 7

1 22

1 3

162

1 4

4 2

3 11

75 13

3 4

2 2

1 2

331

3 4

1 16 2 2 1 10

16 48

1966

3 4

16 4 3

41 71

263

1 2 3

1 3 1

571 19

8 1 2 4

2 11 4 5 3 18

9 52

290

3 4

7 2 1

3 19

206

1 2

4 1

6 13

1 2 3

1 1 1

13 19

266

1 4

12 1 1

24 44

1 2

2 1

170

1 2

2 4

1 1

14 22

18 1 3 4

7 10

196

2 3 4

1 14 4 2 2 11

30 64

1 3

20 22

1021

3 4

1 15 6 6 4 25

20 77

3 4

2 1

224 13

3 4

10 11

1 20

2 44

1 4

5 2

1 3

4121

1 4

9 1 1

9 25

1992

2 3 4

25 30 12 16 6 37

66 192 113

1 3 4

9 10

188

1 4

2 1 1 3 6

1 14

275

4 1 3 4

6 1

1 11

313

8 1 2 3

2 3

3 1 6

3 18

1734

1 3

9 18

1 3

198

2 3 4

3 1 1 4 2 6

4 21

544 26 18

3 4

8 3 4 1 18

22 56

353

1 2 3

1 2 1 3

3 15

242

3 4

2 6 1 1 1 5

1 17

325

1 2

1 8

1 1 8

6 25

223

1 3

1 1 1

1 3

149 17 30

3 4

4 3 3 1 13

16 40

194 12

9 1 3 4

4 13 1

1 39

932

1 3 4

4 1

6 20

54 45 44

3 4

10 2

5 25

396

2 4

2 7 2

1 10

3 25

1 3 4

9 2 1

3 19

159

1 4

6 2

2 17

240 11

3 4

13 5 1

8 36

1342

2 4

1 9 4 2 3 8

17 44

312

3 4

10 1 1 1 7

3 23

224

3 1 3 4

2 4

6 15

246

Chapter 4

Skove altid i zone 1 (N = 681)

Enhed og skov

Løv

HIM Svalebakken

HIM Stenholmen

HIM Julstrup Sø

HIM Øer ved Hals

HIM Lille Ravnkilde

HIM Rebstrup

HIM Vrå Mølle

HIM Blåkilde Dambrug

HIM Dokkedal

HIM Mosskov

133

HIM Halkær Mølle

HIM Naturstien Nibe‐Hau

HIM Vokslev Kalkgrav

HIM Aars Skov

104

HIM Drastrup Skov

209

HIM Nørager

HIM Poulstrup Skov og Ka 8

HIM Sct Nicolai Bjerg ved

HIM Arden Skov

HIM Østre Banevej Skov 41

HIM Hyllebjerg

HIM Grønnerup Strand

HIM P‐plads ved Svingelb

HIM Lille Skovsgårds Hag

HIM Lundshøj

HIM Als Havbakker

HIM P‐plads ved Als Odd

HIM Muddermarens Ø

HIM Pletten

HIM Jenle Plantage

14 68

HIM Plovmandshøj Plant 20 68

HIM Frendrup hede ‐ Vok 2

VJY Rønland Sandø

VJY Gørding Havn

VJY Skærum Mølle

VJY Langerhuse

VJY Sønderholmene og P

VJY Høfde 8

VJY Arealer ved Fjaltring

VJY Bøvling Klit

VJY Rammedige

VJY Femhøjsande

8 28

VJY Øster Lem Hede

VJY Nørre Vium Brunkul 27 10

VJY Troldhede Brunkulsl 15 19

VJY Ahlergaarde Brunku

VJY Lystbæk

26 71

VJY Rejkær Hede

VJY Arealer i Holmsland

VJY Feldsted Kog

VJY Skårmose Plantage

30 60

VJY Ølgryde Plantage

75 273

VJY Døes Højene

VJY Møborg Skov

45 60

VJY Storåen,Naur

VJY Naturskolen Kærgår

VJY Resenborg Plantage 19

VJY Birkild Hede

VJY Livbjerggård Strand

VJY Grisetå Odde

VJY Plethøj

VJY Bøløre Odde

Areal (ha)

Nål Lysåbe

320

173

121

120

140

239

297

1483

Side 1 af 11

Bilag G

%skov allered Antal arter fra hver zone gruppe Prioritet A arte%obs allerede

Total i z3 i z4 z2 z3 z4 z23 z24 z34 ikke‐skovTotal Alle Skov

i z3 i z4

625 16

8 23

221

372

233

4 13

140

336

9 11

297

1483

14 14

119

413

154

Chapter 4

247

Skove altid i zone 1 (N = 681)

Enhed og skov

Løv

VJY Søndbjerg Strand

VJY Odby

VJY Skalstrup Skov

VJY Åbjerg Skov

VJY Harpøth Bæk og Dam

VJY Nees Sandø

VJY Toftum Bjerge og Je

VJY Gejlgård Bakke

SHL Arealer ved Skæring

SHL Ajstrup Strand Og N

SHL Areal ved Brabrand

SHL Stendysse ved Orms

SHL Kollens Mølle

SHL Bærmose‐ Himmerig 132

SHL True Skov

260

SHL Hvinningdal Skov

SHL Solbjerg Skov

SHL Anebjerg Skov

SHL Geding Skov

SHL Rugballegård Skov

SHL Arealer i og ved Bryr

SHL Illerup Ådal

SHL Opholds‐ og stiareal

SHL Areal ved Solbjerg S

SHL Ring Kloster

SHL Fårbjerg

SHL Naturstien Horsens‐

SHL Slaggård Banke

SHL Tunø og småøer

SHL Arealer på Samsø

SHL Meden Kirkeruin

SHL Kysing Strand

SHL Hølken Strand

SHL Spøtterup Strand

SHL Hou Strand

SHL Hundslund‐Åkjær na

SHL Mosevej

SHL Lovdal Skov

SHL Østre Stenhule

SHL Alling, vestlige del

SHL P‐plads syd for Alling

SHL Arealer ved Masken

SHL Areal ved Knudsø sy

SHL Knudhule Strand

SHL Vestbirksøerne

SHL Mossø Brå

SHL Bryggebjerg

SHL Birkhede

SHL Øm Kloster

SHL Pindals Mose

SHL Vilholt

SHL Siim Skov

SHL Østerskoven, vestlig

SHL Sminge kanolejrplad

SHL Anderiet mm.

SHL Bøsmølle Bro

SHL P‐plads ved Nebel B

SHL Trækstien

SHL Sorring

VSY Nordmarken

VSY Danzigmand og Blød 3

VSY Vesterø Sønderland

Side 2 af 11

Bilag G

Areal (ha)

%skov allered Antal arter fra hver zone gruppe Prioritet A arte%obs allerede

Nål Lysåbe Total i z3 i z4 z2 z3 z4 z23 z24 z34 ikke‐skovTotal Alle Skov

i z3 i z4

6 141

55 152

21 23

19 19

58 215

147 443

53 147

32 129

36 111

7 28

33 106

12 12

32 32

84 84

33 35

321 329

20 24

10 10

3 33

16 68

4 27

18 19

58 59

31 32

11 47

2 18 55

16 16

57 57

3 2

98 102

347 349

14 18

248

Chapter 4

Skove altid i zone 1 (N = 681)

Enhed og skov

VSY Rønnerne

VSY Borfeld

VSY Hvide Fyr

VSY Byfogedskoven

VSY Skiverbakken

VSY Skiveren Plantage

VSY Tversted Rimmer

VSY Råbjerg Mose

VSY Videslet Engen

VSY Hirsholmene

VSY Areal ved Hulsig

VSY Tversted Klit

VSY Nejst Plantage

VSY Kærsgård Strand

VSY Lien Skallerup

VSY P‐plads ved Skalleru

VSY Areal ved Lønstrup

VSY Mårup Kirke

VSY Rubjerg Knude Fyr

VSY Kajholm

VSY P‐plads ved Kodal

VSY Hjørring‐Astrup skov

VSY Måstrup Mose

VSY Mosbjerg

VSY P‐plads ved Åsted Å

VSY Areal ved Sulbæk

VSY Solsbæk Strand

VSY Søheden Skov

VSY Slettingen

VSY Nybæk Plantage

VSY Munkens Klit

VSY Lille Norge

VSY Fårup Klit

VSY Pirups Hvarre

VSY Grishøjgårds Krat

VSY Gjøl Bjerg

VSY Store Vildmose

THY Agger Tange

THY Egebjerg

THY Aaby Skoven

THY Rønhede Plantage

THY Fjordholmene

THY Ydby‐Nygård Plantag

THY Hurup Golfskov

THY Faddersbøl

THY Sundby Sø

THY Øer omkring Mors

THY Ejerslev Vang

THY Areal ved Hanklit

THY Buksør Odde

THY Legind Vejle

THY Tissing Vig

THY Arealer Ved Søndre

THY Tvorup Nord

THY Vangså Hede

THY Snedsted Byskov

THY Kronens Hede Planta

THY Bavn Plantage

THY Sjørring Volde

THY Arealer ved Langdys

THY Eshøj

THY Hanstholm Byplanta

Løv

Areal (ha)

Nål Lysåbe

677

106

196

161

869

765

132

618

Side 3 af 11

Bilag G

29 455

5 172

18 11

%skov allered Antal arter fra hver zone gruppe Prioritet A arte%obs allerede

Total i z3 i z4 z2 z3 z4 z23 z24 z34 ikke‐skovTotal Alle Skov

i z3 i z4

700

10 12

18 21

161

9 13

196

1 4

11 16

16 17

8 21

116

165

10 18

869

5 11

765

17 25

102

132

552

1 1

9 15

623

16 22

196

121

Chapter 4

249

Skove altid i zone 1 (N = 681)

Enhed og skov

Løv

THY Vigsø rallejer

THY Vigsø og Ballerum P 47

THY Korsø Plantage

THY Hjardemål Plantage 61

THY Tømmerby Kær

THY Areal ved Selbjerg V

THY Frøstrup Skov

THY Aggersborg

THY Kollerup Plantage

THY Hingelbjerg

THY Hingelbjerg Mose

THY Husby Hole

THY Bredkær Plantage

KJY Udskovene

201

KJY Randers Nørreskov

KJY Randers Nordre Fæl 44

161

KJY Vindum Skov

KJY Busbjerg

KJY Frisenvold Laksegård 1

KJY Frydensbjerg og Kat

KJY Hohøj Arealer

KJY Hadsund Bane

KJY Mosely

KJY Hærup P‐plads

KJY Ulbjerg Klint

KJY Sundstrup Arealerne

KJY Rønnen

KJY Bjødstrup Strand

KJY Karpenhøj

KJY Vænge Sø

KJY Mågeøen

KJY Areal ved Stubbe Br

KJY Natursti Ebeltoft‐Gr

KJY Vibæk Strand

KJY Holme strandareale

KJY Hyllested Bjerge

KJY Bisballe‐Almind

KJY Bruunshåb

KJY Klostermarken

KJY Klokkerholm Skov

KJY Randers Sønderskov 24

KJY Øer i Randers Fjord

KJY Elløv Enge

MJY Borbjerg‐Nørreskove 453

MJY Sjørup Skov

MJY Arealer på Nordfur

MJY Jenle

MJY Brokholm Sø

MJY Havbjerg Skov

MJY Vinderup Skov

MJY Geddal Strandenge

MJY Spøttrup Sø

MJY Arealer på Lundø

MJY Egekrat ved Aulum

MJY Småarealer Salling

MJY Grynderup Sø

MJY Arealer ved Durup

MJY Løvbakke Skov

101

MJY Areal ved Rabis

MJY Ikast Byskov

MJY Søby Brunkulslejer

MJY Funder‐Ejstrup natu 13

Areal (ha)

Nål Lysåbe

340

160

356

434

707

388

134

115

254

185

136

116

135

105

262

141

126

113

215

Side 4 af 11

Bilag G

1662

208

129

%skov allered Antal arter fra hver zone gruppe Prioritet A arte%obs allerede

Total i z3 i z4 z2 z3 z4 z23 z24 z34 ikke‐skovTotal Alle Skov

i z3 i z4

340

1 5

9 15

286

1 1

845

1 1

1156

11 2 6

21 32

254

479

2 3

7 18

350 20

140

324 27

135

9 12

39 98

100

132 20

2376

3 7

2 17

331

6 13

143

146

171

126

10 11

114

100

186

366

1 4

6 16

250

Chapter 4

Skove altid i zone 1 (N = 681)

Enhed og skov

Løv

VADRåbjerg Plantage

VADRenbæk Plantage

VADArrild Plantage

VADNørreskov

VADGasse Høje

VADNørresø og Hestholm

VADHaraldsholm Skov

VADJelssøerne

VADSidekanal ved Lintru

VADParceller i Sømose o

VADJættestuer ved Over

VADSkrydstrup Skov

VADLindet Mose

VADMandbjerg Skov

VADVarming og Nørbæk 99

VADToftlund Skov

VADDankirke

VADBevtoft Plantage

VADGammelskov

VADBjerreskov

VADFavrholt Skov

VADTange Bakker

VADTange Enge

VADTvismark Plantage

VADVestergårde Bjerge

VADAlbatros

VADKlægtagningsarealer

VADArealer ved Husum

VADAreal på Mandø

FYN Røjle Klint

FYN Fortidsminder

FYN Småøer

FYN Vestermose Skov

FYN Holmeskoven

FYN Klakkebjerg

FYN Arealer ved Vissenbj

FYN Feddet

FYN Helnæs Made

FYN Nørreby Hals

FYN Fuglsanggård

FYN Otterup Byskov

FYN Fjordmarken

FYN Vigelsø

FYN Kirkendrup Skov

132

FYN Elmelund Skov

FYN Fyns Hoved

FYN Bogensø Strand

FYN Lods Huse

FYN Lærkedal

FYN Sønderskovgård

FYN Storelung

101

FYN Ringe Skov

FYN Naturstien Ringe ‐ K

FYN Trente Mølle

FYN Lyø

FYN Avernakø

FYN Tåsinge Vejle

FYN Vorbjerg

FYN Borgnæs

FYN Gråsten Nor

FYN Toftegårdsskoven

FYN Egehovedskoven

Areal (ha)

Nål Lysåbe

296

134

240

248

108

124

Side 5 af 11

Bilag G

%skov allered Antal arter fra hver zone gruppe Prioritet A arte%obs allerede

Total i z3 i z4 z2 z3 z4 z23 z24 z34 ikke‐skovTotal Alle Skov

i z3 i z4

117 21 15

205

1 4

13 15

129

100

58 58 14

458

3 1 4

62 16

191

1 4

112

7 10

302

10 12

250

11 18

132

150

270

9 14

169

Chapter 4

251

Skove altid i zone 1 (N = 681)

Enhed og skov

FYN Hov Østerland

FYN Rudkøbing Fredskov

FYN Humble Byskov

FYN Næs

BLH Blåbjerg Plantage

BLH Nørre Nebel Skov

BLH Golfbane

BLH Oversigtsareal ved H

BLH Årgab

BLH Bavnebjerg

BLH Havrendingen

BLH Bjergeborg

BLH Bjerregård

BLH Holmsland Klit

BLH Tipperne

BLH Klægbanken mm.

BLH Præstens ø og Tykrå

BLH Polde

BLH Øer i Nymindestrøm

BLH Skjern Å

BLH Lønborg Hede

BLH Kærgård Plantage, s

BLH Vejers Plantage, nor

BLH Sig kapelbanke

BLH Bordrup Plantage

BLH Ho Plantage

BLH Langli

BLH Fyrpasserboligen

BLH Hafniagrunden

BLH Oles Dige

BLH Lodder Øst for Oksb

BLH Kikkebjerg Plantage

BLH Torp

BLH Sønderho

BLH Søren Jessens Sand

BLH Trinden og Keldsand

TRE Engelsholm Skov

TRE Tykhøj Krat

TRE Refstrup Skov

TRE Nørup Plantage

TRE Vingsted Mølle

TRE Randsfjord Arealer

TRE Vognkær Enge og Hv

TRE Børkop Vandmølle

TRE Fårup Skov

TRE Fire Høje

TRE Sophienlund

TRE Haltrup Hede

TRE Troldhedebanen Ves

TRE Eg Rasteplads

TRE Tirsbjerg Plantage

TRE Bjerge Skov

TRE Tønballegård

TRE Bankehave

TRE Boller Nederskov

TRE Boller Overskov

TRE Klokkedal

TRE Ustrup Bjerge

TRE Dybdal

TRE Dallerup Skov

TRE Lystrup Skov

TRE Borringholm

Løv

124

100

Side 6 af 11

Bilag G

215

122

Areal (ha)

%skov allered Antal arter fra hver zone gruppe Prioritet A arte%obs allerede

Nål Lysåbe Total i z3 i z4 z2 z3 z4 z23 z24 z34 ikke‐skovTotal Alle Skov

i z3 i z4

7 24

8 34

4 100

806

467 1396

8 2 6

21 36

46 163

37 37

154 154

91 91

33 33

28 36

154 154

689 689

17 21

67 67

112 112

2 1963 1967

29 36

344 344

14 18

192

412 608

1 2

17 22

358

305 693

1 4

13 19

625

185 856

1 3

15 20

302

95 423

2 2

12 17

100 100

17 20

11 11

8 36

93 93

54 54

64 147 20

7 86

1 27

0 11

23 35

30 40

27 35

3 10

1 10

48 61

1 24

25 282

3 12

23 71

6 69

15 156

2 59

5 63 14

1 28

1 22

7 63

2 36

252

Chapter 4

Skove altid i zone 1 (N = 681)

Enhed og skov

TRE Sebberup Skov

TRE Sønder Stenderup N

TRE Stenderup Hage

TRE Sønder Stenderup S

TRE Skibelund

TRE Grønninghoved Stra

TRE Vargårde Skov

TRE Trommersgård Skov

TRE Fovslet Skov

TRE Hoppeshuse

TRE Harte Skov

TRE Troldhedebanen

TRE Rastepladser ved Ny

TRE Hakonsminde

TRE Søballegård

TRE Tørrepladsen

TRE Klingebæk

TRE Ravning Enge

TRE Bindeballe Station

TRE Troldhedebanen Syd

SDJ Karholm

SDJ Øvelgunde Fredskov

SDJ Fryndesholm

SDJ Græskobbel

SDJ Blommeskobbel

SDJ Rumohrsgård Dyreh

SDJ Ketting Nor

SDJ Oldenor

SDJ Augustenborg Skov

SDJ Made Skov

SDJ Arnkil Skov

SDJ Arnkil Maj

SDJ Fredskov

SDJ Mjang Dam

SDJ Hartsø Strand

SDJ Nydam Mose

SDJ Roden Skov

SDJ Adsbøl Dam

SDJ Sø‐ og Lystskovareal

SDJ Over‐ og Nederstjer

SDJ Buskmose Skov

SDJ Avnbøl Sned

SDJ Bøffelkobbel

SDJ Skelde Folekobbel

SDJ Skelde Kobbelskov

SDJ Opholdsarealer ved

SDJ Helligsø

SDJ Nybøl

SDJ Arealer ved Strande

SDJ Kelstrup Fredsskov

SDJ Kelstrup Plantage

SDJ Rode Skov

SDJ Kiskelund Plantage

SDJ Kollund Skovholm

SDJ Kruså Skov

SDJ Mølleskov

SDJ Store Okseø

SDJ Gårdbæk Skov

SDJ Waldeck Skov

SDJ Rønshovedskovene

SDJ Gammelmose Skov

SDJ Lyreskoven

Løv

331

144

199

103

Areal (ha)

Nål Lysåbe

100

128

Side 7 af 11

Bilag G

115

147 130

16 18

29 26

%skov allered Antal arter fra hver zone gruppe Prioritet A arte%obs allerede

Total i z3 i z4 z2 z3 z4 z23 z24 z34 ikke‐skovTotal Alle Skov

i z3 i z4

141

412

5 11

193 14

2 13

18 100

50 14

260

1 10

167

240

4 10

3 100

100

31 100

1 100

20 22 13

106

128

145

4 98

100

75 10 10

5 11

4 1

331

3 1 5

1 10

3 11

4 15 68

100

Chapter 4

253

Skove altid i zone 1 (N = 681)

Enhed og skov

Løv

SDJ Parcel i Søndermose

SDJ Kragelund Mose

SDJ Vejbæk Skov

SDJ Bommerlund Planta 155

SDJ Oksekær

SDJ Skov ved Bjerndrup

SDJ Bøghoved

SDJ Areal syd for Sønder

SDJ Hostrup Krat

SDJ Kæmpehøj i Hostrup

SDJ Arealer på Varnæs H 1

SDJ Årtoft Plantage

SDJ Torp Plantage

SDJ Hjordkær

SDJ Årup Skov

195

SDJ Sønderskov, Aabenr 86

SDJ Hjælm

SDJ Vestermark

SDJ Langbjerg Skov

SDJ Søst Skov

SDJ Nørreskov, Aabenra 76

SDJ Rhedersborg Skov

SDJ Rugbjerg Plantage

SDJ Rundemølle

SDJ Jagtprøvebane

SDJ Strangelshøj

SDJ Hjarup Mose

SDJ Arealer ved Potterhu

SDJ Hop Sø

SDJ Tormaj

SDJ Fredshule

SDJ Abkær Mose

SDJ Femhøje

SDJ Haderslev Søndersko 70

SDJ Hjelmvrå

SDJ Teglholt

SDJ Tørning Mølle

SDJ Sandkule

SDJ Ladegård eng

SDJ Elkær dambrug

SDJ Vesterskov

151

SDJ Nautrupgård Skov

SDJ Tamdrup Høj

SDJ Årø Skov mm

SDJ Keldet Skov

SDJ Loft Skov

SDJ Revsø Skov

137

SDJ Gravhøj i Sommerst

SDJ Areal ved Råde Stran

STS Ovstrup Skov

159

STS Sønder Kohave

STS Gedser Fyr

STS P‐pladser på Falster

STS Albuen

STS Langødyssen og Bav

STS Majbøllereservatet

STS Enehøje

STS Teglværksskoven

STS Vildmarksskoven

STS Krogsbølleskoven

STS Krukholm Skov

STS Hyllekrog Fyr

Side 8 af 11

Bilag G

Areal (ha)

%skov allered Antal arter fra hver zone gruppe Prioritet A arte%obs allerede

Nål Lysåbe Total i z3 i z4 z2 z3 z4 z23 z24 z34 ikke‐skovTotal Alle Skov

i z3 i z4

10 10

12 87

368

151 674

0 2 5

2 11

2 20

4 31

79 80

134

43 250

38 209

55 285

11 104

5 50 14

14 120

13 50

12 77

13 97

4 37

112

19 164

5 38 13

19 19

11 11

4 11

9 11

37 37

71 147 34

2 51

17 40 12

68 90

2 47 58 40

10 11

20 187 12

3 10

48 54

6 11

1 18

34 213

40 221

8 70

43 43

8 10

34 34

97 100

16 24

5 20

14 37

254

Chapter 4

Skove altid i zone 1 (N = 681)

Enhed og skov

Løv

STS Krukholm Lille Skov

STS Øer på søterritoriet

STS Mark ved Bursø

STS Udby Skov

STS Hegnede Skov

STS Nyord

STS Areal ved Hjelm Bug

STS Bakkely Skov

STS Tårnborggård

STS Galgebakke og Hash

STS Højbjerg Skov

STS Kobæk Skov

STS Glumsø Statsskov

STS Areal ved Sandvig

STS Prins Carls Skole

STS Gåsetårnet

STS Faksinge Skov

STS Vrangstrup Enge v. S

STS Anneliselunden

STS Stevns Fyr

STS Avnø

STS Rønnebæk fælled

STS Ladby Skov

STS Fælleseje Skov

STS Vridsløse Skov

STS Even Skov

STS Fakse Kalkbrud

STS Fodsporet

STS Øer på søterritoriet

BONArealer ved Arnager

BONAreal ved Sose Odde

BONAreal ved Nordbakke

BONAreal ved Gubbegår

BONEgeby fortidsmindeo

BONAreal ved Hundsemy

BONUdkæret

BONKærgården

BONSkovholt

BONRingborgen

BONArealer ved Vandmø 1

BONAreal ved Stammers

BONByggehøj

BONRø Præsteskov

BONØster Borregårds Sk 24

BONAreal ved Bobbeå

BONSalene Bugt

HST Trørød Hegn

HST Kohave Skov

HST Ermelunden

HST Mikkelborg

HST Bredelte

HST Hørsholm Slotshave

HST Bistrup Hegn

HST Stumpedysse Hegn

HST Sjælsø Lund

VSJ Grønnehave Skov

147

VSJ Annebjerg Skov

VSJ Nakke Skov

VSJ Højby Sø

VSJ Skansehage

VSJ Øer på søterritoriet

VSJ Nygård Sø

Areal (ha)

Nål Lysåbe

136

Side 9 af 11

Bilag G

207

%skov allered Antal arter fra hver zone gruppe Prioritet A arte%obs allerede

Total i z3 i z4 z2 z3 z4 z23 z24 z34 ikke‐skovTotal Alle Skov

i z3 i z4

105

136

9 11

112

211

20 32

5 11

100

4 27

2 1

203

9 18

Chapter 4

255

Skove altid i zone 1 (N = 681)

Enhed og skov

VSJ Ulkerup Skov

VSJ Stokkebjerg Skov

VSJ Grevinge Skov

VSJ Hønsehals Skov

VSJ Bognæs Skov

VSJ Mosemark Skov

VSJ Øer på søterritoriet

VSJ Ellinge Indhegning

VSJ Jyderup Skov

VSJ Bjergene

VSJ Vollerup Skov

VSJ Klosterskov

VSJ Overby Lyng

VSJ Stenstrup Troldstue

VSJ Lærkereden

VSJ Veddinge Bakker

VSJ Sydspids af Sejerø

VSJ Gravhøj ved Svebøll

VSJ Vestborgen

VSJ Stenhus i Kalundbor

VSJ Øer på søterritoriet

VSJ Vrangeskov

VSJ Fællesfolden

VSJ Svallerup Strand

VSJ Langesø Eng

VSJ Langdysse ved Vielst

VSJ Knud Lavards Kapel

VSJ Tadre Mølle

VSJ Øer på søterritoriet

VSJ Fugledegaard

VSJ Pedersted Skov

VSJ Stubberup Skov

VSJ Kattinge Søerne

VSJ Gershøj

VSJ Arealer ved Ejby

VSJ Arealer ved Gamme

VSJ Høng Skov

VSJ Benløse Skov

VSJ Skovrejsning Slagels

NSJ Brødemose skov

NSJ Avderød skov

NSJ Holstrupgård

NSJ Fuglsanggård

NSJ Kanalerne

NSJ Nordhuse

NSJ Arrenæs‐arealer

NSJ Statens tørvemose

NSJ Hyttegården

NSJ Hovgårds pynt

NSJ Holløse bredning

NSJ Alsønderup enge

NSJ Solbjerg enge

NSJ Lyngby mose

NSJ Ullerup skov

NSJ Sandflugtsplantagen

NSJ Hyllingbjerg

NSJ Stejlepladserne

NSJ Skansen

NSJ Sandflugtsmonumen

NSJ Helenekilde

NSJ Strandbjerggård

NSJ Vieholmgård

Løv

192

187

129

Areal (ha)

Nål Lysåbe

131

158

Side 10 af 11

Bilag G

%skov allered Antal arter fra hver zone gruppe Prioritet A arte%obs allerede

Total i z3 i z4 z2 z3 z4 z23 z24 z34 ikke‐skovTotal Alle Skov

i z3 i z4

291

231

126

213

17 20

205

122

4 10

10 16

175

10 17

5 11

8 16

101

5 21

256

Chapter 4

Skove altid i zone 1 (N = 681)

Enhed og skov

NSJ Rågegården

NSJ Gilbjerggård

NSJ Valby Hegn

NSJ Høbjerg hegn

NSJ Nejede vesterskov

NSJ Æbelholt klosterruin

NSJ Skævinge

NSJ Gørløse

NSJ Søborg Slotsruin

NSJ Klosterhus

NSJ Esrum Kanal

NSJ Parcel i Nødebo

NSJ Sørup Havn

NSJ Fredensborg Havn

NSJ Nødebo Holt

NSJ Gadevang Skov

NSJ Selskov

NSJ Mose i Gadevang

NSJ Tirsdagsskoven

NSJ Brøde Skov

NSJ Lille Hestehave

NSJ Snevret

NSJ Horneby Sand

NSJ Risby Vang

NSJ Krogenberg Hegn

NSJ Munkegårds Hegn

NSJ Endrup Hegn

NSJ Kelleris Hegn

NSJ Dageløkke Skov

NSJ Skipperholm

NSJ Kovang

NSJ Knorrenborg Vang

NSJ Grønholt Vang

NSJ Grønholt Hegn

NSJ Stasevang

OSJ Farum Sø

OSJ Nyvang

OSJ Skydebanearealerne

OSJ Ganløse Forte

OSJ Brudehøje

OSJ Kollekolle

OSJ Snubbekorsskoven

OSJ Karlstrup Skov

OSJ Øer i Køge Bugt

OSJ Regnemark

OSJ Ramsø

OSJ Greve Skov

OSJ Solhøj Fælled

OSJ Tune Skov

OSJ Kildebrønde Skov

OSJ Sønderskov

OSJ Lystrup Skov

OSJ Uggeløse skov

OSJ Krogenlund

OSJ Bastrup sø

OSJ Hammergård m.m.

OSJ Sperrestrup skov

OSJ Grønlien skov

OSJ Hørup Skov

OSJ Gulddysse Skov

OSJ Himmelev Skov

Løv

267

137

Areal (ha)

Nål Lysåbe

143

101

125

154

104

Side 11 af 11

Bilag G

143

154

164

183

138

124

%skov allered Antal arter fra hver zone gruppe Prioritet A arte%obs allerede

Total i z3 i z4 z2 z3 z4 z23 z24 z34 ikke‐skovTotal Alle Skov

i z3 i z4

1 9

20 36

360

1 6

3 16

151

107 49 48

4 13

219

129

49 96

1 100

160

4 14

179

37 23 76

67 19 24

251

2 1 2

1 11

10 59 19

109

236

8 12

1 16

273

1 4

1 2

125

6 10

59 16

5 12

104

208

8 15

26 28

111

113

44 17

4 12

199

162

6 14

6 1

8 19

8 10

116

121

140

Chapter 4

257

Bilag H

Bilag H. Resultater fra alle scenarier.

Scenarie

z1 %

areal

25,9

26,9

39,3

37,1

38,3

57,8

58,2

25,9

26,9

39,3

37,1

38,3

57,9

58,2

36,7

38,1

26,0

35,8

36,7

58,0

26,0

35,8

36,7

58,0

57,2

46,5

z2 %

areal

24,3

23,6

20,3

19,7

13,7

13,4

24,3

23,6

20,3

19,7

13,1

13,0

20,3

19,7

24,2

24,3

20,3

13,0

24,2

24,3

20,3

13,0

15,5

9,4

z3 %

areal

28,2

29,2

38,2

25,1

25,6

15,2

15,6

29,5

30,3

40,3

40,6

27,7

28,1

18,8

19,5

28,6

27,3

29,3

23,5

28,6

19,1

29,3

23,5

28,6

19,1

15,2

19,4

z4 %

areal

21,7

20,3

22,5

17,5

16,5

13,3

12,8

20,4

19,2

20,4

20,1

14,9

14,0

10,2

9,4

14,4

15,0

20,5

16,4

14,4

9,9

20,5

16,4

14,4

9,9

12,1

24,7

53,5

Tab %

årligt

61,3

61,4

68,7

53,0

53,6

36,9

37,1

53,9

53,5

58,4

45,6

45,9

32,6

32,4

46,1

46,5

50,3

42,7

45,5

30,5

42,9

36,3

38,1

25,5

33,1

48,1

58,9

z4 (ha)

z4 (n)

Mål nået

(af 304

arter)

217

215

199

229

232

257

217

215

199

229

232

261

262

231

230

221

255

231

258

221

255

231

258

166

A penge

B areal

C penge

D areal

E penge

F areal

G penge

H areal

I penge

J areal

K penge

L areal

M penge

N areal

O penge

P areal

Q penge

R areal

S penge

T penge

U penge

V penge

W penge

X penge

Y penge

Z penge

22.933

21.541

23.797

23.807

18.502

17.424

14.094

13.519

21.573

20.302

21.577

21.241

15.803

14.810

10.763

9.964

15.281

15.878

21.693

17.338

15.281

10.524

21.693

17.338

15.281

10.524

12.771

26.119

56.679

203

0,83

0,84

0,87

0,86

0,87

0,91

0,83

0,84

0,87

0,86

0,87

0,92

0,87

0,88

0,85

0,90

0,87

0,91

0,85

0,90

0,87

0,91

0,93

Linjer i fed

er scenarier med målet sat til 3 repræsentationer i stedet for 5. GM = gennemsnitlig

målopfyldelse for de prioritet A arter, som indgår i scenariet. z = zone. NA = ikke relevant. Alle

areal mål gælder skovbevokset areal, dvs. uden lysåbne områder som græsland, søer, veje m.v..

Se tabel 3 og 13 for hver scenaries forudsætninger.

258

Chapter 4

Side 1 af 1

Saproxyl?

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Saproxyl

Ej‐saproxyl

Gap arter i scenarie Q (N = 73)

Bilag I

Gruppe

Zone a

Skove (N) Match gap?

Åben skov

3 gap1

Åben saproxyl

0 gap1

Åben saproxyl

3 gap2

Åben saproxyl

0 gap2

Åben saproxyl

0 gap1

Åben saproxyl

3 gap2

Åben saproxyl

0 gap1

Åben skov

4 gap1

Åben saproxyl

0 gap1

Åben skov

1 gap1

saproxyl skovart 4

2 gap1

Åben saproxyl

4 gap1

saproxyl skovart 4

1 gap1

Åben skov

1 gap1

saproxyl skovart 4

2 gap2

Nål obligat

1 gap3

Åben skov

2 gap2

Åben skov

0 gap1

Åben skov

3 gap2

saproxyl skovart 4

4 gap1

Nål obligat

3 gap2

Åben skov

3 gap2

Åben skov

4 gap1

Åben skov

0 gap1

Nål fakultativ

3 gap1

Nål obligat

0 gap3

Åben skov

3 gap1

Åben skov

3 gap1

Åben skov

0 gap1

Åben skov

1 gap3

Åben skov

0 gap1

Åben skov

1 gap1

Nål obligat

1 gap2

Nål obligat

2 gap3

Nål fakultativ

3 gap1

Nål obligat

1 gap1

Åben skov

4 gap1

saproxyl skovart 4

3 gap1

saproxyl skovart 4

0 gap1

saproxyl skovart 4

4 gap1

Nål fakultativ

2 gap1

saproxyl skovart 4

1 gap1

saproxyl skovart 4

3 gap1

Nål obligat

1 gap2

Åben skov

0 gap1

Nål obligat

1 gap1

Nål obligat

3 gap1

Åben skov

2 gap2

saproxyl skovart 4

1 gap1

Nål fakultativ

3 gap2

Åben skov

0 gap1

Urørt løvskov

0 gap2

saproxyl skovart 4

4 gap1

Åben skov

4 gap1

Nål obligat

1 gap1

saproxyl skovart 4

2 gap3

saproxyl skovart 4

1 gap2

saproxyl skovart 4

1 gap1

Anden løvskov

1 gap1

Nål fakultativ

2 gap1

Åben skov

0 gap2

Åben skov

4 gap1

Åben skov

3 gap1

Nål fakultativ

0 gap1

Åben skov

0 gap1

Åben skov

1 gap1

Åben skov

2 gap1

Åben skov

4 gap1

Åben skov

1 gap1

Åben skov

2 gap2

Åben skov

3 gap1

Åben skov

3 gap1

Åben skov

0 gap2

Artsgruppe

Arts navn

Truet hvor? Rødliste TilbagegaPrioritetPræference

Pattedyr

Bredøret flagermus

EU anx 2 art

VU nej

Løv

Biller

Allecula morio

VU ja

Løv

Biller

Ampedus hjorti

Globalt

VU nej

Løv

Biller

Eghjort

RE RE

Løv

Biller

Grøn pragttorbist

CR ja

Løv

Biller

Matsort træsmælder

VU ja

Løv

Biller

Pragtsmælder

VU ja

Løv

Biller

Sort blomsterbuk

VU ja

Løv

Biller

Tachyta nana

VU ja

Løv

Tovinger

Hvidbåndet rovflue

CR ja

Løv

Tovinger

Lille træsvirreflue

VU ja

Løv

Tovinger

Mørk myresvirreflue

VU ja

Løv

Tovinger

Pragtsvirreflue

EN ja

Løv

Tovinger

Smuk løgsvirreflue

VU ja

Løv

Tovinger

Sort træsmuldsvirreflue

EN ja

Løv

Tovinger

Sort vedrovflue

EN ja

Nål obligat

Tovinger

Stikkelsbær‐glanssvirreflue DK

VU ja

Løv

Tovinger

Sump‐urtesvirreflue

VU ja

Løv

Tovinger

Tidlig ornamentsvirreflue

VU ja

Løv

Tovinger

Uldhåret pelssvirreflue

VU ja

Løv

Årevinger

Blank gæstemyre

Globalt

VU nej

Nål obligat

Dagsommerfug Kirsebærtakvinge

CR ja

Løv

Dagsommerfug Rødlig perlemorsommerfugl DK

CR ja

Løv

Sommerfugle Brachionycha nubeculosa

RE RE

Løv

Sommerfugle Dværgspinder

CR ja

Nål fakultativ

Sommerfugle Gran‐nonne

EN ja

Nål obligat

Sommerfugle Rødbrun ordenugle

RE RE

Løv

Sommerfugle Stribet målerugle

VU ja

Løv

Sommerfugle Trapez‐glansugle

RE RE

Løv

Græshopper Stor enggræshoppe

CR ja

Løv

Edderkopper Midia midas

EN ja

Løv

Edderkopper Orange hjulspinder

EN ja

Løv

Sæksvampe

Kæmpe‐stenmorkel

VU ja

Nål obligat

Basidiesvamp Ædelgran‐mælkehat

EN ja

Nål obligat

Basidiesvamp Blåfodet kødpigsvamp

CR ja

Nål fakultativ

Basidiesvamp Brandgul fagerhat

EN ja

Nål obligat

Basidiesvamp Brungul rødblad

VU ja

Løv

Basidiesvamp Brusk‐bævretop

VU ja

Løv

Basidiesvamp Cinnober‐muslingesvamp

EN ja

Løv

Basidiesvamp Duftende kæmpeskælhat

VU ja

Løv

Basidiesvamp Entoloma lampropus

EN ja

Nål fakultativ

Basidiesvamp Glat ildporesvamp

EN ja

Løv

Basidiesvamp Gylden grynskælhat

EN ja

Løv

Basidiesvamp Højstokket ridderhat

VU ja

Nål obligat

Basidiesvamp Hypholoma ericaeum

EN ja

Løv

Basidiesvamp Inocybe sambucina

EN ja

Nål obligat

Basidiesvamp Lak‐skørhat

VU ja

Nål obligat

Basidiesvamp Maj‐rødblad

VU ja

Løv

Basidiesvamp Mønster‐lædersvamp

CR ja

Løv

Basidiesvamp Orangegul ridderhat

EN ja

Nål fakultativ

Basidiesvamp Punktstokket vokshat

EN ja

Løv

Basidiesvamp Rosalilla rødblad

EN ja

Løv

Basidiesvamp Sortfodet stilkporesvamp

VU ja

Løv

Basidiesvamp Strågul køllesvamp

VU ja

Løv

Basidiesvamp Teglfarvet mælkehat

EN ja

Nål obligat

Basidiesvamp Tofarvet foldporesvamp

EN ja

Løv

Basidiesvamp Tomentella lateritia

VU ja

Løv

Basidiesvamp Vellugtende læderporesvamp DK

EN ja

Løv

Basidiesvamp Violet koralsvamp

EN ja

Løv

Lav

Brungrøn bægerlav

VU ja

Nål fakultativ

Lav

Elegant skållav

EN ja

Løv

Lav

Opblæst bægerlav

VU ja

Løv

Lav

Sprække‐punktlav

VU ja

Løv

Lav

Tørve‐bægerlav

CR ja

Nål fakultativ

Lav

Ved‐nålesvamp

CR ja

Løv

Karplanter

Blåtoppet kohvede

VU ja

Løv

Karplanter

Eng‐hejre

VU ja

Løv

Karplanter

Finsk røn

CR ja

Løv

Karplanter

Fruesko

EU anx 2 art

VU nej

Løv

Karplanter

Hvidplettet lungeurt

VU ja

Løv

Karplanter

Kantet kohvede

VU ja

Løv

Karplanter

Kost‐nellike

VU ja

Løv

Karplanter

Tørve‐viol

CR ja

Løv

Se nederst på Bilag B for forklaring på overskrifter.

Chapter 4

259

Resultat af manuel optimering ved zoneopdeling af skove (N = 48).

Bilag J

Ha z3 Ha z4 Zone Zone for Zoner

Areal i hektar

løv nål åbent i alt allerede allerede Mx_Q gap‐art manueltKommentar om især zone 4

Enhed og skov

HIM Nørreskov

181 227

64 472

430 Kun z4 i NV og ikke hidtidig græsningsskov

HIM Skindbjerglund

43 z3 ok. Urørt‐arterne har allerede urørt.

HIM Fællesskov

187 307

98 592

191

32 Nat.strat. uændret er fint for arterne

VJY Hoverdal Plantage

115 1039 408 1562

VJY Fejsø Plantage

200 658 731 1588

SHL Nordskov

191 607 188 986

171

110

42 z24 kombi (evt delvis)

SHL Odderholm

432 Split aht to gap arter. Z4 kun for skovdel.

SHL Lysbro Skov

67 149

430 Nat.strat. uændret er fint for arterne

SHL Vesterskov

184 375

94 653

187

175

23 4320 Nat.strat. uændret er fint for arterne

VSY Læsø Plantage

375 786 714 1875

162

4 4320 z24 behøves delvis, lysninger holdes åbne

THY Vilsbøl Plantage

143 473 603 1218

THY Hanstholm Kystskrænt

5 245 252

3 z3 OK. Gap‐Rødbladen vokser også i ore.

THY Svinkløv Plantage

132 332 253 717

432 Split z24 undt. z3 i NV

KJY Indskovene

276

88 461 825

106

32 Nat.strat. uændret er fint for arterne

KJY Markerne

0 388 390

MJY Palsgård Skov

149 720 235 1104

1 366 565

259

43 Nat.strat. uændret er fint for arterne

VAD Draved Skov og Kongens 198

FYN Kasmose Skov

43 Nat.strat. uændret er fint for arterne

FYN Sydlangeland

106

7 333 447

3 z3 OK. Gap‐arten havde fejl i præf.

TRE Stagsrode Skov

176

12 218

432 OK z4 inkl urørt nål. Kan evt reduceres

TRE Svanemosen

88 124

43 Nat.strat. uændret er fint for arterne

250

2 286 538

208

128

430 Fasthold græsn.skov. Udelad randskove.

STS Klinteskov

STS Ulvshale Skov

13 308 410

43 ok z4 for skovdele, men allerede urørt

STS Kongskilde

2 285 324

3 z3 i stedet for z4 efter rettet økol behov

BON Almindingen

916 1097 406 2420

24 4320 Delvis z4 (inkl nål) + z3 strøg

BON Ølene

32 Allerede urørt mht skov. Bevar nål delvis.

BON Hammerknuden

5 170 197

100

320 Bevar noget nål i området

BON Rø Plantage

213 320

60 593

320 Bevar noget nål i området

HST Jægersborg Hegn

426

96 554

430 Delvis z4 og 3 + græsning. z2 art var fejl.

145

9 193

432 Evt z4 hele skoven + nål essentiel.

HST Geel Skov

HST Jægersborg Dyrehave

435

8 452 895

776

43 z3 OK efter rettede øko behov +lidt urørt.

HST Ordrup Krat

4 z4 ok eft rettet øko behov, split ej.

HST Kongelunden

208

74 331

32 OK t z23 nærmest normal drift

HST Vestamager

236

0 1730 1966

132

430 Split evt. al skov t z4. Rest lysåbent.

HST Rude Skov og Friheden 321 120 130 571

107

34 4320 z24 inkl nål, ekskl lysåbne.

VSJ Røsnæs

8 166 196

320 Nål og lysåbent fokus.

VSJ Bidstrup Skovene

646 195 181 1021

430 Split t ca 100 ha z4 + z3 bryn fokus

168

29 224

43 Split i z3 + z4, fx 50/50

VSJ Boserup Skov

NSJ Tisvilde hegn

338 1280 375 1992

128

432 z4 Hegnet, z2 plantagerne, z3 melby ore

NSJ Gribskov

1943 1180 722 3845

411

303

432 z24 urørt inkl. urørt nål, multizone behov

NSJ Søskoven

498 188 100 785

288

30 Dele z3 ok eft rettet øko behov. Ej z4 behov.

NSJ Hornbæk Plantage

105

22 198

32 z 23 fyrreskovs fokus

OSJ Ryget

115

14 149

4 ok t z4 eft rettet øko behov for tørveviol

OSJ Nørreskoven

169

13 194

4 ok z4 efter rettet øko behov. Kan reduceres

OSJ Ganløse Ore

216 150

30 396

42 OK t z4. Nål vigtig. Kan reduceres

OSJ Store Hareskov

181

45 295

43 OK t z4 hele skoven. Kan reduceres.

OSJ Vestskoven

556 183 604 1342

32 Normal drift, ej z4 eft rettet øko behov.

OSJ Ravnsholt

150 123

40 312

32 z3 inkl nål dødtved efter rettet øko behov.

260

Chapter 4

Zoneforskel mellem scen. Q, Q1 og Q2 (N = 146)

Enhed og skov

HIM Nørreskov

HIM Skindbjerglund

HIM Navn Sø

HIM Fællesskov

VJY Klosterheden Plantage

VJY Arealer ved Ferring og Trans Kirke

VJY Harboøre Tange

VJY Hoverdal Plantage

VJY Fejsø Plantage

VJY Husby Plantage

VJY Arealer i Ringkøbing kommune

VJY Fjand Enge

VJY Vest Stadil Fjord

VJY Sandfær

VJY Tihøje Hede

SHL Østerskoven, østlige del

SHL Nordskov

SHL Bjørnholt

SHL Trustrup Høje

SHL Høgdal

SHL Kallehave

SHL Klostermølle mm.

SHL Ny Vissingkloster

SHL Kobskov Vest

VSY Læsø Plantage

VSY Skagen Plantage

VSY Bunken Plantage

VSY Ålbæk Plantage

VSY Uggerby Plantage

VSY Tornby Plantage

VSY Tranum Plantage

VSY Hune Plantage

VSY Grishøjgårds Krat

THY Lyngby Hede

THY Ejerslev Vang

THY Nystrup Plantage, østlige del

THY Nystrup Plantage, vestlige del

THY Vilsbøl Plantage

THY Hanstholm Vildtreservat

THY Hanstholm Kystskrænt

THY Hjardemål Plantage

THY Madsbøl Plantage

THY Lild Plantage, vestlige del

THY Vester Thorup Plantage

THY Svinkløv Plantage

THY Langdal Plantage

KJY Indskovene

KJY Ajstrup Strand

KJY Mols Bjerge

KJY Helgenæs Syd

KJY Dragsmur

KJY Ørnbjerg Mølle

KJY Gjerrild Strand

KJY Viborg Plantage

KJY Stanghede

KJY Inderø Skov

KJY Kalø Hovedgård

KJY Markerne

MJY Rydhave Skov

MJY Mønsted Kalkgruber

MJY Kompedal Plantage

MJY Nørlund Plantage

MJY Bøllingsø

MJY Palsgård Skov

MJY Gludsted

VAD Hønning Plantage

VAD Soldaterskoven

VAD Klaskeroj Skov

VAD Stursbøl Hegn

VAD Kirkeby Plantage

VAD Rømø Strand

VAD Tingdal Plantage

FYN Topgården

FYN Frøbjerg Bavnehøj

FYN Thurø Rev

Løv

181

187

847

Areal (ha)

Nål Lysåben

227

100

307

4462

1090

1114

1039

408

658

731

710

256

253

1108

529

210

138

149

607

188

786

714

698

732

484

690

525

132

427

276

280

237

1408

2243

161

1196

833

311

234

612

473

603

3208

245

707

388

325

436

631

584

1059

328

332

253

122

461

715

174

275

198

388

2062

378

1435

575

720

235

2597

689

256

148

244

166

235

4171

Total

472

100

592

6399

1114

1562

1588

1066

257

1108

569

217

379

986

1875

1474

1221

736

786

560

3922

165

1270

1239

863

1218

3288

252

1156

773

1287

1504

717

176

825

836

209

437

249

390

2683

2982

630

1104

3467

524

173

118

465

406

4173

Side 1 af 2

Bilag K

Zone valgt i scenarie

115

200

100

191

375

272

143

117

132

276

132

243

112

149

181

120

145

Chapter 4

261

Side 2 af 2

Bilag K

Total

447

754

691

599

2003

1254

804

491

712

232

124

198

335

185

538

136

204

2420

197

207

162

593

1966

182

571

287

170

196

1021

224

1992

313

785

1734

198

544

119

616

442

149

194

932

396

240

312

224

167

Zone valgt i scenarie

Zoneforskel mellem scen. Q, Q1 og Q2 (N = 146)

Enhed og skov

FYN Lille Rise

FYN Ristinge

FYN Sydlangeland

BLH Kærgård Plantage, nordlige del

BLH Vejers Plantage, sydlige del

BLH Ål Plantage

BLH Skallingen

BLH Fanø Plantage

TRE Frederikshåb Plantage

TRE Gyttegård Plantage

TRE Skærsø

TRE Randbøl Hede

TRE Skovhave M.M.

TRE Kærskov

TRE Tapsøre Statsskov

TRE Svanemosen

SDJ Dybbøl

SDJ Hønsnap Skov

SDJ Frøslev Mose

SDJ Areal ved Assenholm

SDJ Østerskov

STS Klinteskov

STS Dybsø

BON Blykobbe Plantage

BON Almindingen

BON Ejendommen Lassen

BON Ølene

BON Hammerknuden

BON Hammersholm og Hammershus

BON Slotslyngen

BON Del af Ringebakkerne

BON Borrelyngen

BON Rø Plantage

HST Enrum Skov

HST Charlottenlund Skov

HST Vestamager

HST Kalvebod Fælled

HST Rude Skov og Friheden

VSJ Korshage

VSJ Sonnerup Skov

VSJ Kårup Skov

VSJ Røsnæs

VSJ Diesbjerg

VSJ Bidstrup Skovene

VSJ Boserup Skov

NSJ Arresødal skov

NSJ Sonnerup Skov

NSJ Lyngby skov

NSJ Ll. lyngby mose

NSJ Tisvilde hegn

NSJ Gilbjerggård

NSJ Stenholt Vang

NSJ Søskoven

NSJ Esrum Sø

NSJ Nakkehoved

NSJ Hornbæk Plantage

NSJ Teglstrup Hegn

NSJ Egebæksvang Skov

NSJ Horserød Hegn

NSJ Gurre Vang

NSJ Fredensborg Skovene

OSJ Ryget

OSJ Nørreskoven

OSJ Furesø

OSJ Terkelskov

OSJ Ganløse Ore

OSJ Jonstrup Vang

OSJ Ravnsholt

OSJ Krogenlund

OSJ Slagslunde skov

OSJ Ganløse Eged m.m.

Løv

106

148

145

250

916

213

236

321

646

168

338

198

498

105

306

381

146

115

169

216

174

150

188

129

Areal (ha)

Nål Lysåben

333

398

285

387

284

377

127

2003

620

580

504

220

205

252

710

198

327

286

136

1097

406

170

169

320

1730

153

120

130

173

166

195

181

1280

375

188

100

1734

138

100

166

258

932

150

123

262

Chapter 4

Side 1 af 2

Gap‐arter i scen. Q eller Q1 (N = 155).

Sorteret efter truet hvor og Gap.

Artsgruppe

Årevinger

Biller

Karplanter

Padder

Snegle

Karplanter

Pattedyr

Karplanter

Lav‐Sæksvamp

Sommerfugle

Årevinger

Basidiesvampe

Græshopper

Tovinger

Basidiesvampe

Karplanter

Fugle

Lav‐Sæksvamp

Edderkopper

Sæksvampe

Dagsommerfugle

Basidiesvampe

Karplanter

Basidiesvampe

Biller

Sommerfugle

Biller

Sommerfugle

Lav‐Sæksvamp

Karplanter

Sommerfugle

Basidiesvampe

Tovinger

Biller

Tovinger

Lav‐Sæksvamp

Karplanter

Basidiesvampe

Sommerfugle

Lav‐Sæksvamp

Edderkopper

Basidiesvampe

Sommerfugle

Biller

Dagsommerfugle

Biller

Lav‐Sæksvamp

Basidiesvampe

Biller

Karplanter

Basidiesvampe

Artsnavn

Blank gæstemyre

Ampedus hjorti

Cypres‐ulvefod

Løvfrø

Skæv vindelsnegl

Flad ulvefod

Bredøret flagermus

Fruesko

Opblæst bægerlav

Treforkugle

Enghumle

Tofarvet foldporesvamp

Stor enggræshoppe

Sort vedrovflue

Strågul køllesvamp

Finsk røn

Nøddekrige

Skælklædt bægerlav

Marmoreret hjulspinder

Kæmpe‐stenmorkel

Kirsebærtakvinge

Brungul rødblad

Brusk‐bævretop

Sortfodet stilkporesvamp

Kost‐nellike

Gulgrå køllesvamp

Bestøvlet tragthat

Kliddet parasolhat

Hyphoderma medioburiense

Løvegul skærmhat

Rødmende alfehat

Glat løber

Uldhale

Azurbille

Brunrods‐hætteugle

Kruset skjoldlav

Lav rapgræs

Gran‐nonne

Rosalilla rødblad

Tomentella lateritia

Orangegul ridderhat

Stikkelsbær‐glanssvirreflue

Eghjort

Tidlig ornamentsvirreflue

Elegant skållav

Tørve‐viol

Blåfodet kødpigsvamp

Dværgspinder

Sprække‐punktlav

Brungrøn bægerlav

Orange hjulspinder

Bleg rørhat

Hasselporesvamp

Tykbladet rødblad

Firfliget stjernebold

Spatel‐filthat

Grønskællet parasolhat

Rod‐gråblad

Orangebrun troldhat

Violblå fagerhat

Bævrekølle

Grå landmand

Bøgeløber

Enghvidvinge

Magdalis armigera

Elmeloppe

Pudret bægerlav

Kvist‐kantskivelav

Gulgrøn kantskivelav

Bark‐blegskivelav

Grønpudret bogstavlav

Ædelgran‐mælkehat

Maj‐rødblad

Højstokket ridderhat

Matsort træsmælder

Hvidplettet lungeurt

Punktstokket vokshat

Cinnober‐muslingesvamp

Truet hvor Zone Gap_Q

Globalt

2 gap2

Globalt

3 gap2

3 gap1

4 gap3

3 gap3

2 gap3

3 gap1

2 gap2

3 gap2

3 gap1

4 gap1

3 gap1

2 gap3

4 gap2

23 gap2

3 gap2

23 gap1

3 gap1

23 gap1

3 gap1

2 gap3

3 gap2

2 gap2

3 gap2

3 gap1

4 gap1

Bilag L

Videnskabeligt navn

Formicoxenus nitidulus

Ampedus hjorti

Diphasiastrum tristachyum

Hyla arborea

Vertigo angustior

Diphasiastrum complanatum

Barbastella barbastellus

Cypripedium calceolus

Cladonia sulphurina

Acronicta tridens

Bombus veteranus

Gloeoporus dichrous

Chorthippus dorsatus

Laphria ephippium

Clavaria flavipes

Sorbus hybrida

Nucifraga caryocatactes

Cladonia squamosa

Araneus marmoreus

Gyromitra gigas

Nymphalis polychloros

Entoloma formosum

Exidia cartilaginea

Polyporus melanopus

Dianthus armeria

Clavulinopsis cinereoides

Clitocybe alexandri

Cystolepiota hetieri

Hyphoderma medioburiense

Pluteus leoninus

Porpoloma metapodium

Carabus glabratus

Eriogaster lanestris

Pytho depressus

Shargacucullia scrophulariae

Peltigera praetextata

Poa supina

Calliteara abietis

Entoloma queletii

Tomentella lateritia

Tricholoma aurantium

Epistrophe grossulariae

Lucanus cervus

Xanthogramma festivum

Melanohalea elegantula

Viola epipsila

Sarcodon scabrosus

Nudaria mundana

Anisomeridium polypori

Cladonia novochlorophaea

Araneus alsine

Boletus impolitus

Dichomitus campestris

Entoloma clandestinum

Geastrum quadrifidum

Hohenbuehelia auriscalpium

Lepiota grangei

Lyophyllum leucophaeatum

Rhodocybe nitellina

Rugosomyces ionides

Tremellodendropsis tuberosa

Acronicta cinerea

Carabus intricatus

Leptidea juvernica

Magdalis armigera

Rhynchaenus rufus

Cladonia cenotea

Lecanora symmicta

Lecanora varia

Ochrolechia androgyna

Opegrapha herbarum

Lactarius albocarneus

Entoloma aprile

Tricholoma inamoenum

Crepidophorus mutilatus

Pulmonaria officinalis

Camarophyllopsis atropuncta

Crepidotus cinnabarinus

Gap_Q1

gap5

gap3

gap4

gap1

gap5

gap4

gap3

gap2

gap1

Økologiske behov

Myretuer af rød skovmyre

Gl træer med dødt ved, især eg

Råjord i m. lys skov / hede; brand.

Rent klart ynglevand fri for fisk

Lysninger i skov, kalkrig jordbund

Råjord i m. lys skov / hede; brand.

Insektrig skov‐lysåben mix, huler

Kalkrig / lerbund

Ren fugtig luft.

Lysåbent, næringsfattigt.

Dødt ved i løvskov

Fugtig eng, kontinentalt klima

Løv saproxyl og kan lide solskin på død bøg

Kalkrig / lerbund

Lys skov eller skovbryn

Fyr og hassel i nåledomineret skov

Ren fugtig luft.

Sumpskov, vand, fattigkær

På nåletræsved i jord

Solåbent blandet løvskovbryn

Sumpskov, overdrev

Dødt ved i løvskov

Kalkrig / lerbund m. jordliggende dødt løvtræved

Lysåbent, næringsfattigt.

Kalkrig / lerbund

Kalkrig / lerbund med nåletræ

Urørt skovjordbund i løvskov

Dødt ved i gl løvskov

Sumpskov med dødt ved af løvtræ

Lysåben næringsfattig kalk‐ / lerbund

Mosdækket bund i nål/løvskov+hede

Gl krat på overdrev

Gl nåleskov med dødt ved

Skovbryn

Ren fugtig luft.

Lyst, trampet, fx grusplads, græssti.

Nåleskov med gran

Kalkrig overdrev og sumpskov

Kalkrig / lerbund m. liggende dødt løvtræved

Kalkrig / lerbund

Skovbryn

Gl træer med dødt ved; Lys

Overgangszone ore, buske, bryn

Ren fugtig luft.

Meget våd mose m krat

Næringsfattig skovbund, især gl nål.

Lysåbent, næringsfattigt.

På bark af løvtræer i fugtigt miljø og ren luft

Ren fugtig luft.

Sumpskov, vand, fattigkær

Uforstyrret skovbund

Levende veterantræer

Lysåbent, næringsfattigt.

Kalkrig / lerbund

Dødt ved i gl løvskov

Urørt skovjordbund i løvskov

Næringsfattig skovbund, især nål.

Klit, skov

Kalkrig / lerbund

Kalkrig / lerbund med gl krat

Lysåbent, næringsfattigt.

Gammel løvskov

Eng‐skov‐mix med gul fladbælg

Store levende skovelme

Levende elmetræer

Ren fugtig luft.

Gl ædelgran på kalk/lerbund

Levende elmetræer

Gl nåleskov på kalk/lerbund

Nataktiv, gl hule løvtræer inkl bøg

Kalkrig / lerbund

Gl krat på overdrev

Dødt ved i løvskov

Chapter 4

263

Gap‐arter i scen. Q eller Q1 (N = 155).

Sorteret efter truet hvor og Gap.

Artsgruppe

Basidiesvampe

Biller

Sommerfugle

Tovinger

Sommerfugle

Tovinger

Biller

Sommerfugle

Biller

Lav‐Sæksvamp

Edderkopper

Karplanter

Basidiesvampe

Sommerfugle

Biller

Sommerfugle

Dagsommerfugle

Biller

Tovinger

Biller

Lav‐Sæksvamp

Edderkopper

Tovinger

Basidiesvampe

Dagsommerfugle

Tovinger

Biller

Sommerfugle

Tovinger

Karplanter

Artsnavn

Entoloma lampropus

Hypholoma ericaeum

Inocybe sambucina

Glat ildporesvamp

Brandgul fagerhat

Lak‐skørhat

Vellugtende læderporesvamp

Mønster‐lædersvamp

Allecula morio

Brachionycha nubeculosa

Sump‐urtesvirreflue

Rødbrun ordenugle

Hvidbåndet rovflue

Grøn pragttorbist

Stribet målerugle

Pragtsmælder

Tachyta nana

Tørve‐bægerlav

Ved‐nålesvamp

Midia midas

Eng‐hejre

Skællet fåreporesvamp

Hvidlig skiveskorpe

Athelidium aurantiacum

Brungul vokshat

Skønfodet slørhat

Safrankødet slørhat

Pighud

Skæv rødblad

Tæge‐rødblad

Kulkantarel

Grønfodet trævlhat

Stor grenkølle

Thallus‐navlehat

Sej fedtporesvamp

Steccherinum subcrinale

Musegrå posesvamp

Skinnende jordfarveugle

Tofarvet hedeløber

Højmose‐tiggerugle

Pragt grønsagsugle

Rustplettet ugle

Skovhvidvinge

Egeværftbille

Smaragdina salicina

Temnostoma meridionale

Falsk skjoldbille

Skov‐punktlav

Voksgul orangelav

Gulgrøn bægerlav

Gul trådkantlav

Filtrandet kantskivelav

Turners blegskivelav

Oliven‐bogstavlav

Ru prikvortelav

Tynd prikvortelav

Rendet grenlav

Gele‐skivelav

Almindelig skæglav

Skæglav slægten

Busket skæglav

Stor pukkelhjulspinder

Sort træsmuldsvirreflue

Gylden grynskælhat

Duftende kæmpeskælhat

Teglfarvet mælkehat

Violet koralsvamp

Rødlig perlemorsommerfugl

Pragtsvirreflue

Uldhåret pelssvirreflue

Smuk løgsvirreflue

Mørk myresvirreflue

Sort blomsterbuk

Trapez‐glansugle

Lille træsvirreflue

Kantet kohvede

Blåtoppet kohvede

Truet hvor Zone Gap_Q

23 gap1

3 gap1

2 gap1

4 gap1

2 gap1

4 gap1

3 gap1

23 gap1

3 gap1

4 gap2

4 gap1

2 gap1

34 gap1

3 gap1

4 gap1

3 gap1

34 gap1

3 gap1

4 gap1

3 gap1

Side 2 af 2

Bilag L

Videnskabeligt navn

Entoloma lampropus

Hypholoma ericaeum

Inocybe sambucina

Phellinus laevigatus

Rugosomyces chrysenteron

Russula rhodopus

Trametes suaveolens

Xylobolus frustulatus

Allecula morio

Brachionycha nubeculosa

Cheilosia frontalis

Cosmia affinis

Cyrtopogon lateralis

Gnorimus nobilis

Herminia tarsicrinalis

Ischnodes sanguinicollis

Tachyta nana

Cladonia incrassata

Mycocalicium subtile

Midia midas

Bromus racemosus

Albatrellus pes‐caprae

Aleurodiscus disciformis

Athelidium aurantiacum

Camarophyllopsis micacea

Cortinarius colus

Cortinarius traganus

Dentipellis fragilis

Entoloma depluens

Entoloma scabrosum

Faerberia carbonaria

Inocybe calamistrata

Lentaria epichnoa

Lichenomphalia hudsoniana

Spongipellis fissilis

Steccherinum subcrinale

Volvariella murinella

Agrochola nitida

Cymindis vaporariorum

Diarsia dahlii

Lacanobia splendens

Lasionhada proxima

Leptidea sinapis

Lymexylon navale

Smaragdina salicina

Temnostoma meridionale

Thymalus limbatus

Anisomeridium biforme

Caloplaca cerina

Cladonia carneola

Haematomma ochroleucum

Lecanora intumescens

Ochrolechia turneri

Opegrapha vulgata

Pertusaria hemisphaerica

Pertusaria leioplaca

Ramalina calicaris

Trapeliopsis gelatinosa

Usnea filipendula

Usnea genus

Usnea subfloridana

Araneus angulatus

Chalcosyrphus valgus

Flammulaster limulatus

Hemipholiota heteroclita

Lactarius hysginus

Ramaria fennica

Boloria euphrosyne

Caliprobola speciosa

Criorhina floccosa

Eumerus ornatus

Microdon analis

Stictoleptura scutellata

Xestia ditrapezium

Xylota abiens

Melampyrum cristatum

Melampyrum nemorosum

Gap_Q1

gap1

Økologiske behov

Lysåbent, næringsfattigt.

Uforstyrret morbund i nåleskov

Dødt ved i løvskov

Kalkrig / lerbund med krat og nåletræ

Uforstyrret morbund i nåleskov

Sumpskov med dødt ved af løvtræ

Dødt ved på gl egetræer

Gl træer med dødt ved; Lys

Sumpskov, birkeskov

Sumpskov, kildevæld, mose

Lysåbent, næringsfattigt.

Gl træer med dødt ved; Lys

Skovbryn

Gl træer med dødt ved; Lys

Ren fugtig luft.

Epixylisk på død rødgran

Stabile forhold i gl skov / park

Lysåbent, næringsfattigt.

Gl. løvskov med morbund

Levende veterantræer

Dødt ved i løvskov

Gl krat på overdrev

Næringsfattig nåleskov

Dødt ved i løvskov

Sumpskov, overdrev

Gl. brandpletter

Løvskov med gl træer

Dødt ved i gl løvskov

Lysåbent, næringsfattigt.

Gl træer med dødt ved

Dødt ved i løvskov

Kalkrig / lerbund

Gl skov / bryn

Lysåbent, næringsfattigt.

Sumpskov, højmose

Sumpskov

Lysåbent, næringsfattigt.

Blomsterrig eng‐skov‐mix, fladbælg

Solåbent dødt ved på stående eg

Lysåbent skovbryn

Dødt ved i gl løvskov

Under bark og i dødt nål/løv‐ved + træsvampe

Ren fugtig luft.

Grene af løvtræer i fugtig ren luft

Ren fugtig luft.

Fugtig ren luft og rigbarks‐træer

Ren fugtig luft.

Næringsfattig gl nåleskov

Dødt ved i gl løvskov

Dødt ved i løvskov

Sumpskov med dødt ved af løvtræ

Næringsfattig gl nåleskov

Gl løvskov på kalk/lerbund

Blomsterrigt eng‐skov‐mix med violer

Dødt ved i gl løvskov

Gl løvtræer m dødt ved

Skovbryn

Gl træer ved højmoser

Saproxyl i solåbne bøge

Lysåbent, næringsfattigt.

Dødt ved i gl løvskov

Lysåbent, næringsfattigt.

Kalkrig / lerbund m. slæt / heste

Bilag M

264

Chapter 4

Enhed og skov

314_110 Vilsted Sø

314_203 Halkær Mølle

314_207 Drastrup Skov

314_210 Sct Nicolai Bjerg v. Sebbers.

314_308 Als Havbakker

334_101 Klosterheden Plantage

334_1101 Stråsø Plantage

334_114 Arealer v. Ferring og Trans K

334_118 Harboøre Tange

334_1201 Hoverdal Plantage

334_1205 Troldhede Brunkulsleje

334_1301 Fejsø Plantage

334_1304 Lystbæk

334_1401 Husby Plantage

334_1402 Arealer i Ringkøbing komm.

334_1403 Arealer i Holmsland komm.

334_1405 Vest Stadil Fjord

Truet

Globalt

Art

Blomster‐stængelugle

Olivenbrun farvetunge

Citronbjørn

Okkergul pletvinge

Jensens vokshat

Gråblå rødblad

Olivenbrun farvetunge

Trichoglossum walteri (en svamp)

Trævlet vokshat

Markperlemorsommerfugl

Birkemus

Engblåfugl

Markperlemorsommerfugl

Spids bægerlav

Spættet bredpande

Syl‐firling

Spættet bægerlav

Gråbrun vokshat

Dyndløber

Karmindompap

Stor kobbersneppe

Ensianblåfugl

Entoloma elodes (en art svamp)

Gulstokket rødblad

Kløver‐køllesværmer

Kortsporet blærerod

Lille blåpil

Stor tornskade

Trehornet skarnbasse

Uld‐tuekogleaks

Sen damsvirreflue

Ensianblåfugl

Kæruld‐græsugle

Klithede‐maskesvirreflue

Småskællet rødblad

Karmindompap

Klit‐champignon

Brushane

Strandtudse

Latinsk navn

Eremobia ochroleuca

Microglossum olivaceum

Setina irrorella

Melitaea cinxia

Hygrocybe ingrata

Entoloma griseocyaneum

Microglossum olivaceum

Trichoglossum walteri

Hygrocybe intermedia

Argynnis aglaja

Sicista betulina

Cyaniris semiargus

Argynnis aglaja

Cladonia subulata

Pyrgus malvae

Sagina subulata

Cladonia rangiformis

Hygrocybe fornicata

Carabus clathratus

Carpodacus erythrinus

Limosa limosa

Maculinea alcon

Entoloma elodes

Entoloma xanthochroum

Zygaena trifolii

Utricularia ochroleuca

Orthetrum coerulescens

Lanius excubitor

Typhaeus typhoeus

Trichophorum alpinum

Anasimyia lunulata

Maculinea alcon

Amphipoea lucens

Paragus finitimus

Entoloma hispidulum

Carpodacus erythrinus

Agaricus devoniensis

Philomachus pugnax

Epidalea calamita

Biotop

Åbent

Ore

Åbent

Ore

Åbent

Eng

Åbent

Hede

Åbent

Kyst

Hede

Ore

Eng

Åbent

Eng

Åbent

Mose

Ore

Eng

Vand

Hede

Ore

Mose

Åbent

Mose

Hede

Ore

Åbent

Klit

Eng

Åbent

Økologiske behov

Lysåbent, næringsfattigt.

Varieret blandet vegetation

Høslætagtig drift, blomsterrigt.

Lysåbent, næringsfattigt.

Våd lavtvoksende ret fersk eng

Lysåbent med buske og småtræer.

Våd lavtvoksende ret fersk eng

Klokkeensian til æglægning

Lysåbent, næringsfattigt, vådt.

Lysåbent, næringsfattigt.

Høslætagtig drift, blomsterrigt.

Lysåbent, næringsfattigt.

Rent vand til ynglen.

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, græsning.

Lysåbent, næringsfattigt, vådt.

Klokkeensian til æglægning

Lysåbent, næringsfattigt, vådt.

Lysåbent, næringsfattigt.

Lysåbent med buske og småtræer.

Lysåbent, næringsfattigt.

Våd lavtvoksende ret fersk eng

Lavt, klart ynglevand fri for fisk.

Trusler

Tilgroning og eutrofiering

Tilgroning og eutrofiering + klima

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

Tilgroning og fragmentering

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

ukendt

Mangler slæt / græsning; Predation.

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

ukendt

Tilgroning og lign

Tilgroning og eutrofiering

Tilgroning, eutrofier., fragmentering

Tab af vådområder

Tilgroning og eutrofiering

ukendt

Ophør af græsning/høslet

Mangler slæt / græsning; Predation.

For få gode vandhuller

Bilag M, side 1 af 10

Bilag M

Chapter 4

265

Enhed og skov

334_1405 Vest Stadil Fjord

340_103 Nordskov

340_104 Bjørnholt

340_106 Ajstrup Strand + Norsminde

340_212 Trustrup Høje

340_214 Arealer på Samsø

340_305 Høgdal

340_314 Dalgård

340_317 Ny Vissingkloster

340_401 Lysbro Skov

350_11 Læsø Plantage

350_21 Skagen Plantage

Truet

Globalt

Art

Sort fløjlsløber

Nordlig jordtunge

Sortskællet vokshat

Entoloma cocles (en art svamp)

Gråblå rødblad

Gulfodet vokshat

Hedemøgbille

Mel‐rødblad

Mørkstribet vokshat

Nordlig jordtunge

Papil‐vokshat

Sortskællet vokshat

Vinter‐stilkbovist

Grøngul vokshat

Markfirben

Entoloma caesiocinctum (svamp)

Entoloma cocles (en art svamp)

Gråviolet vokshat

Gulfodet vokshat

Mørkøjet rødblad

Nordlig jordtunge

Purpur‐køllesvamp

Skønfodet rødblad

Sommer‐rødblad

Trichoglossum walteri (en svamp)

Stor gødningsrovflue

Violetrandet ildfugl

Femplettet køllesværmer

Smalrandet humlebisværmer

Gråbrun vokshat

Hede‐takspinder

Argusblåfugl

Hede‐takspinder

Markfirben

Markpiber

Brun skjoldlav

Citronbjørn

Gråbåndet bredpande

Kappeugle

Latinsk navn

Chlaenius tristis

Geoglossum starbaeckii

Hygrocybe turunda

Entoloma cocles

Entoloma griseocyaneum

Hygrocybe flavipes

Aphodius coenosus

Entoloma prunuloides

Hygrocybe radiata

Geoglossum starbaeckii

Hygrocybe subpapillata

Hygrocybe turunda

Tulostoma brumale

Hygrocybe citrinovirens

Lacerta agilis

Entoloma caesiocinctum

Entoloma cocles

Hygrocybe lacma

Hygrocybe flavipes

Entoloma lividocyanulum

Geoglossum starbaeckii

Clavaria zollingeri

Entoloma corvinum

Entoloma solstitiale

Trichoglossum walteri

Asilus crabroniformis

Lycaena hippothoe

Zygaena lonicerae

Hemaris tityus

Hygrocybe fornicata

Phyllodesma ilicifolia

Plebejus argus

Phyllodesma ilicifolia

Lacerta agilis

Anthus campestris

Peltigera rufescens

Setina irrorella

Erynnis tages

Calophasia lunula

Biotop

Mose

Ore

Åbent

Ore

Åbent

Ore

Hede

Åbent

Hede

Åbent

Klit

Hede

Åbent

Klit

Økologiske behov

Lysåbent, næringsfattigt, vådt.

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, græsning.

Lysåbent, næringsfattigt.

Solåben sandjord til æglægning

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, græsning.

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, lyng.

Lysåbent, næringsfattigt.

Solåben sandjord til æglægning

Lysåbent, næringsfattigt.

Trusler

ukendt

Tilgroning og eutrofiering

Tilgroning og lign

Tilgroning og eutrofiering

Ophør af græsning/høslet

Tilgroning og eutrofiering

Tilgroning og lign

Tilgroning og eutrofiering

Tilgroning, eutrofier., fragmentering

Tilgroning og lign

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

Tilgroning og lign

Forstyrrelser, tilgron., eutrof., klima

Tilgroning og eutrofiering

Tilgroning og eutrofiering + klima

Tilgroning, eutrofier., fragmentering

ukendt

Bilag M, side 2 af 10

Bilag M

266

Chapter 4

Enhed og skov

350_21 Skagen Plantage

350_22 Bunken Plantage

350_23 Råbjerg Plantage

350_31 Ålbæk Plantage

350_32 Tversted Plantage

350_36 Råbjerg Mose

350_42 Uggerby Plantage

350_43 Lilleheden Plantage

350_45 Tornby Plantage

350_53 Rubjerg Plantage

Truet

Globalt

Art

Ruskællet rødblad

Sne‐kruslav

Svalehale

Hede‐takspinder

Markpiber

Strandtudse

Almindelig skæglav

Blomster‐stængelugle

Kappeugle

Kyst‐bredfodsflue

Hedepletvinge

Markfirben

Lav bægerlav

Sortfodet bægerlav

Hedepletvinge

Hede‐takspinder

Blomster‐stængelugle

Citronbjørn

Gråbåndet bredpande

Klithede‐maskesvirreflue

Lyngpenselspinder

Svalehale

Violetrandet ildfugl

Hede‐takspinder

Karmindompap

Krum star

Lav bægerlav

Navle‐stjernebold

Sortbrun blåfugl

Hede‐takspinder

Strandtudse

Svalehale

Sortbrun blåfugl

Vinter‐stilkbovist

Løgfrø

Dværgulvefod

Karmindompap

Kalk‐vokshat

Pukkellæbe

Latinsk navn

Entoloma jubatum

Flavocetraria nivalis

Papilio machaon

Phyllodesma ilicifolia

Anthus campestris

Epidalea calamita

Usnea filipendula

Eremobia ochroleuca

Calophasia lunula

Platycheirus immarginatus

Euphydryas aurinia

Lacerta agilis

Cladonia humilis

Cladonia phyllophora

Euphydryas aurinia

Phyllodesma ilicifolia

Eremobia ochroleuca

Setina irrorella

Erynnis tages

Paragus finitimus

Orgyia antiquoides

Papilio machaon

Lycaena hippothoe

Phyllodesma ilicifolia

Carpodacus erythrinus

Carex maritima

Cladonia humilis

Geastrum elegans

Aricia artaxerxes

Phyllodesma ilicifolia

Epidalea calamita

Papilio machaon

Aricia artaxerxes

Tulostoma brumale

Pelobates fuscus

Selaginella selaginoides

Carpodacus erythrinus

Hygrocybe calciphila

Herminium monorchis

Biotop

Ore

Hede

Åbent

Hede

Klit

Åbent

Særl.

Åbent

Klit

Vand

Åbent

Klit

Hede

Åbent

Hede

Åbent

Hede

Åbent

Hede

Åbent

Kyst

Klit

Ore

Kyst

Hede

Åbent

Kyst

Ore

Åbent

Ore

Eng

Økologiske behov

Lysåbent, næringsfattigt.

Lavt, klart ynglevand fri for fisk.

Fugtig ren luft og rigbarks‐træer

Lysåbent, næringsfattigt.

Djævelsbid til æglægning.

Solåben sandjord til æglægning

Lysåbent, næringsfattigt.

Djævelsbid til æglægning.

Lysåbent, næringsfattigt.

Lysåbent med buske og småtræer.

Lysåbent, næringsfattigt.

Lavt, klart ynglevand fri for fisk.

Lysåbent, næringsfattigt.

Rent ynglevandhul fri for fisk.

Lysåbent, næringsfattigt, vådt.

Lysåbent med buske og småtræer.

Lysåbent, næringsfattigt.

Kalkrig, ret lys næringsfattig bund

Trusler

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

Forstyrrelser, tilgron., eutrof., klima

For få gode vandhuller

Luftforurening

Tilgroning og eutrofiering

ukendt

Tilgroning og eutrofiering

Tilgroning, eutrofier., fragmentering

Tilgroning og lign

Tilgroning og eutrofiering

Tilgroning, eutrofier., fragmentering

Hård græsnings‐ eller brandpleje

Tilgroning og eutrofiering

Tilgroning og eutrofiering + klima

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

Tilgroning og eutrofiering

Tilgroning, eutrofier., fragmentering

Hård græsnings‐ eller brandpleje

ukendt

Indsamling, klima, tilfældig uddøen.

Tilgroning og eutrofiering

Tilgroning og lign

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

For få gode vandhuller

Tilgroning og eutrofiering

Ophør af græsning/høslet

For få gode vandhuller

Tilgroning og eutrofiering

ukendt

Tilgroning og eutrofiering

Bilag M, side 3 af 10

Bilag M

Chapter 4

267

Enhed og skov

350_53 Rubjerg Plantage

350_63 Mosbjerg

350_69 Slettingen

350_71 Blokhus Plantage

350_72 Tranum Plantage

350_78 Hune Plantage

360_13 Lyngby Hede

360_20 Stenbjerg Plantage, vestl. del

360_21 Stenbjerg Plantage, østl. del

360_22 Tvorup Syd

360_26 Ejerslev Vang

360_29 Legind Vejle

360_32 Tvorup Nord

360_35 Nystrup Plantage, vestl. del

360_42 Kronens Hede Plantage

360_51 Tved Plantage

Truet

Globalt

Art

Skønfodet rødblad

Blomster‐stængelugle

Metalvinge

Vår‐ærenpris

Hede‐takspinder

Engblåfugl

Kommabredpande

Spættet bredpande

Violetrandet ildfugl

Spættet bægerlav

Hede‐takspinder

Markfirben

Klithede‐maskesvirreflue

Lav bægerlav

Hede‐takspinder

Tinksmed

Almindelig skæglav

Entoloma turci (en art svamp)

Fintornet randtæge

Gråbåndet bredpande

Grågul bægerlav

Spættet bægerlav

Takket bægerlav

Gråbrun vokshat

Skarlagen‐vokshat

Sen damsvirreflue

Gråbrun vokshat

Knaldrød vokshat

Smalrandet humlebisværmer

Jordkrebs

Violetrandet ildfugl

Brun skjoldlav

Ensianblåfugl

Knaldrød vokshat

Rødbrun vokshat

Røggrå køllesvamp

Latinsk navn

Entoloma corvinum

Eremobia ochroleuca

Adscita statices

Veronica verna

Phyllodesma ilicifolia

Cyaniris semiargus

Hesperia comma

Pyrgus malvae

Lycaena hippothoe

Cladonia rangiformis

Phyllodesma ilicifolia

Lacerta agilis

Paragus finitimus

Cladonia humilis

Phyllodesma ilicifolia

Tringa glareola

Usnea filipendula

Entoloma turci

Coriomeris scabricornis

Erynnis tages

Cladonia zopfii

Cladonia rangiformis

Cladonia crispata

Hygrocybe fornicata

Hygrocybe punicea

Anasimyia lunulata

Hygrocybe fornicata

Hygrocybe splendidissima

Hemaris tityus

Gryllotalpa gryllotalpa

Lycaena hippothoe

Peltigera rufescens

Maculinea alcon

Hygrocybe splendidissima

Hygrocybe colemanniana

Clavaria fumosa

Biotop

Ore

Åbent

Ore

Hede

Eng

Ore

Åbent

Hede

Åbent

Hede

Klit

Hede

Vand

Særl.

Ore

Åbent

Klit

Hede

Ore

Mose

Ore

Åbent

Hede

Åbent

Ore

Økologiske behov

Lysåbent, næringsfattigt.

Høslætagtig drift, blomsterrigt.

Lysåbent, næringsfattigt.

Solåben sandjord til æglægning

Lysåbent, næringsfattigt.

Vandrig hede‐klit‐natur med fred.

Fugtig ren luft og rigbarks‐træer

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, vådt.

Lysåbent, næringsfattigt.

Klokkeensian til æglægning

Lysåbent, næringsfattigt.

Trusler

Tilgroning og eutrofiering

Tilgroning og lign

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

Tilgroning og lign

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

Udgrøftede heder, tilgroning, klima.

Luftforurening

Tilgroning og eutrofiering

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

ukendt

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

Bilag M, side 4 af 10

Bilag M

268

Chapter 4

Enhed og skov

360_53 Hanstholm Byplantage

360_55 Vigsø rallejer

360_62 Østerild Plantage

360_73 Madsbøl Plantage

360_83 Vester Thorup Plantage

360_90 Kollerup Plantage

370_101 Indskovene

370_201 Vindum Skov

370_212 Frisenvold Laksegård

370_221 Frydensbjerg + Katbj. Odde

370_225 Ajstrup Strand

370_311 Helgenæs Syd

370_316 Vænge Sø

370_338 Holme strandarealer

370_340 Gjerrild Strand

370_401 Viborg Plantage

370_408 Klostermarken

380_102 Sønderskoven

380_104 Arealer på Nordfur

380_110 Geddal Strandenge

380_201 Alheden Skov

380_202 Kompedal Plantage

380_303 Søby Brunkulslejer

380_304 Haunstrup Brunkulslejer

380_305 Bøllingsø

Truet

Art

Orangegylden vokshat

Tråd‐vandaks

Almindelig skæglav

Ensianblåfugl

Smalrandet humlebisværmer

Olivenbrun farvetunge

Bredbægret ensian

Klitperlemorsommerfugl

Violetrandet ildfugl

Blomster‐stængelugle

Gråbrun vokshat

Siddende fontænehat

Skarlagen‐vokshat

Blomstersiv

Bændel‐vandaks

Skarlagen‐vokshat

Kalk‐vokshat

Markfirben

Drosselrørsanger

Pimpinellekøllesværmer

Røggrå køllesvamp

Stregtæge

Femplettet køllesværmer

Blomster‐stængelugle

Metalvinge

Okkergul pletvinge

Moserandøje

Mel‐rødblad

Mørkstribet vokshat

Strandtudse

Blegrød tørvelav

Spids bægerlav

Spættet bægerlav

Gråbrun vokshat

Lille blåpil

Rød bægerlav

Okkergul pletvinge

Blomster‐stængelugle

Højmosekvikløber

Latinsk navn

Biotop

Hygrocybe aurantiosplendenOre

Potamogeton filiformis

Vand

Usnea filipendula

Særl.

Maculinea alcon

Åbent

Hemaris tityus

Åbent

Microglossum olivaceum

Ore

Gentianella campestris

Åbent

Argynnis niobe

Åbent

Lycaena hippothoe

Åbent

Eremobia ochroleuca

Åbent

Hygrocybe fornicata

Ore

Arrhenia lobata

Åbent

Hygrocybe punicea

Ore

Scheuchzeria palustris

Mose

Potamogeton compressus Vand

Hygrocybe punicea

Ore

Hygrocybe calciphila

Ore

Lacerta agilis

Åbent

Acrocephalus arundinaceus Vand

Zygaena minos

Ore

Clavaria fumosa

Ore

Jalla dumosa

Åbent

Zygaena lonicerae

Åbent

Eremobia ochroleuca

Åbent

Adscita statices

Åbent

Melitaea cinxia

Åbent

Coenonympha tullia

Mose

Entoloma prunuloides

Ore

Hygrocybe radiata

Ore

Epidalea calamita

Åbent

Icmadophila ericetorum

Mose

Cladonia subulata

Hede

Cladonia rangiformis

Hede

Hygrocybe fornicata

Ore

Orthetrum coerulescens

Vand

Cladonia diversa

Ore

Melitaea cinxia

Åbent

Eremobia ochroleuca

Åbent

Agonum ericeti

Mose

Økologiske behov

Lysåbent, næringsfattigt.

Fugtig ren luft og rigbarks‐træer

Klokkeensian til æglægning

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, vådt.

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, vådt.

Lysåbent, næringsfattigt.

Solåben sandjord til æglægning

Store kraftige tagrør i søer.

Lysåbent, næringsfattigt.

Højmose

Lysåbent, næringsfattigt.

Lavt, klart ynglevand fri for fisk.

Lysåbent, næringsfattigt, vådt.

Lysåbent, næringsfattigt.

Rent vand til ynglen.

Lysåbent, næringsfattigt.

Højmose

Trusler

Tilgroning og eutrofiering

Luftforurening

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

Tilgroning og lign

ukendt

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

Tilgroning og lign

ukendt

Tilgroning og lign

Tilgroning og eutrofiering

Tilgroning og lign

Tilgroning og eutrofiering

Tilgroning og lign

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

For få gode vandhuller

ukendt

Tilgroning og eutrofiering

ukendt

Tilgroning og eutrofiering

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

Tilgroning, eutrofier., fragmentering

Bilag M, side 5 af 10

Bilag M

Chapter 4

269

Enhed og skov

380_305 Bøllingsø

380_401 Hastrup Plantage

380_402 Kilderne

380_404 Gludsted

514_113 Nørresø og Hestholm Kog

514_202 Klaskeroj Skov

514_203 Barsbøl Skov

514_204 Jelssøerne

514_214 Stursbøl Hegn

514_302 Stensbæk Plantage

514_401 Tvismark Plantage

514_402 Kirkeby Plantage

514_403 Vråby Plantage

514_405 Rømø Strand

514_503 Tingdal Plantage

540_20 Klakkebjerg

540_22 Topgården

540_24 Feddet

540_25 Bobakkerne

540_51 Bogensø Strand

540_71 Thurø Rev

540_72 Tåsinge Vejle

Truet

Globalt

Art

Stor gødningsrovflue

Drosselrørsanger

Jensens vokshat

Arktisk smaragdlibel

Smalrandet humlebisværmer

Sort foldekantlav

Takket bægerlav

Tørvemos‐huesvamp

Fjordterne

Sortterne

Fjerbenet vandnymfe

Spættet bredpande

Birkemus

Grågul bægerlav

Mariehøneedderkop

Spættet bredpande

Trehornet skarnbasse

Syl‐firling

Hede‐takspinder

Hede‐glansugle

Hus lav‐ugle

Kyst‐dværg

Hede‐takspinder

Strandtudse

Løgfrø

Blomster‐stængelugle

Gulfodet vokshat

Kløver‐køllesværmer

Liden stjernebold

Okkergul pletvinge

Vinter‐stilkbovist

Grøngul vokshat

Jensens vokshat

Lav bægerlav

Mosehornugle

Grønbroget tudse

Latinsk navn

Asilus crabroniformis

Acrocephalus arundinaceus

Hygrocybe ingrata

Somatochlora arctica

Hemaris tityus

Polysporina simplex

Cladonia crispata

Mycena concolor

Sterna hirundo

Chlidonias niger

Platycnemis pennipes

Pyrgus malvae

Sicista betulina

Cladonia zopfii

Eresus sandaliatus

Pyrgus malvae

Typhaeus typhoeus

Sagina subulata

Phyllodesma ilicifolia

Xestia agathina

Bryophila domestica

Nola aerugula

Phyllodesma ilicifolia

Epidalea calamita

Pelobates fuscus

Eremobia ochroleuca

Hygrocybe flavipes

Zygaena trifolii

Geastrum minimum

Melitaea cinxia

Tulostoma brumale

Hygrocybe citrinovirens

Hygrocybe ingrata

Cladonia humilis

Asio flammeus

Bufotes variabilis

Biotop

Ore

Vand

Ore

Vand

Åbent

Særl.

Hede

Mose

Vand

Åbent

Klit

Hede

Åbent

Ore

Kyst

Hede

Åbent

Hede

Åbent

Ore

Eng

Kyst

Åbent

Ore

Klit

Åbent

Økologiske behov

Lysåbent, næringsfattigt, græsning.

Store kraftige tagrør i søer.

Lysåbent, næringsfattigt.

Højmose

Flydeø til rede, rent fiskerigt vand

Flydeø til rede; insektrigt vand.

Lysåbent, næringsfattigt.

Varieret blandet vegetation

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, græsning.

Lysåbent, næringsfattigt.

Lavt, klart ynglevand fri for fisk.

Rent ynglevandhul fri for fisk.

Lysåbent, næringsfattigt.

Høslætagtig drift, blomsterrigt.

Lysåbent, næringsfattigt.

Afhængig af museår og fred

Lavt, klart vand uden planter og fisk

Trusler

Tilgroning og eutrofiering

ukendt

Tilgroning og eutrofiering

Tilgroning og lign

Forstyrrelse, for få øer til rede

Eutrofiering, dræning og tilgroning

Tilgroning og eutrofiering

Tilgroning og fragmentering

Tilgroning og eutrofiering

Tilgroning og lign

Tilgroning og eutrofiering

Tilgroning og lign

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

Tab af hede

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

For få gode vandhuller

Tilgroning og eutrofiering

Ophør af græsning/høslet

Tilgroning, eutrofier., fragmentering

Ophør af græsning/høslet

Tilgroning og eutrofiering

For få gode vandhuller

Bilag M, side 6 af 10

Bilag M

270

Chapter 4

Enhed og skov

540_91 Nordlangeland

550_101 Nyminde Plantage, nordl. del

550_102 Nyminde Plantage, sydl. del

550_103 Blåbjerg Plantage

550_116 Tipperne

550_204 Vrøgum Plantage

550_302 Vejers Plantage, sydlige del

550_303 Ål Plantage

550_401 Bordrup Plantage

550_402 Oksby Plantage

550_403 Ho Plantage

550_410 Fanø Plantage

560_101 Gødding Skov

560_201 Frederikshåb Plantage

560_203 Haltrup Hede

560_516 Harte Skov

560_517 Troldhedebanen

560_604 Tørrepladsen

560_606 Klingebæk

560_610 Kongens Kær

570_111 Ketting Nor

570_202 Sønderskov

570_210 Mjang Dam

570_213 Dybbøl

Truet

Globalt

Art

Lav bægerlav

Rød bægerlav

Rødlig vandaks

Moseperlemorsommerfugl

Strandtudse

Lyng‐silke

Stor tornskade

Karmindompap

Klitperlemorsommerfugl

Kløver‐køllesværmer

Mat skjoldlav

Blomster‐stængelugle

Kortsporet blærerod

Lyngpenselspinder

Strandtudse

Argusblåfugl

Ensianblåfugl

Klokkelyng‐ugle

Moserandøje

Hede‐glansugle

Lyng‐silke

Hede‐takspinder

Hede‐glansugle

Kløver‐køllesværmer

Hede‐takspinder

Vandstær

Løgfrø

Metalvinge

Coranarta cordigera (sommerfugl)

Pungmejse

Birkemus

Vandstær

Jensens vokshat

Pungmejse

Svalehale

Drosselrørsanger

Pungmejse

Gråbrun vokshat

Latinsk navn

Cladonia humilis

Cladonia diversa

Potamogeton rutilus

Boloria aquilonaris

Epidalea calamita

Cuscuta epithymum

Lanius excubitor

Carpodacus erythrinus

Argynnis niobe

Zygaena trifolii

Peltigera malacea

Eremobia ochroleuca

Utricularia ochroleuca

Orgyia antiquoides

Epidalea calamita

Plebejus argus

Maculinea alcon

Heliothis maritima

Coenonympha tullia

Xestia agathina

Cuscuta epithymum

Phyllodesma ilicifolia

Xestia agathina

Zygaena trifolii

Phyllodesma ilicifolia

Cinclus cinclus

Pelobates fuscus

Adscita statices

Coranarta cordigera

Remiz pendulinus

Sicista betulina

Cinclus cinclus

Hygrocybe ingrata

Remiz pendulinus

Papilio machaon

Acrocephalus arundinaceus

Remiz pendulinus

Hygrocybe fornicata

Biotop

Klit

Ore

Vand

Mose

Åbent

Hede

Åbent

Eng

Hede

Åbent

Vand

Hede

Åbent

Hede

Mose

Hede

Åbent

Hede

Eng

Hede

Vand

Åbent

Hede

Mose

Åbent

Vand

Ore

Mose

Åbent

Vand

Mose

Ore

Økologiske behov

Lysåbent, næringsfattigt.

Højmose

Lavt, klart ynglevand fri for fisk.

Lysåbent, næringsfattigt.

Lysåbent med buske og småtræer.

Lysåbent, næringsfattigt.

Høslætagtig drift, blomsterrigt.

Lysåbent, næringsfattigt.

Lavt, klart ynglevand fri for fisk.

Lysåbent, næringsfattigt, lyng.

Klokkeensian til æglægning

Lysåbent, næringsfattigt.

Højmose

Lysåbent, næringsfattigt.

Høslætagtig drift, blomsterrigt.

Lysåbent, næringsfattigt.

Strømrig stenet, ren å.

Rent ynglevandhul fri for fisk.

Lysåbent, næringsfattigt.

Busket mose med dunhammer

Varieret blandet vegetation

Strømrig stenet, ren å.

Lysåbent, næringsfattigt.

Busket mose med dunhammer

Lysåbent, næringsfattigt.

Store kraftige tagrør i søer.

Busket mose med dunhammer

Lysåbent, næringsfattigt.

Trusler

Tilgroning og eutrofiering

Tilgroning, eutrofier., fragmentering

For få gode vandhuller

Tilgroning og eutrofiering

Tilgroning og lign

ukendt

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

For få gode vandhuller

Tilgroning og eutrofiering

Tilgroning, eutrofier., fragmentering

Tab af hede

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

Tab af hede

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

Vandforurening, uegnede vandløb.

For få gode vandhuller

Tilgroning og lign

Tilgroning og eutrofiering

ukendt

Tilgroning og fragmentering

Vandforurening, uegnede vandløb.

Tilgroning og eutrofiering

ukendt

Tilgroning og eutrofiering

ukendt

Tilgroning og eutrofiering

Bilag M, side 7 af 10

Bilag M

Chapter 4

271

Enhed og skov

570_502 Frøslev Plantage

570_508 Søgård Skov

570_511 Areal ved Assenholm

570_532 Lerskov Plantage

570_534 Kalvø mv.

570_626 Slivsø

724_102 Hannenov Skov

724_120 Albuen

724_122 Lysbro Mose v. Hejrede Sø

724_129 Hyllekrog Fyr

740_102 Blykobbe Plantage

740_106 Ejendommen Lassen

740_201 Hammerknuden

740_206 Del af Ringebakkerne

740_209 Borrelyngen

740_302 Rø Plantage

750_103 Jægersborg Hegn

750_107 Geel Skov

750_201 Jægersborg Dyrehave

750_202 Rygård

750_302 Strandparken

750_303 Ordrup Krat

750_501 Kongelunden

Truet

Globalt

Art

Lille kobbervandnymfe

Moseperlemorsommerfugl

Jensens vokshat

Brun skjoldlav

Sommer‐rødblad

Drosselrørsanger

Lancet‐skeblad

Grønbroget tudse

Strandtudse

Rødhovedet and

Grønbroget tudse

Karmindompap

Skarlagen‐vokshat

Karmindompap

Markfirben

Tråd‐vandaks

Karmindompap

Pungmejse

Glat navlelav

Kliddet navlelav

Klippe‐kantskivelav

Knudret kraterlav

Koral‐prikvortelav

Lav bægerlav

Argusblåfugl

Engperlemorsommerfugl

Markperlemorsommerfugl

Svalehale

Broget metalsvirreflue

Knaldrød vokshat

Vandstær

Svalehale

Grøngul vokshat

Jensens vokshat

Gulfodet vokshat

Sødgræs‐stængelugle

Drosselrørsanger

Elme‐ugle

Karmindompap

Latinsk navn

Lestes virens

Boloria aquilonaris

Hygrocybe ingrata

Peltigera rufescens

Entoloma solstitiale

Acrocephalus arundinaceus

Alisma lanceolatum

Bufotes variabilis

Epidalea calamita

Netta rufina

Bufotes variabilis

Carpodacus erythrinus

Hygrocybe punicea

Carpodacus erythrinus

Lacerta agilis

Potamogeton filiformis

Carpodacus erythrinus

Remiz pendulinus

Umbilicaria polyphylla

Umbilicaria deusta

Lecanora intricata

Diploschistes scruposus

Pertusaria corallina

Cladonia humilis

Plebejus argus

Brenthis ino

Argynnis aglaja

Papilio machaon

Lejogaster tarsata

Hygrocybe splendidissima

Cinclus cinclus

Papilio machaon

Hygrocybe citrinovirens

Hygrocybe ingrata

Hygrocybe flavipes

Phragmatiphila nexa

Acrocephalus arundinaceus

Polymixis polymita

Carpodacus erythrinus

Biotop

Vand

Mose

Ore

Hede

Ore

Vand

Åbent

Vand

Åbent

Ore

Åbent

Vand

Åbent

Mose

Særl.

Sten

Særl.

Klit

Åbent

Mose

Åbent

Mose

Ore

Vand

Åbent

Ore

Mose

Vand

Åbent

Økologiske behov

Ren sø med mange vandplanter.

Højmose

Lysåbent, næringsfattigt.

Store kraftige tagrør i søer.

Lysåbent, næringsfattigt.

Lavt, klart vand uden planter og fisk

Lavt, klart ynglevand fri for fisk.

Ren sø med mange vandplanter.

Lavt, klart vand uden planter og fisk

Lysåbent med buske og småtræer.

Lysåbent, næringsfattigt.

Lysåbent med buske og småtræer.

Solåben sandjord til æglægning

Lysåbent, næringsfattigt.

Lysåbent med buske og småtræer.

Busket mose med dunhammer

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, lyng.

Lysåbent, næringsfattigt+ mjødurt.

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, vådt.

Lysåbent, næringsfattigt.

Strømrig stenet, ren å.

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, vådt.

Store kraftige tagrør i søer.

Lysåben, næringsfattig + Elmetræ

Lysåbent med buske og småtræer.

Trusler

ukendt

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

ukendt

Tilgroning og eutrofiering

For få gode vandhuller

For få rene søer med vandplanter.

For få gode vandhuller

ukendt

Tilgroning og eutrofiering

ukendt

Tilgroning og lign

Tilgroning og eutrofiering

ukendt

Tilgroning og eutrofiering

ukendt

Tilgroning og eutrofiering

Vandforurening, dræning

Tilgroning og eutrofiering

Vandforurening, uegnede vandløb.

Tilgroning og eutrofiering

ukendt

Bilag M, side 8 af 10

Bilag M

272

Chapter 4

Enhed og skov

750_502 Vestamager

750_504 Kalvebod Fælled

760_205 Nakke Skov

760_401 Sonnerup Skov

760_405 Kårup Skov

760_408 Røsnæs

770_113 Ll. lyngby mose

770_121 Holløse bredning

770_123 Solbjerg enge

770_206 Tisvilde hegn

770_209 Hyllingbjerg

770_218 AP‐Møllergrunden

770_301 Valby Hegn

770_304 Nejede vesterskov

770_601 Store Dyrehave

770_604 Præstevang

770_821 Nakkehoved

770_823 Hornbæk Plantage

770_825 Teglstrup Hegn

770_826 Egebæksvang Skov

770_843 Danstrup Hegn

Truet

Globalt

Art

Brushane

Grønbroget tudse

Femplettet køllesværmer

Koralrød vokshat

Pungmejse

Svalehale

Overdrevsløber

Stor gødningsrovflue

Lyngpenselspinder

Rodstreg‐øreugle

Spættet bredpande

Sortfodet bægerlav

Markfirben

Løgfrø

Drosselrørsanger

Svalehale

Drosselrørsanger

Markfirben

Plettet kongepen

Liden stjernebold

Pimpinellekøllesværmer

Stribet hedespinder

Pude‐korallav

Pimpinellekøllesværmer

Blomstersiv

Tørve‐nøgenhat

Violetrandet ildfugl

Bjerg‐mus

Elme‐ugle

Fætter‐ugle

Rodstreg‐øreugle

Elme‐ugle

Fætter‐ugle

Rodstreg‐øreugle

Dolomedes plantarius (edderkop)

Rudret skivelav

Stengærde‐kantskivelav

Soral‐bredskivelav

Stengærde‐kantskivelav

Latinsk navn

Philomachus pugnax

Bufotes variabilis

Zygaena lonicerae

Hygrocybe constrictospora

Remiz pendulinus

Papilio machaon

Carabus cancellatus

Asilus crabroniformis

Orgyia antiquoides

Spaelotis ravida

Pyrgus malvae

Cladonia phyllophora

Lacerta agilis

Pelobates fuscus

Acrocephalus arundinaceus

Papilio machaon

Acrocephalus arundinaceus

Lacerta agilis

Hypochoeris maculata

Geastrum minimum

Zygaena minos

Spiris striata

Stereocaulon evolutum

Zygaena minos

Scheuchzeria palustris

Psilocybe turficola

Lycaena hippothoe

Caradrina montana

Polymixis polymita

Protolampra sobrina

Spaelotis ravida

Polymixis polymita

Protolampra sobrina

Spaelotis ravida

Dolomedes plantarius

Lecidea fuscoatra

Lecanora rupicola

Porpidia soredizodes

Lecanora rupicola

Biotop

Eng

Åbent

Ore

Mose

Åbent

Ore

Hede

Åbent

Hede

Åbent

Vand

Åbent

Vand

Åbent

Kyst

Ore

Hede

Særl.

Ore

Mose

Åbent

Hede

Åbent

Hede

Åbent

Hede

Åbent

Mose

Særl.

Sten

Særl.

Sten

Økologiske behov

Våd lavtvoksende ret fersk eng

Lavt, klart vand uden planter og fisk

Lysåbent, næringsfattigt.

Busket mose med dunhammer

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, græsning.

Lysåbent, næringsfattigt.

Solåben sandjord til æglægning

Rent ynglevandhul fri for fisk.

Store kraftige tagrør i søer.

Lysåbent, næringsfattigt.

Store kraftige tagrør i søer.

Solåben sandjord til æglægning

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, vådt.

Højmose

Lysåbent, næringsfattigt.

Lysåben, næringsfattig + Elmetræ

Lysåbent, næringsfattigt.

Lysåben, næringsfattig + Elmetræ

Lysåbent, næringsfattigt.

Lysåbent, næringsfattigt, vådt.

Lysåbent, næringsfattigt.

Trusler

Mangler slæt / græsning; Predation.

For få gode vandhuller

Tilgroning og lign

Tilgroning og eutrofiering

ukendt

Tilgroning og eutrofiering

Tilgroning, eutrofier., fragmentering

Tilgroning og eutrofiering

Hård græsnings‐ eller brandpleje

ukendt

Tilgroning og eutrofiering

Tilgroning og lign

For få gode vandhuller

ukendt

Tilgroning og eutrofiering

ukendt

Tilgroning og lign

Tilgroning og eutrofiering

Ophør af græsning/høslet

Tilgroning og lign

Indavl og dårlig spredning.

Tilgroning og eutrofiering

Tilgroning og lign

Tilgroning og eutrofiering

Tilgroning og lign

Tilgroning, eutrofier., fragmentering

ukendt

Tilgroning og eutrofiering

Bilag M, side 9 af 10

Enhed og skov

770_852 Krogerup Skovene

780_205 Jonstrup Vang

780_301 Vestskoven

780_402 Karlstrup Skov

780_404 Regnemark

780_501 Tokkekøb Hegn

780_601 Lystrup skov

780_603 Krogenlund

780_604 Slagslunde skov

Truet

Art

Rød skægrovflue

Klippe‐porina

Soral‐bredskivelav

Svalehale

Drosselrørsanger

Gaffelsnudebille

Pungmejse

Småknoppet skållav

Stengærde‐kantskivelav

Svalehale

Grønbroget tudse

Kalk‐vokshat

Vinter‐stilkbovist

Skarlagen‐vokshat

Vår‐ærenpris

Broget bredskivelav

Stor sandtæge

Klippe‐porina

Soral‐bredskivelav

Latinsk navn

Biotop

Eutolmus rufibarbis

Åbent

Porina chlorotica

Sten

Porpidia soredizodes

Særl.

Papilio machaon

Åbent

Acrocephalus arundinaceus Vand

Lixus paraplecticus

Mose

Remiz pendulinus

Mose

Xanthoparmelia verruculifer Sten

Lecanora rupicola

Sten

Papilio machaon

Åbent

Bufotes variabilis

Åbent

Hygrocybe calciphila

Ore

Tulostoma brumale

Ore

Hygrocybe punicea

Ore

Veronica verna

Ore

Porpidia tuberculosa

Særl.

Odontoscelis fuliginosa

Åbent

Porina chlorotica

Sten

Porpidia soredizodes

Særl.

Økologiske behov

Lysåbent, næringsfattigt.

Store kraftige tagrør i søer.

Lysåbent, næringsfattigt, vådt.

Busket mose med dunhammer

Lysåbent, næringsfattigt.

Lavt, klart vand uden planter og fisk

Lysåbent, næringsfattigt.

Trusler

ukendt

Tilgroning og eutrofiering

ukendt

Dræning, gødskning, tilgroning

ukendt

Tilgroning og eutrofiering

For få gode vandhuller

Tilgroning og eutrofiering

Ophør af græsning/høslet

Tilgroning og eutrofiering

Tilgroning og lign

Tilgroning og eutrofiering

Bilag M

Chapter 4

Type

Åbent

Klit

Eng

Hede

Kyst

Mose

Ore

Sten

Særl.

BIOTOP (hoved biotop)

Lidt af hvert af lysåbne typer, eller mange mulige lysåbne typer.

Klit

Eng

Hede

Kyst

Mose

Overdrev

Sten eller klippe

Særligt substrat

273

Bilag M, side 10 af 10

Bilag N

274

Chapter 4

Truet

globalt

Art

Birkemus

Grønbroget tudse

Hedepletvinge

Løgfrø

Markfirben

Mosehornugle

Sandterne

Strandtudse

Brushane

Engryle

Hede‐takspinder

Markpiber

Havterne

Klyde

Dolomedes plantarius (edderkop)

Fedtet krogmos

Gul stenbræk

Mygblomst

Grøngul vokshat

Jensens vokshat

Sortplettet blåfugl

Hvidbrystet præstekrave

Bred vandkalv

Fjordterne

Grøn frø

Grøn kølleguldsmed

Grøn mosaikguldsmed

Klokkefrø

Latterfrø

Lys skivevandkalv

Sortterne

Tinksmed

Vandranke

Latinsk navn

Sicista betulina

Bufotes variabilis

Euphydryas aurinia

Pelobates fuscus

Lacerta agilis

Asio flammeus

Gelochelidon nilotica

Epidalea calamita

Philomachus pugnax

Calidris alpina schinzii

Phyllodesma ilicifolia

Anthus campestris

Sterna paradisaea

Recurvirostra avosetta

Dolomedes plantarius

Hamatocaulis vernicosus

Saxifraga hirculus

Liparis loeselii

Hygrocybe citrinovirens

Hygrocybe ingrata

Maculinea arion

Charadrius alexandrinus

Dytiscus latissimus

Sterna hirundo

Pelophylax esculentus

Ophiogomphus cecilia

Aeshna viridis

Bombina bombina

Pelophylax ridibundus

Graphoderus bilineatus

Chlidonias niger

Tringa glareola

Luronium natans

Biotop

Åbent

Eng

Hede

Klit

Kyst

Mose

Ore

Strand

Vand

Økologiske behov

Varieret blandet vegetation

Lavt, klart vand uden planter og fisk

Djævelsbid til æglægning.

Rent ynglevandhul fri for fisk.

Solåben sandjord til æglægning

Afhængig af museår og fred

Ø til rede + mix natur til fouragering

Lavt, klart ynglevand fri for fisk.

Våd lavtvoksende ret fersk eng

Lysåbent, næringsfattigt.

Uforstyrret strand til yngel.

Fredfyldt fladvand med ø til rede

Lysåbent, næringsfattigt, vådt.

Lavt vådt lysåbent kær.

Koldt rent lysåbent kildevand.

Lav lysåben kalkrig våd vegetation.

Lysåbent, næringsfattigt.

Timian, specialmyrer, blomsterrigt.

Uforstyrret sandstrand til yngel.

Rene vandhuller og småsøer.

Flydeø til rede, rent fiskerigt vand

Ret rene vandhuller og småsøer.

Rent vand i strømrig å og bæk.

Krebseklo i rent vand.

Lavt, klart ynglevand fri for fisk.

Ret rene vandhuller og småsøer.

Rene vandhuller og småsøer.

Flydeø til rede; insektrigt vand.

Vandrig hede‐klit‐natur med fred.

Rent ret næringsfattigt vand.

Trusler

Tilgroning og fragmentering

For få gode vandhuller

Tilgroning, eutrofier., fragmentering

For få gode vandhuller

Tilgroning og lign

Tilgroning og eutrofiering

Tilfældig uddøen, forstyrrelser.

For få gode vandhuller

Mangler slæt / græsning; Predation.

Hård græsnings‐ eller brandpleje

Forstyrrelser, tilgron., eutrof., klima

Færdsel på ellers uforstyrret strand

Færdsel, forurening.

ukendt

Tilgroning og eutrofiering

Tilgroning, eutrofiering, klima

Tilgroning og eutrofiering

Færdsel på ellers uforstyrret strand

Tilgroning og eutrofiering

Forstyrrelse, for få øer til rede

Tilgroning og eutrofiering

Å regulering og forurening

Tilgroning og eutrofiering

For få gode vandhuller

Tilgroning og eutrofiering

Eutrofiering, dræning og tilgroning

Udgrøftede heder, tilgroning, klima.

Tilgroning og eutrofiering

Bilag N, side 1 af 1

APPENDIX I

Where are Europe’s last primary forests?

Francesco Maria Sabatini, Sabina Burrascano, William S. Keeton

Christian Levers, Marcus Lindner, Florian Pötzschner

Pieter Johannes Verkerk, Jürgen Bauhus, Erik Buchwald

Oleh Chaskovsky, Nicolas Debaive, Ferenc Horváth

Matteo Garbarino, Nikolaos Grigoriadis, Fabio Lombardi

Inês Marques Duarte, Peter Meyer, Rein Midteng, Stjepan Mikac

Martin Mikolas, Renzo Motta, Gintautas Mozgeris, Leónia Nunes

Momchil Panayotov, Peter Ódor, Alejandro Ruete, Bojan Simovski

Jonas Stillhard, Miroslav Svoboda, Jerzy Szwagrzyk

Olli-Pekka Tikkanen, Roman Volosyanchuk, Tomas Vrska

Tzvetan Mladenov Zlatanov, Tobias Kuemmerle

(Invited for resubmission, Diversity and Distributions)

276

Previous page: Ulvshale forest (photo by Erik Buchwald)

Appendix I

277

Where are Europe’s last primary forests?

Short running title:

Where are Europe’s last primary forests?

Authors:

Francesco Maria Sabatini*, Sabina Burrascano, William S. Keeton, Christian Levers, Marcus

Lindner, Florian Pötzschner, Pieter Johannes Verkerk, Jürgen Bauhus, Erik Buchwald, Oleh

Chaskovsky, Nicolas Debaive, Ferenc Horváth, Matteo Garbarino, Nikolaos Grigoriadis, Fabio

Lombardi, Inês Marques Duarte, Peter Meyer, Rein Midteng, Stjepan Mikac, Martin Mikolas,

Renzo Motta, Gintautas Mozgeris, Leónia Nunes, Momchil Panayotov, Peter Ódor, Alejandro

Ruete, Bojan Simovski, Jonas Stillhard, Miroslav Svoboda, Jerzy Szwagrzyk, Olli-Pekka

Tikkanen, Roman Volosyanchuk, Tomas Vrska, Tzvetan Mladenov Zlatanov, Tobias Kuemmerle

Addresses:

F.M. Sabatini* – Humboldt-Universität zu Berlin, Geography Department, Unter den Linden 6,

10099, Berlin, Germany. [email protected]. Corresponding

author

S. Burrascano – Sapienza, Università di Roma, Department of Environmental Biology, P.le Aldo

Moro 5, 00185, Rome, Italy. [email protected]

W.S. Keeton – University of Vermont, Rubenstein School of Environment and Natural Resources,

308 Aiken Center, Burlington, VT USA 05405 [email protected]

C. Levers – Humboldt-Universität zu Berlin, Geography Department, Unter den Linden 6, 10099,

Berlin, Germany. [email protected]

M. Lindner – European Forest Institute, Heinrich-von-Stephan-Str. 9, 51375 Bonn, Germany.

[email protected]

F. Pötzschner – Humboldt-Universität zu Berlin, Geography Department, Unter den Linden 6,

10099, Berlin, Germany. [email protected]

P.J. Verkerk –European Forest Institute, Yliopistokatu 6, 80100, Joensuu, Finland.

[email protected]

J. Bauhus – University of Freiburg, Chair of Silviculture, Faculty of Environment and Natural

Resources, Tennenbacherstr. 4, 79106 Freiburg, Germany. [email protected]

freiburg.de

E. Buchwald – The Danish Nature Agency, Førstballevej 2, DK-7183 Randbøl, Denmark.

[email protected]

O. Chaskovsky – National University of Forestry and Wood Technology, Lviv, Institute of Forest

Management, Henerala Chuprynky St, 103, 79000 Lviv, Ukraine.

[email protected]

N. Debaive – Réserves naturelles de France, La Bourdonnerie, 2 allée Pierre Lacroute, CS 67524,

21075 Dijon Cedex, France. [email protected]

H. Ferenc – MTA Centre for Ecological Research, Institute of Ecology and Botany, Alkotmány u.

2-4, 2163 Vácrátót, Hungary. [email protected]

M. Garbarino – University of Torino, Department of Agricultural, Forest and Food Sciences

(DISAFA), Largo Paolo Braccini 2, 10095, Grugliasco, Italy. [email protected]

N. Grigoriadis – Forest Research Institute of Thessaloniki, Vassilika 57006 Greece.

[email protected]

F. Lombardi – Mediterranean University of Reggio Calabria, Department of Agraria, Loc. Feo di

Vito I, Reggio Calabria, 89122, Italy. [email protected]

278

Appendix I

I. Marques Duarte – Centre for Applied Ecology “Professor Baeta Neves” (CEABN), InBio,

School of Agriculture, University of Lisbon, Tapada da Ajuda 1349-017, Lisbon,

Portugal. [email protected]

P. Meyer – Northwest German Forest Research Institute, Grätzelstraße 2, D-37079 Göttingen,

[email protected]

R. Midteng – Asplan Viak, Postboks 24, 1300 Sandvika, Norway. [email protected]

Stjepan Mikac – University of Zagreb, Faculty of Forestry, Department of Forest ecology and

silviculture, Svetosimunska 25, 10000 Zagreb, Croatia. [email protected]

M. Mikolas – Czech University of Life Sciences Prague, Faculty of Forestry and Wood Sciences,

Kam�½cká 129, 165 00 Praha 6 - Suchdol, Czech Republic; PRALES, Odtrnovie 563, 013

22 Rosina, Slovakia. [email protected]

R. Motta – University of Torino, Department of Agricultural, Forest and Food Sciences

(DISAFA), Largo Paolo Braccini 2, 10095, Grugliasco, Italy. [email protected]

G. Mozgeris – Aleksandras Stulginskis University, Institute of Forest Management and Wood

Science, Studentų 11, LT-53361 Akademija, Kaunas reg., Lithuania.

[email protected]

L. Nunes – Centre for Applied Ecology “Professor Baeta Neves” (CEABN), InBio, School of

Agriculture, University of Lisbon, Tapada da Ajuda 1349-017, Lisbon, Portugal;

University of Trás-os-Montes and Alto Douro, CITAB Centre of the Research and

Technology of Agro-Environmental and Biological Science, Qta. de Prados, 5000-801,

Vila Real, Portugal. [email protected]

M. Panayotov – University of Forestry, Dendrology Department, Kliment Ohridski 10 Blvd.,

1756 Sofia, Bulgaria. [email protected]

P. Ódor – MTA Centre for Ecological Research, Institute of Ecology and Botany, H-2163

Vácrátót, Alkotmány u. 2-4., Hungary, [email protected]

A. Ruete – Greensway AB. Ulls vägen 29A, 756 51 Uppsala, Sweden. [email protected]

B. Simovski – Ss. Cyril and Methodius University in Skopje, Faculty of Forestry in Skopje, Dept.

of Botany and Dendrology, P. O. Box 235, MK-1000 Skopje, Republic of Macedonia.

[email protected]

J. Stillhard – Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Research

Unit Forest Resources and Management, Zürcherstrasse 111, 8903 Birmensdorf,

Switzerland. [email protected]

M. Svoboda – Czech University of Life Sciences Prague, Faculty of Forestry and Wood Sciences,

Kam�½cká 129, 165 00 Praha 6 - Suchdol, Czech Republic. [email protected]

J. Szwagrzyk – University of Agriculture, Faculty of Forestry, Institute of Forest Ecology and

Silviculture, Al. 29-Listopada 46, 31-425 Kraków, Poland. [email protected]

O.-P. Tikkanen – University of Eastern Finland, School of Forest Sciences, PO Box 111, 80101

Joensuu, Finland. [email protected]

R. Volosyanchuk – WWF Danube-Carpathian Programme Ukraine, Mushaka, 42 Lviv, 79011,

Ukraine. [email protected]

T. Vrska – Silva Tarouca Research Institute, Forest Ecology Department, Lidicka 25/27, 602 00

Brno, Czech Republic. [email protected]

T.M. Zlatanov – Forest Research Institute at the Bulgarian Academy of Sciences, Department of

Silviculture, 132 "St. Kliment Ohridski" Blvd., 1756 Sofia, Bulgaria,

[email protected]

T. Kuemmerle – Humboldt-Universität zu Berlin, Geography Department, Unter den Linden 6,

10099, Berlin, Germany. [email protected]

Appendix I

279

Abstract

Aim:

Primary forests have high conservation value, serve as a reference for

understanding the functioning and dynamics of natural ecosystems, but are rare in Europe

due to historic deforestation and past forest management. Yet many primary forest

patches remain unmapped and unprotected. Our aim was to i) compile the first European-

scale map of known primary forests, ii) analyze the spatial determinants characterizing

their location, and iii) locate areas where as yet unmapped primary forests are likely to

occur.

Location:

Europe

Methods:

We aggregated data from a literature review, online questionnaires, and 32

datasets of primary forests. We used boosted regression trees to explore which

biophysical, socioeconomic and forest-related variables explain the current distribution of

primary forests. Finally, we predicted and mapped the relative likelihood of primary

forest occurrence at a 1-km resolution across all of Europe.

Results:

Data on primary forests were frequently incomplete or inconsistent among

countries. Known primary forests covered 1.4 Mha in 32 countries (0.7% of Europe’s

forest area). Most of these forests were protected (89%), but only 46% of them strictly.

Primary forests mostly occurred in mountain and boreal areas, and were unevenly

distributed across countries, biogeographical regions and forest types. Unmapped primary

forests likely occur in the least accessible and populated areas, where forests cover a

greater share of land but wood demand historically has been low.

Main Conclusions:

The current distribution of primary forests is the result of centuries of

land-use dynamics and forest management. The conservation outlook for these forests is

uncertain, however, since their small and fragmented patch structure makes them prone to

extinction debt and human disturbance. Predicting where unmapped primary forests likely

occur could aid protection efforts, especially for those countries (e.g., Romania and

Slovakia) where large areas of primary forest is being lost at an alarming pace.

Keywords

Old-growth forest, primary forest, virgin forest, forest naturalness, land-use change,

sustainable forest management, boosted regression trees, spatial determinants

280

Appendix I

1. Introduction

Primary forests, i.e. naturally regenerated forests of native species where there are

no clearly visible indications of human activities and the ecological processes are not

significantly disturbed, are becoming rare as forestland globally is cleared for agriculture

or put under active management (Mackey et al., 2015; Potapov et al., 2017). Given their

irreplaceability and unique qualities, protecting primary forests is a global concern

(Mackey et al., 2015). Not only are they cherished for their wild nature (Navarro &

Pereira, 2012), and represent a social perception of untouched nature (Schnitzler, 2014),

they are also ecologically important in regions where forests are highly fragmented

(Vandekerkhove et al., 2009). Primary forests, for instance, serve as refuges or sources of

propagules for rare or endangered species, especially for forest species sensitive to human

disturbance (Peterken, 1996; Paillet et al., 2015). Furthermore, primary forests serve as a

model for understanding natural disturbance and successional dynamics (Leibundgut,

1959; Kuuluvainen & Aakala, 2011; Král et al., 2014), especially in the face of climate

change, and provide baselines for the delivery of ecosystem services under unmanaged

conditions, including carbon stocks and sequestration (Harmon, Ferrell & Franklin, 1990;

Burrascano, Keeton, Sabatini & Blasi, 2013). Finally, primary forests help us to evaluate

human impacts on forest ecosystems and to understand the potential and the limitations of

close-to-nature forest management (Bauhus, Puettmann & Messier, 2009; Kuuluvainen &

Aakala, 2011; EEA, 2014).

In Europe, as in other human-dominated regions, historical deforestation and

forest exploitation came close to eliminating primary forests (Kaplan, Krumhardt &

Zimmermann, 2009; Potapov et al., 2017). Europe’s forests are now mainly composed of

semi-natural forests, while forests undisturbed by man account for only 4% of the total

(FOREST EUROPE, 2015). Even this little share of undisturbed forest is heavily

fragmented as no intact forest landscapes greater than 500 km

exist outside European

Russia and boreal northern Europe (Potapov et al., 2017). Finally, although some Eastern

European countries may still contain relatively large areas of primary forests (Frank et al.,

2007; Kulakowski et al., 2017), these remain often unmapped and unprotected, and are

being lost at an alarming rate (Knorn et al., 2013; Chylarecki & Selva, 2016).

Semi-natural forests cannot be easily restored to a primary status (Ford & Keeton,

2017). In the absence of anthropogenic disturbance, forests slowly recover the natural

disturbance dynamics and develop those structural features (e.g. deadwood, large live

trees, gap dynamics) that are typical for the old-growth phases of primary forests,

Appendix I

281

although this process takes decades (Vandekerkhove et al., 2009; Burrascano et al., 2013;

Paillet et al., 2015). The ongoing process of agricultural intensification in productive

areas, which co-occurs with de-intensification or even abandonment of marginal areas,

may offer important conservation opportunities (Navarro & Pereira, 2012; Schnitzler,

2014; Jepsen et al., 2015). Especially in Western Europe, satisfying wood demands

increasingly relies on imports, while forests located in remote areas are today being

managed much less intensively than in the past (Levers et al., 2014; Burrascano et al.,

2016). As a result of these economic changes, as well as of changing management

priorities, the proportion of European forests in the older age classes is increasing,

although wide regional differences exist (FOREST EUROPE, 2015). Since late-

successional forests are similar to primary forests in terms of conservation value,

ecological functioning, and ecosystem services provisioning, efforts devoted at

identifying and protecting primary forests should also include these late-successional

forests, especially given that in many European regions they represent the most natural

forests still existing in the landscape.

For the purpose of this study, we thus use the term ‘primary

forests’

(sensu

(Buchwald, 2005), to include all forests having a high naturalness, without implying that

these forests were never cleared nor disturbed by man. A growing body of knowledge

about the structure and dynamics of such primary forests in Europe has accumulated

recently (Keeton et al., 2010; Kuuluvainen & Aakala, 2011; Burrascano et al., 2013;

EEA, 2014). This research generally focused on a few iconic primary forests, such as

Białowieża in Poland, Uholka-Shyrokyi Luh in Ukraine, �½ofín in the Czech Republic,

and Izvoarele Nerei in Romania (Bernadzki et al., 1998; Veen et al., 2010; Král et al.,

2014; Hobi, Commarmot & Bugmann, 2015). Only a few countries have systematically

surveyed remaining primary forest fragments, such as the Czech Republic, Slovakia and

France (Adam & Vrška, 2009; Paillet et al., 2015), yet large regional gaps remain.

Transboundary efforts for mapping and protecting primary forests are rare and confined

to specific ecoregions (e.g., the Carpathians, the green belt of Fennoscandia), protected

areas (Parviainen et al., 2000; Frank et al., 2007) or specific forest types (e.g., UNESCO

network of primeval beech forests).

Overall, little effort has been devoted to consolidating and harmonizing

information at the continental scale (García Feced, Berglund & Strnad, 2015), and no up-

to-date European-wide database and map of primary forests exists. As a result, systematic

research to quantify the extent of primary forests in Europe, to assess whether primary

282

Appendix I

forests are adequately protected, or to understand what determines their spatial

distribution is missing. A map of the primary forests of Europe, is thus highly needed, to

ensure that primary forests receive adequate recognition and protection (Mackey et al.,

2015), and as a starting point for a systematic gap analysis that highlights those

biogeographical regions or forest types for which primary forests are absent or

underrepresented. Finally, analyzing the determinants of the spatial distribution of

primary forests could help to indicate the pressures faced by these ecosystems, as well as

candidate sites for restoration initiatives (Navarro & Pereira, 2012; Schnitzler, 2014).

In this paper, we addressed the following questions:

1) What is the current distribution of primary forests across Europe, biogeographical

regions, forest types and protection levels?

2) Which biophysical, socio-economic historical land-use factors determine the extant

pattern of primary forests?

3) What are the areas with the highest likelihood of finding previously unmapped

primary forests?

2. Methods

Primary forest database

To produce the first map of known European primary forests, we followed the

framework proposed by Buchwald (2005) that defines primary forests as “Relatively

intact forest areas that have always or at least for the past sixty to eighty years been

essentially unmodified by human activity. Human impacts in such forest areas have

normally been limited to low levels of hunting, fishing and harvesting of forest products,

and, in some cases, to historical or pre-historical low intensity agriculture”.

Our

definition thus comprises all those forests previously indicated as primeval, virgin, near-

virgin, old-growth and long-untouched (Buchwald, 2005).

Based on this workable definition, we conducted a literature review and collected

all the studies published between January 2000 and January 2017, reporting basic

information on primary forests in Europe, excluding Russia. We limited our review to

papers published after 2000, to avoid including those forests that, although being reported

as primary in older papers, may have meanwhile lost their primary status due to logging

or other human disturbances. We identified relevant publications in the ISI Web of

Appendix I

283

Knowledge using the search term ‘(primary OR virgin OR old-growth OR primeval)

AND forest*’ in the title field. The initial search was then refined using geographical and

subject areas as filters (see Supplementary material – Appendix 1 for details). This

preliminary list of papers was then supplemented with literature in their own reference

lists as well as with studies known to the authors. For all papers, we extracted the location

and basic information on the primary forests described. In addition, we sent out a

questionnaire to scientists and experts on primary forests to collect information on (i)

existing maps and databases of primary forests in their country, (ii) primary forests not

yet included in existing maps and databases, and (iii) contacts of additional experts. In

total, we contacted 134 forest experts from 32 European countries. When a suitable

dataset or map was found, we invited the data owner to join our informal network of

forest scientists, and share the dataset in their possession.

All data were integrated into a geodatabase, where each primary forest patch was

reported either as a polygon or as a point location. Our minimum mapping unit was 2 ha.

For each forest, we gathered a set of basic descriptors, including name, location,

naturalness level (following the broad definitions reported in Buchwald 2005), extent, and

dominant tree species. We followed (EEA, 2006) to assign each stand to a broad forest

type, based on the stand’s dominant tree species, elevation, and biogeographical region

(BfN, 2003). We derived the protection status and IUCN category of each forest patch

based on the World Database of Protected Area (UNEP-WCMC & IUCN, 2017). A

detailed description of the database architecture and of each dataset is provided in the

Supporting Information (Table S1, Table S2 and Appendix 2).

Biophysical and socio-economic location characteristics of the mapped

forests

Based on the variables that were previously used as spatial determinants of harvest

intensity and wood production across Europe (Levers et al., 2014; Verkerk et al., 2015),

we identified a set of 19 biophysical (including climate, soil, topography and forest

conditions), socio-economic and historical land-use variables that could explain primary

forest distribution (Table 1). Most predictors were available as raster layers with a

resolution of 1x1 km or finer, with the exception of three variables that had either a 0.5°

resolution, or were available at the country level. We re-projected all predictors to the

Lambert Azimuthal Equal Area projection. We checked for collinearity and excluded

284

Appendix I

collinear predictors when an individual variable returned a Variance Inflation Factor

(VIF) greater than 10 (Dormann et al., 2013), or returned a Pearson’s r >0.7 with another

variable (in this case the variable having the highest VIF was excluded; Tab. 1).

Relative likelihood of the occurrence of und etected primary forests

We converted the map of primary forests to a 1-km presence-absence raster and

used Boosted Regression Trees (BRTs) to explore the relationships between our set of

predictors and the occurrence of primary forests in order to estimate the relative

likelihood that a gridcell contained a primary forest patch. BRTs are non-parametric

models based on decision trees in a boosting framework. They have the advantage of not

requiring prior assumptions, of being relatively robust against overfitting, missing data,

and collinearity, and thus represent a flexible approach for uncovering nonlinear

relationships and interactions among predictors. BRTs are increasingly used for attaining

system understanding, hypothesis testing, and statistical inferences (Elith, Leathwick &

Hastie, 2008; Dormann et al., 2013). Our BRT was parameterized using a learning rate of

0.02, a tree complexity of 5, and a bag fraction of 0.7 (Elith et al., 2008). We used the

gbm.step

routine provided by the

dismo

package (Hijmans, Phillips, Leathwick & Elith,

2011) in R (R Development Core Team, 2015) to determine the optimal number of trees.

All the analyses were run after masking non-forest areas as in (Gallaun et al., 2010).

Since the data on primary forest presence were spatially clustered and this may

lead to inaccurate models (Phillips et al., 2009), we used a spatial filtering approach to

rarefy the available data on a 5x5 km grid. To account for the bias in our dataset due to

some countries not reporting any or very few data, we also created a map of sampling

effort (1: high sampling effort, 0: low sampling effort; Figure S1). We selected 37,060

pseudo-absence points (i.e. ten times the number of presences after the rarefaction) in the

two sampling effort classes proportionally to the presence points. To account for

remaining spatial bias, we used the

pwdSample

function in the

dismo

package to pair each

test presence site with the closest test pseudo-absence site prior to evaluating the

performance of our model. We also tested for spatial autocorrelation in model residuals

using Moran’s I.

We used the receiver-operating characteristic curves (ROC) and the area under the

curve (AUC) to evaluate prediction performance. Since AUC is only rank-based, we also

Appendix I

285

calculated Pearson’s correlation between the observed presence\pseudo-absence and the

likelihood predicted from the BRT model (Phillips et al., 2009). Finally, we used the true

positive and negative rates, to calculate model accuracy and precision when using

different likelihood thresholds for discriminating between predicted primary forest

occurrence vs. absence. The threshold returning the highest accuracy was used to create a

map of the 1x1 km forested grid cell potentially containing one or more patches of

primary forest. The relative importance of predictors was evaluated according to the

number of times that a variable was selected for splitting, weighted by the squared

improvement to the model as a result of each split, and averaged over all trees (Elith et

al., 2008). For those predictors with a relative importance above that expected by chance

(100%/number of predictors), we produced partial dependency plots constrained between

the 2.5 and 97.5 percentiles of the predictor range and smoothed using a LOESS

interpolation (span parameter = 0.2) to enhance interpretability.

3. Results

Our database covered 1.4 Mha of primary forest in 32 European countries (Fig. 1).

This database was composed of 32 regional datasets (Table S2) that we integrated with

data on additional 254 primary forest patches, described in 94 studies or reports retrieved

through the literature review (Table S3). A list of the data sources is found in Appendix 3.

Most of the primary forests were located in northern Europe, especially Finland (0.9

Mha), and Eastern Europe (0.2 Mha), especially Ukraine, Bulgaria and Romania (Table

S4). The countries having the highest proportion of primary forest where Finland (2.9%

of national territory), Switzerland, Lithuania, Slovenia and Bulgaria (each about 0.5%;

Fig. 2). Countries for which we were not able to retrieve data on primary forests were

Latvia, Belarus, Moldova and Ireland, while only scattered information was collected for

Sweden, Austria, United Kingdom, Bosnia-Herzegovina, Montenegro and Serbia (Fig. 2).

Primary forests occurred mostly in the boreal (1 Mha, 1% of that biogeographical

region) and the alpine regions (0.4 Mha, 0.6%). The Macaronesian region also had a high

relative proportion of primary forests, all of it located in the Laurissilva of Madeira

(15,100 ha, 1.5%; Table S5). The mapped primary forest patches were, on average, very

small: the median size was only 24 ha, and only 4.3% of the patches were larger than

1,000 ha. Most (89.1%) of the primary forest in our dataset was protected, but only 46%

was currently under strict protection (IUCN category I), with an additional 24% being

included in national parks (IUCN category II; Fig. 3, Table S4).

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Appendix I

With regards to the forest types (FTs,

sensu

EEA, 2006), boreal forest (FT1)

accounted for the highest share of the mapped primary forests (1.09 Mha), followed by

mountain beech forest (FT7 – 0.15 Mha), and, to a minor extent, alpine coniferous forest

(FT3 – 0.07 Mha; Fig. 1, Fig. S2). According to the definitions reported in Buchwald

(2005), most of the primary forests in our dataset was near-virgin (n7 – 1.20 Mha), while

old-growth (n6 – 0.15 Mha) or long-untouched stands (n5 – 0.11 Mha) accounted only for

a minor fraction (10%) of the cumulative area we mapped. However, when considering

the number of polygons rather than the area, the highest share of the forest patches we

mapped were classified as old-growth forests and belonged to the boreal (FT1), alpine

coniferous (FT3) and mountain beech (FT7) forest types (Fig. S3).

The Boosted Regression Tree modeling provided insights into the relative

importance of our predictors in determining the spatial patterns of known primary forests.

The BRT model fitted 2,050 trees and returned a relatively high cross-validated AUC and

correlation (mean ± SD range 0.86±0.005 and 0.63±0.008, respectively). When

evaluating the model performance on the independent test data, the AUC and the

correlation were lower (0.70 and 0.33, respectively), indicating that the model

performance was affected by the spatial dependency of our data. The highest model

accuracy (0.64) was observed for a threshold corresponding to the 90

percentile of the

probability distribution (Table S6, Fig. S4).

Biophysical, socio-economic and historical variables all played a role in

determining the likelihood of primary forest occurrence (Fig. 4). Primary forests were

more likely found in areas with higher ruggedness and water availability. Socio-economic

factors had the highest relative importance among the selected variables, with

accessibility and population density selected in 12.6% and 12.2% of all model runs.

Primary forests occurred more likely farther away from major towns and where

population density was lower. Both historical variables we used were important

predictors: the likelihood of occurrence of primary forest decreased for increasing

historical levels of wood demand up to a certain threshold, above which it increased

again. The amount of land suitable for agriculture still forested in 1850, instead, showed a

reverse U-shaped relationship. Finally, our model also highlighted differences across

biogeographical regions: the likelihood of occurrence of primary forests was higher than

average for the Alpine, Black Sea and Boreal regions.

Appendix I

287

The areas with the highest primary forest likelihood (Fig. 5) were along the

Finnish-Russian and the Finnish-Swedish borders and in mountain ranges, especially the

Carpathians, the eastern Alps, the Dinaric Mountains and, to lesser extent, the highest

parts of the Pyrenees. Areas with low primary forests likelihood were the Atlantic region,

the Britannic Archipelago, the Middle European lowlands, the Pannonian plain and the

Hemiboreal Baltic region. Areas with predicted and observed primary forest (Fig. S5)

matched in those regions where we had a high sampling size (N Finland, Slovakian and

Ukrainian Carpathians, Balkan mountains). On the contrary, our model predicted the

occurrence of scattered and isolated primary forest patches in S Finland, in the continental

lowlands, or in the western Mediterranean areas weakly. Only 38% of the area predicted

to host primary forest was included in protected areas, of which only 5.6% was under

strict protection (i.e. IUCN category I; Fig. S6).

4. Discussion

Our study produced the most comprehensive dataset on known primary forests in

Europe currently available and resulted in the first European-scale distribution map.

Known primary forests covered approximately 1.4 Mha in 32 European countries, which

represent 0.25% of terrestrial Europe and 0.7% of Europe’s forest area excluding Russia.

This extent represents only a small fraction of the 7.3 Mha of forest estimated to be

‘undisturbed by man’

in Europe (FOREST EUROPE, 2015), probably as a result of

differences in the definitions used (Appendix 4). We found a general increase in the

number of primary forest patches from the West to the East and from the South to the

North. Most of the primary forests in our dataset were located in Finland (0.9 Mha), in

the Carpathians (0.16 Mha) and in the Balkans (0.08 Mha). The area for Finland is three

times larger than previous estimates (FOREST EUROPE, 2015), possibly because we

considered as primary forests not only stands older than 160-200 years, but also those

primary forests composed of a mosaic of developmental phases still occurring in the

extreme north of Finland (Kuuluvainen & Aakala, 2011; Potapov et al., 2017). On the

contrary, the primary forest area mapped for the Carpathians is far lower than previously

reported (0.44 Mha, (FOREST EUROPE, 2015). The data we aggregated for the

Carpathians mostly derived from surveys coordinated within the framework of the UNEP

– Carpathian Convention. Not only are these inventories still incomplete in countries such

as Ukraine and Romania, but they also prioritize those forests having the highest

288

Appendix I

naturalness levels. Therefore a considerable share of forest of lower naturalness levels,

but still qualifying as primary, may remain unmapped in the Carpathians (Kulakowski et

al., 2017). The low share of primary forest in Western Europe was expected considering

the historically high population density, and long history of land-use, especially in the

Mediterranean (Jepsen et al., 2015). Biodiversity-rich Mediterranean forest types (i.e.,

FT8, FT9 and FT10) were particularly scarce in our map (Fig. S2, Fig. S3), possibly also

due to the relevance of fire in these ecosystems. Mediterranean fire-prone forests show

fundamentally different structural characteristics from temperate mesic forests, as fire,

either naturally or anthropogenically triggered, may hinder the development of structural

features typically associated with old-growth stages, such as deadwood or large trees

(Burrascano et al., 2013; Kulakowski et al., 2017). These features are commonly used for

identifying primary forests, although they may not be suitable in the Mediterranean

context, suggesting that significant portions of Mediterranean primary forest may remain

overlooked for the lack of appropriate indicators of primary conditions.

Primary forest disproportionately occurred in remote, scarcely populated areas,

mostly in rugged mountain areas or at high latitudes, i.e., on land with low agricultural

productivity or low profitability for forestry operations. This makes intuitively sense,

since accessibility and the distance from markets, or other centers of demand is one of the

main drivers of land-use allocation. Indeed, in remote and unfavorable areas such as

northern Fennoscandia, the Carpathians and the Balkan mountains, land-use history has

been shorter and less intense than in the rest of Europe (Parviainen et al., 2000; Jepsen et

al., 2015; Kulakowski et al., 2017), making the persistence of primary forests more likely.

This finding is also consistent with previous work in Fennoscandia (Kuuluvainen &

Aakala, 2011), as well as with the known bias in protected areas distribution towards

higher elevation and more remote locations (Joppa & Pfaff, 2009). Interestingly,

accessibility and population density are also important spatial determinants for explaining

the patterns of wood production and harvesting intensity in Europe (Levers et al., 2014;

Verkerk et al., 2015). The correlation between primary forest and water availability

probably reflects the same pattern, since high water availability is usually associated with

both higher mountain locations, and boreal region. Finally, our model predicted an

unexpectedly high likelihood of occurrence of primary forest in the rugged portions of the

Pyrenees and the Alps. The Pyrenees and the Alps have a longer history of land-use and

higher historical rates of forest management intensity than other European mountain

ranges, which our models could not account for.

Appendix I

289

Although difficult to treat in a spatially-explicit fashion, historical land-use

pressures played a key role in our model to explain present-day primary forest

distribution. Primary forests, for instance, had a lower likelihood of occurring in those

regions with higher historical wood demand (Fig. 4), but only up to a threshold, after

which the likelihood increased unexpectedly. We believe this relationship derives by the

occurrence of several primary forest patches in some historical mining areas where a

historical high wood demand co-occurred with a high historical forest cover, such as the

Upper Silesian province (Poland). The historical variables we used, however, did not

fully capture the role of historical events and contingencies. For instance, the occurrence

of some primary forest patches may depend on the short distance from major historical

political boundaries as in the case of the Bieszczady region (SE Poland) or the Rodope

mountains (between Greece and Bulgaria). The peripheral location of this region and\or

the historical lack of effective means for timber transportation determined the persistence

of considerable areas of primary forest well into the 20

century. These areas could have

followed a trajectory similar to other peripheral areas where primary forests were

extensively cut in the last century if political upheavals, including the establishment of the

iron curtain, did not result in their almost complete depopulation, that allowed large tracts

of primary forest to persist or re-develop (Keeton et al., 2010). In addition to major

historical events, peculiar local episodes could also explain the presence of some primary

forest patches, such as Fonte Novello, a 50 ha old-growth stand in Gran Sasso National

Park (central Italy), which is located at the boundary between two municipalities.

Ownership of this forest remnant has been contented between the two municipalities since

their formal establishment at the beginning of 19

century, and remains unresolved

nowadays. This dispute coupled with the deep economic depression of this mountain area,

saved the stand from being exploited for timber and degradation until its recent

‘rediscovery’ and protection. Other emblematic examples include primary forests that

were set aside centuries ago as hunting grounds by kings or noblemen, such as in

Białowieża (lowland Poland), Biogradska Gora National Park (Montenegro), or Central

Bohemia (Czech Republic).

The result of an unprecedented international collaboration, our dataset should be

considered as a necessary first step toward a more complete inventory. Important

limitations include high variability in data quantity and quality across countries. For many

countries no complete inventory exists, and data derives from the knowledge of local

experts, or from partial inventories with relatively narrow breadth, focussing either on

290

Appendix I

forest inside (e.g., France, Italy, Finland) or outside protected areas (e.g. Norway), or

specific regions (e.g., the Transcarpathian region of Ukraine, the French Pyrenees). In

some countries (e.g., Norway, Sweden, Latvia and Austria) we found only incomplete

data although extensive forestry statistics and databases are generally available for these

countries. For other countries with abundant forest resources and presumably also a

relatively high fraction of primary forest (e.g., many Balkan countries and Belarus), data

were unavailable, at least in the scientific literature or in digitized forms. In this case, we

advocate a higher commitment from the international community to support local

research institutions and NGOs in the collection or digitization of data on primary forests.

Few data also exist for those countries with low forest cover (e.g. <10 %) and in which

significant areas of primary forest are unlikely to occur due to historic clearing or

biophysical factors, such as the British Isles, Moldova or Cyprus.

Granting adequate protection to European primary forests should be a

conservation priority (Mackey et al., 2015), especially given the recent concerns about

commercial exploitation of old-growth forests in Eastern Europe (Knorn et al., 2013;

Chylarecki & Selva, 2016). The majority (89%) of primary forest in our dataset is

currently under some form of protection, nevertheless its future protection remains

uncertain. A high fraction of primary forests (54%) is currently found outside strictly

protected areas, and broad differences exist amongst European countries in the

management restriction applied in other protected areas (Parviainen et al., 2000; Verkerk,

Zanchi & Lindner, 2014). In some countries, some forest management activities (e.g.,

salvage logging) are allowed even in protected areas, representing a threat to primary

forests (Thorn et al.). Another concern is the small average size of primary forest patches.

Even if protected, a small patch of forest may not be large enough to host the full range of

ecological processes, and biodiversity may suffer from extinction debt (Peterken, 1996).

When large patches of primary forest do not exist, maintaining existing patches in a large

matrix of natural or semi-natural forests should be the priority. This is necessary both to

buffer the effects of direct and indirect anthropogenic disturbance on primary forests, and

because these patches could function as ‘strongholds’ for the recovery and recolonization

of many specialist species in the surrounding forest (Vandekerkhove et al., 2009). Given

the current low share of primary forests, their restoration should be a priority throughout

Europe (Navarro & Pereira, 2012; Schnitzler, 2014). Our map could be used to prioritize

those regions and forest types for possible restoration efforts. Finally, our work

highlighted areas, such as the most rugged parts of the Alps and the Pyrenees, where

Appendix I

291

land-use pressure is relatively low and primary forests could potentially occur, thus

suggesting that the opportunity-costs of restoring primary forests and associated

ecosystem processes and biodiversity in these areas may be lower than elsewhere.

5. Acknowledgements

The project FORESTS & CO has received funding from the European Union’s

Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie

grant agreement No. 658876. Additional funding sources for individual datasets are in

Appendix 2. A particular thank goes to Adam Dušan, Hanns Kirchmeir, Ann Kraut, Rok

Pisek, Oscar Schwendtner, Juraj Vysoky, Daniel Vallauri, the staff from WWF Romania,

especially Costel Bucur and Radu Melu, for providing valuable data, help and\or

suggestions. An additional thank goes to all those who responded to the questionnaires.

This work could not have been possible without the effort of all the people involved in the

collection of the data here presented on the field. To them goes our grateful

acknowledgement.

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conditions.

PloS one,

e107522.

7. Data Accessibility

The data on primary forests here presented were collected within the F&CO-NET

initiative, and remain property of the institutions, organizations or person who created or collected

them. The custodian of each dataset, i.e. person who owns or represents the contributed data, is

listed in Table S2. F&CO-NET is available on request for disclosing data to individuals or groups

of individuals for research or application purposes. Requests will be considered by the F&CO-

NET coordinator (F.M. Sabatini, T. Kuemmerle) and data released after receiving the approval

from the respective custodians. All derived data are available upon request from the

corresponding author.

8. Biosketch

Francesco Maria Sabatini focuses on the processes and determinants underlying the

distribution of plant biodiversity in forests. Most of his research has been on old-growth and

virgin forests since these represent reference systems for understanding baselines and processes

related to natural disturbance regimes and forest dynamics, tree demography, and carbon cycling.

9. Tables

See next page.

Appendix I

295

Table 1 - Description of single predictors, as grouped in classes. For each predictor, we reported the

measurement unit, resolution (Res), data source, the sign of the expected relationship with the likelihood of

occurrence of primary forests (+ positive, - negative), and data format. (R – raster, V – vector). Only underlined

variables were retained in the final model.

Class

Predictor

Description

Mes.

Res

Source

Expected

For-

unit

relationship mat

Climate

Growing

Num. days per year

days

30 arc (Hijmans et al., -

degree days

having a mean temp >

secs

2005)

(GDD)

5°C

Mean Annual Long Term mean annual °C

30 arc (Hijmans et al., -

Temperature temperature

secs

2005)

Water

Priestly-Taylor

ratio

30 arc (Trabucco &

availability

coefficient: difference

secs

Zomer, 2010)

between precipitation

and potential evapo-

transpiration

Soil

Crop suitability

Maximum suitability

0-1

30 arc (Zabel, Putzen- -

value across 16 crops.

secs

lechner &

Mauser, 2014)

Topography Elevation

Elevation a.s.l.

30 arc (NASA, 2006)

secs

Slope

30 arc (NASA, 2006)

secs

Aspect

30 arc (NASA, 2006)

secs

Ruggedness

Solar

Radiation

Forest

Conditions

Forest Cover

Forest Core

area

Growing stock

Net annual

increment

Terrain ruggedness

expressing relief energy

Potential annual direct log (MJ

–2

incident radiation

–1

*yr )

Percentage of forested %

area

Percentage of forested

area classified as core

m /ha

Annual aboveground

biomass increment

30 arc (NASA, 2006)

secs

1km (McCune &

Keon, 2002)

1 km

(Kempeneers et +

al., 2011)

(Soille & Vogt,

2009)

(Gallaun et al.,

2010)

(Busetto,

Barredo & San-

Miguel-Ayanz,

2014)

(BfN, 2003)

Ton

1km

d.m.*

-1

ha *yr

dummy

n/km

Socio-

economic

Biogeograhical

region

Population

Landscan dataset

density

Travel Time to

the nearest

city

Historical

legacies

Harvest

intensity

(2000-2015

average)

Forested Cover

Amount of land suitable %

(1850)

for agriculture still

forested in 1850

Wood demand

Historical wood demand TgC

(1828)

reconstructed for 1828

Estimated travel time to min

the nearest city of

50,000 or more people

in year 2000

Percentage of net

annual increment

harvested in 2000

30 arc (Oak Ridge

secs

National Labo-

ratory, 2005)

1 km (Nelson, 2008)

Count (FOREST

EUROPE, 2015)

Count (Kaplan et al.,

2009)

0.5 °

(McGrath et al.,

2015)

296

Appendix I

10. Figures

Figure 1 - Distribution of primary forest patches retrieved for Europe by forest type. The map of

biogeographical regions in the background follows (BfN, 2003). Forest types follow (EEA, 2006):

FT1- Boreal forest, FT2 - Hemiboreal and nemoral coniferous-mixed forest, FT3 - Alpine coniferous,

FT4-5 - Mesophytic deciduous & acidophilous forest, FT6 – Beech forest, FT7 - Mountainous beech

forest, FT8 - Thermophilous deciduous forest, FT9 - Broadleaved evergreen forest; FT10 - Coniferous

Mediterranean forest; FT11-12 – Mire and swamp forests & Floodplain forest, FT13 - Non-riverine

alder, birch or aspen, NA-NC - NoData/Unclassified.

Appendix I

297

Figure 2 – Amount of primary forest mapped in each country, and proportion of primary forest under

strict protection (IUCN category I), included in protected areas having other IUCN categories, or

unprotected. The size of the pie is proportional to the logarithm of the total primary forest extent

mapped in a country. The pie fractions only represent the data currently available and they should not

be directly compared across countries, as data quality and availability is not comparable.

Furthermore, for some countries only inventories of primary forest located either inside (e.g. Italy,

Finland, France) or outside (Norway) protected areas were available.

298

Appendix I

Figure 3 - Area of European primary forest across IUCN categories . I – Strict Nature Reserves or

Wilderness Areas; II – National Parks; III – Natural Monuments or features; IV - Habitat/Species

Management Areas; V - Protected Landscapes; VI - Protected area with sustainable use of natural

resources. When a patch of primary forest was protected under multiple levels, we only considered the

strictest category.

Appendix I

299

Figure 4 – Partial Dependency Plots (PDPs) showing the relationship between spatial determinants

and the relative likelihood of occurrence of primary forest patches in a given 1x1 km pixel. The

vertical axis of the PDPs shows fitted values for each observation along the variable’s data range

(horizontal axis). X-axes are equipped with rug plots that visualise the distribution of the respective

data space. Numbers in parentheses represent the relative importance of a given variable.

Biogeographical regions: ALP=Alpine, BLK=Black Sea, CON=Continental, MED=Mediterranean,

PAN=Pannonian, ATL=Atlantic, BOR=Boreal).

300

Appendix I

Figure 5 – Pixels showing the highest likelihood of occurrence of primary forest in Europe at a 1 x 1

km resolution. The top ranking 5% pixels were highlighted in purple, and the 90-95

percentile in

blue. Forests are reported in grey and follow (Gallaun et al., 2010).

Appendix I

301

11. Supplementary material

Appendix 1 – Literature review query.

Appendix 2 – Primary forest datasets factsheets.

Appendix 3 – Data sources of literature review.

Appendix 4 – Definition framework.

Table S1 – Geodatabase architecture.

Table S2 – Summary of primary forest datasets collected.

Table S3 – Results of literature review.

Table S4 – Summary of primary forest extent by country.

Table S5 – Summary of primary forest extent by biogeographical region.

Table S6 – Summary of BRT accuracy and precision for increasing likelihood thresholds.

Figure S1 – Fraction of the study area were systematic inventories of primary forests were

performed (bias mask).

Figure S2 – Barplot of primary forest area per forest type and naturalness level.

Figure S3 – Barplot of primary forest patches (counts) per forest type and naturalness level.

Figure S4 – Precision and accuracy of BRT model for increasing thresholds of relative

likelihoods.

Figure S5 – Map of congruency between predicted (top 10

percentile) and observed primary

forests.

Figure S6 – Map of the protections status of the areas predicted to host primary forests.

302

Appendix I

Appendix 1 – Literature review query

#Supplementary material to the paper:

Sabatini et al. 2017

Where are Europe’s last primary forests?

Submitted to: Diversity and Distribution

## Research query in Web of Knowledge, advanced search

TI= (Primary OR Virgin OR old-growth OR Primeval)

AND

TI= Forest*

AND

TS=(Europ* OR Austria OR Belgium OR Bulgaria OR Croatia OR Cyprus OR

Czech Republic OR Denmark OR Estonia OR Finland OR France OR

Germany OR Greece OR Hungary OR Ireland OR Italy OR Latvia OR

Lithuania OR Luxembourg OR Malta OR Netherlands OR Poland OR

Portugal OR Romania OR Slovakia OR Slovenia OR Spain OR Sweden OR

United Kingdom OR Norway OR Switzerland OR Ukraine OR Bosnia OR

Serbia OR Montenegro OR Albania OR Belarus)

AND

SU= ( ENVIRONMENTAL SCIENCES ECOLOGY OR FORESTRY OR

ZOOLOGY OR BIODIVERSITY CONSERVATION OR PLANT

SCIENCES OR GEOGRAPHY OR EVOLUTIONARY BIOLOGY OR

REMOTE SENSING )

Appendix I

303

Appendix 2 - Primary forest datasets factsheets

Dataset 1

DATASET NAME

Name

e-mail address

Affiliation and contacts

Forest Ecology Group CULS

Mikoláš, Martin

[email protected]

Czech University of Life Sciences

Faculty of Forestry and Wood Sciences

Department of Forest Ecology

Kam�½cka 129

Praha 6 Suchdol

16521

Czech Republic

Shapefile describing the distribution of primary forests surveyed by

the Forest Ecology Group CULS

MAP

Albania, Romania, Ukraine

PARTIAL SURVEY

2016

1. dendrochronological research - establishment of permanent study

plots and analyses of the natural disturbance regimes

2. We refer to forest remnants included in the dataset as forest with

minimal signs of direct human impact which structure, composition

and processes resulted from natural disturbances.

These stands were surveyed for the indicators of the naturalness

(e.g. course woody debris in various stages of decay, presence of

old trees) and signs of human impact, stands with evidence of past

logging and grazing were avoided. Further, we searched all available

archival information regarding the history of land use in these areas.

In these stands, permanent study plots have been established and

dendrochronological data have been collected.

Published papers :

Ukraine dataset: Trotsiuk, V., Svoboda, M., Janda, P., Mikolas, M.,

Bace, R., Rejzek, J., Samonil, P., Chaskovskyy, O., Korol, M. &

Myklush, S. (2014) A mixed severity disturbance regime in the

primary Picea abies (L.) Karst. forests of the Ukrainian Carpathians.

Forest Ecology and Management, 334, 144-153.

Romania (Calimani) dataset: Svoboda, M., Janda, P., Bače, R.,

Fraver, S., Nagel, T.A., Rejzek, J., Mikoláš, M., Douda, J., Boublík,

K., Šamonil, P., Čada, V., Trotsiuk, V., Teodosiu, M., Bouriaud, O.,

Biriş, A.I., S�½kora, O., Uzel, P., Zelenka, J., Sedlák, V. & Lehejček, J.

(2014) Landscape-level variability in historical disturbance in primary

Picea abies mountain forests of the Eastern Carpathians, Romania.

Journal of Vegetation Science, 25, 386-401.

UNPUBLISHED

Mikoláš, Martin, Miroslav Svoboda

The data were collected by project GACR no. 15-14840S

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

304

Appendix I

Data Accessibility

Regime 3: Free-access: Data are available to a wider community of

users. These data can be made available on request to non-

members after submitting a request to the FORESTS and CO, with

no need for special approval.

Dataset 2

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Region(s) covered

Data Completeness

Update status

Object of the dataset

LomJanPerBio

Garbarino, Matteo

[email protected]

Marche Polytechnic University, Dept. D3A, Via Brecce Bianche 10,

Ancona

Shapefiles describing the distribution of

picea-abies-beech

old-growth

forests in the Balkan region

MAP

BiH, MNE

PARTIAL SURVEY

2014

All the mapped forests are protected areas with their own core area

and buffer zone. They can be considered old-growth forests, but

probably Biogradska Gora and Perucica are the better preserved

followed by Lom and lastly by Janj.

The four OG forests that we mapped and surveyed were studied by

using both medium resolution remote sensing (SPOT5 satellite image

classification) and field-based forest structure surveys (regular grid

sampling design of circular plots). Only for Lom a high resolution

image (kompsat2) was used to map forest canopy gaps.

PUBLISHED - Data and Description

Motta, R., Garbarino, M., Berretti, R., Meloni, F., Nosenzo, A. &

Vacchiano, G. (2014) Development of old-growth characteristics in

uneven-aged forests of the Italian Alps. European Journal of Forest

Research, 134, 19-31.

Motta, R., Garbarino, M., Berretti, R., Bjelanovic, I., Borgogno

Mondino, E., Čurović, M., Keren, S., Meloni, F. & Nosenzo, A. (2015)

Structure, spatio-temporal dynamics and disturbance regime of the

mixed beech–silver fir–Norway spruce old-growth forest of

Biogradska Gora (Montenegro). Plant Biosystems, 149, 966-975.

Garbarino, M., Borgogno Mondino, E., Lingua, E., Nagel, T.A., Dukić,

V., Govedar, Z. & Motta, R. (2012) Gap disturbances and

regeneration patterns in a Bosnian old-growth forest: a multispectral

remote sensing and ground-based approach. Annals of Forest

Science, 69, 617-625.

Bottero, A., Garbarino, M., Dukic, V., Govedar, Z., Lingua, E., Nagel,

T. & Motta, R. (2011) Gap-phase dynamics in the old-growth forest of

Lom, Bosnia and Herzegovina. Silva Fennica, 45, 875-887.

Motta, R., Berretti, R., Castagneri, D., Dukic, V., Garbarino, M.,

Govedar, Z., Lingua, E., Maunaga, Z. & Meloni, F. (2011) Toward a

definition of the range of variability of central European mixed Fagus-

Abies-Picea forests: the nearly steady-state forest of Lom (Bosnia

and Herzegovina). Canadian Journal of Forest Research-Revue

Canadienne De Recherche Forestiere, 41, 1871-1884.

Lingua, E., Garbarino, M., Mondino, E.B. & Motta, R. (2011) Natural

disturbance dynamics in an old-growth forest: from tree to landscape.

Methodological approach

Publication Status

Publication Details

Appendix I

305

Procedia Environmental Sciences, 7, 365-370.

Ownership of the dataset

Funding acknowledgment

Matteo Garbarino and Renzo Motta (University of Torino)

Spot Image Inc., through Planet Action Program domain “forest and

deforestation,” provided funding for this research by donating satellite

imagery and image processing systems.

Regime 2 - Semi-restricted-access: Data are available for data

contributors only (i.e., custodians of other datasets) but without the

need for a specific permission in each single case.

Data Accessibility

Dataset 3

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

WWF - Old Forests in Bulgaria

Tzvetan Mladenov Zlatanov

[email protected]

Forest Research Institute, Department of silviculture, 132 "St. Kliment

Ohridski" Blvd., 1756 Sofia, Bulgaria

Shapefile describing the distribution of old-growth forests in the Balkan

range and Rhodopes Mountain, Bulgaria

DATABASE

The Balkan range and Rhodopes Mountain, Bulgaria

PARTIAL SURVEY

2016

Project title "Mapping of old-growth forests in Bulgaria".

Project promoter: WWF Bulgaria.

An index for identification and evaluation of old-growth forests was

used for stand selection. The criteria included: (1) Gradual reduction of

the number of trees with the diameter increase; (2) Presence of

certain number of large trees (depending on species); (3) Presence of

standing and fallen dead trees in different wood decomposition

classes (with threshold of coarse woody debris depending on

species); (4) Heterogeneous spatial structure on predominating part of

the forest territory with presence of natural gaps and regeneration in

different development phases; (5) Lack of signs or minimal signs of

management activities in the past.

Elaboration of the dataset: Step 1 – Selection of potential stands

based on Forest Management Plans provided by the Local Forest

Enterprises; Step 2 – Further selection based on remote sensing –

orthophoto pictures and topographic ancillary information; Step 3 –

best candidates were inventoried on the field by assessment of DBH

and spatial structure, regeneration potential, dead wood accumulation

and traces of management. A systematic grid of 25 plots (100 m in

size each) was set in a representative stand in each complex. The

following stand structural attributes were measured and evaluated in

each plot: DBH structure; growing stock (by species and total);

reproduction density (by species, size and total); dead wood (by size

and degrees of decomposition); gaps – number and size; canopy

closure; traces of past management; topographic features. Nearly

2500 plots were established on the field.

PUBLISHED - Only description

https://naukazagorata.files.wordpress.com/2014/09/ng-1-

Methodological approach

Publication Status

Publication Details

306

Appendix I

2_2013_2.pdf

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Tzvetan Mladenov Zlatanov

WWF Bulgaria, project No.9E0710.04, „IKEA Stage 4 and 5”

Both other custodians and the wider community can use the dataset

bur only after permission of the dataset in question custodian

Dataset 4

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

BULGARIA-Conifers

Panayotov Momchil

[email protected]

University of Forestry, Dendrology Department, Sofia 1756, Kliment

Ohridski 10 Blvd., Bulgaria

Shapefile with old-growth coniferous forests in Rila and Pirin National

Parsk

DATABASE

Bulgaria, Pirin National Park and Rila National Park

PARTIAL SURVEY

2015

Old-growth forests in the national forest database of Bulgaria (above

120 years); they were verified on orthophotos or visited on site.

Small forest patches, which were adjacent to roads, were excluded

despite other characteristics. The data is based on "compartment"

level, which means that also younger patches of more than several

hectares between older patches were excluded as not matching the

first criteria of choice.

The initial data collection took place during inventory works form

mapping and general inventory purposes. However, for national

parks in Bulgaria the level of detail was not as high as for

management forests. There are occasions, when in field studies

higher ages were estimated than in the inventory data. For some of

the forests there was detailed work performed. Such are listed in

scientific publications.

UNPUBLISHED

Panayotov, M., Tsvetanov, N., Gogushev, G., Tsavkov, E., Zlatanov,

T., Anev, S., Ivanova, A., Nedelin, T., Zafirov, N., Aleksandrov, N. &

Dountchev, A. (2016)

Mountain coniferous forests in Bulgaria –

structure and natural dynamics.

University of Forestry, Sofia, 332 pp.

Panayotov, M., Kulakowski, D., Laranjeiro Dos Santos, L. & Bebi, P.

(2011) Wind disturbances shape old Norway spruce-dominated

forest in Bulgaria.

Forest Ecology and Management,

262,

470-481.

Rangelova, P. & Panayotov, M. (2013) Structure of old-growth Pinus

heldreichii forests in Pirin Mountains.

Bulgarian Journal of

Agricultural Science,

19,

273-276.

The original datasets belong to the Bulgarian government, Ministry of

Environment and Waters.

Data on the structure and natural dynamics of certain forests was

obtain in the framework of studies carried within the projects

„Subalpine

forest development in Bulgarian Mountain Forests under

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Appendix I

307

Data Accessibility

climate change”

(IZEBZO143109) funded by the Swiss National

Science Fund and the Ministry of education and science of

Bulgaria.";

“Natürliche

Dynamik von Lawinenschutzwäldern”

funded by the

Velux Foundation in Switzerland; and

„Natural

disturbances and dynamics in pristine Norway spruce (Picea

abies Karst) forests”

funded by the National Science Fund of

Bulgaria.

Regime 1 - Restricted-access: Data are available for data

contributors only (i.e., custodians of other datasets) and their use

depends on the permission of the respective data custodian.

Specific data for certain forests are however available from the

contributor

Dataset 5

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Croatian Science Foundation under the project IP-2014-09-1834

Regime 3: Free-access: Data are available to a wider community of

users. These data can be made available on request to non-members

after submitting a request to the FORESTS and CO, with no need for

special approval.

field research

UNPUBLISHED

CroatianOGForest

Mikac, Stjepan

[email protected]

University of Zagreb, Faculty of Forestry

Croatian OG forests reserve

MAP

Croatia

COMPLETE INVENTORY

2015

Dataset 6

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

DATABASE

CZ_oldgrowth_forests

Vrska Tomas

[email protected]

Silva Tarouca Research Institute, dpt. Forest Ecology, Lidicka 25/27,

602 00 Brno, CZ

308

Appendix I

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Czech Republic

COMPLETE INVENTORY

2016

Project objectives: (i) classification of forest naturalness; (ii) to prepare

the "Natural Forests Databank" as the usable system for the public,

nature conservation authorities, researchers etc. We used the

definitions for different degrees of naturalness incl. old-growth,

primeval etc. The set of 31 criterions for evaluation was created and

used for the whole country. The methodological approach is the part of

Czech rule of law (Regulation No. 64/2011- Ministry of Environment)

For the use in the national environmental policy and nature

conservation a naturalness assessment of all the Czech forests was

performed using 30 criterions (direct human impact on stand

development: 17 criterions, indirect human impact on stand

development: three criterions, tree species composition: seven

criterions, deadwood: four criterions) (Anonymous 2011). 50 local

evaluators were instructed and trained and every evaluator was

responsible for one region. The forests in the protected areas were

evaluated preferentially. Data from national forest databank were

tested if some non-protected forest fulfill the basic criterions and if yes

it was evaluated in the field. From the total forested area of 2,568 000

ha about 30 000 ha of old-growth forests were identified on 490

localities in the range of 10-1200 ha per locality. The old-growth

forests were classified into three degrees of naturalness 1) 3%

primeval, 2) 28% natural and 3) 69% near-natural (Adam and Vrška

2009).

Actually every protected area has the management plan and al natural

forests are evaluated every ten years (if some new left to spontaneous

development etc.) and the Natural Forest Databank is updated every

year.

References:

Adam, D. & Vrška, T. (2009) Important localities of old-growth forests

Landscape Atlas of the Czech Republic (ed. by H.E. Al.), p. 209.

Ministry of Environment and Silva Tarouca Research Institute.

Anonymous (2011) Regulation No. 64/2011 on management plans,

marking and register of protected areas according the Nature and

Landscape Protection Act (No. 114/1992).

Statute book

pp. 645-675.

PUBLISHED - Data and Description

Data were selected from Natural Forests Databank of the Czech

Republic. according to criterions in the paper and adjusted. Main part

of dataset is available on-line -

http://naturalforests.cz/czech-natural-

forests-databank

Adam, D. & Vrška, T. (2009) Important localities of old-growth forests.

Hrnčiarová et al.(eds.). Landscape Atlas of the Czech Republic.

Ministry of Environment and Silva Tarouca Research Institute. 209 p,

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Intellectual property belongs Silva Tarouca Research Institute where

Tomas Vrska and Dusan Adam are the custodians

Tomas Vrska was supported by an institutional subsidy (VUKOZ-IP-

00027073)

Regime 1 - Restricted-access: Data are available for data contributors

only (i.e., custodians of other datasets) and their use depends on the

permission of the respective data custodian.

Appendix I

309

But data from Natural Forests Databank are free for public use:

http://naturalforests.cz/czech-natural-forests-databank

Dataset 7

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Erik Buchwald

Regime 3: Free-access: Data are available to a wider community of

users. These data can be made available on request to non-

members after submitting a request to the FORESTS and CO, with

no need for special approval.

Oldgrowth & long untouched forests of Denmark

Buchwald, Erik

[email protected]

Danish Nature Agency, Førstballevej 2, DK-7183 Randbøl

Oldgrowth & long untouched forests of Denmark

DATABASE

Denmark

COMPLETE INVENTORY

2016

To get a national overview of primary forests in Denmark using the

terminology, criteria and definitions proposed in Buchwald 2005.

Extract of Agency GIS and databases with all protected Danish

forests to get the forests meeting the relevant criteria.

UNPUBLISHED

Dataset 8

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Old-growth-Estonia

Francesco Maria Sabatini based on data provided by Ann Kraut

[email protected]

Humboldt-Universität zu Berlin, Department of Geography. Unter den

Linden 6, 10099, Berlin. Germany

Hemiboreal old-growth forests of Estonia

Map

Estonia

PARTIAL SURVEY

2010

To improve the silvicultural targets for ecologically sustainable

forestry, important structural features were quantified for the first time

in a representative set of old-growth forests in hemiboreal Europe.

Altogether, 23 old-growth stands of four site-type groups were

compared with mature commercial stands nearby in the Estonian

state forests that hold the Forest Stewardship Council (FSC)

310

Appendix I

certificate of sustainable forestry.

Methodological approach

The survey databases of the Estonian State Forest Management

Centre was used to pre-select old-growth stands based on their site

type, size (≥2 ha), and the estimated age of dominant canopy trees

(>120 years).

Publication Status

Publication Details

PUBLISHED - Only description

Lõhmus, A. & Kraut, A. (2010) Stand structure of hemiboreal old-

growth forests: Characteristic features, variation among site types,

and a comparison with FSC-certified mature stands in Estonia.

Forest Ecology and Management, 260, 155-165.

Ann Kraut & Asko Lõhmus

The research was supported by the Estonian Science Foundation

(grants 6457 and 7402), the Estonian Ministry of Education and

Science (project SF0180012s09) and the European Union through

the European Regional Development Fund (Centre of Excellence

FIBIR).

Regime 2 - Semi-restricted-access: Data are available for data

contributors only (i.e., custodians of other datasets) but without the

need for a specific permission in each single case.

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Dataset 9

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

BEECH_HVF_EU

Lombardi Fabio

[email protected]

Mediterranean University of Reggio Calabria, Department of Agraria,

Contrada Melissari, Loc. Feo di Vito, 89122, Reggio Calabria, Italiy

High Value Beech Forest in Europe

DATABASE

Large scale data (17 EU Countries)

Forest extension, coordinates, Main tree species, Living and

deadwood volumes, Years since last harvesting

2014

The data set was developed in a project aimed to verify if the HVF

(High Value Forests) across Europe overlaps with the occurring Old-

Growth Forests (see: Bastrup-Birk et al., 2014. Developing a forest

naturalness indicator for Europe Concept and methodology for a high

nature value (HNV) forest indicator. EEA Technical report No 13/2014,

Affiliation: European Environment Agency Technical, DOI:

10.13140/2.1.1726.3684)

As a first step, the beech-dominated forest area was identified

considering the tree species distribution maps (JRC, 2012a; Brus et

al., 2012) which report the percentage of each tree species for 1 km x

1 km pixel. The extraction of the beech forest information (pixels) was

based on data from NFIs published in Annex 1 of the last State of

Europe's Forests report (Barbati et al., 2011). The naturalness of

pixels dominated by beech forests delineated in the previous step was

assessed by multiple variables selected according to the availability of

the data sets. Five indicators were selected: naturalness of tree

species composition, hemeroby, growing stock volume, accessibility,

connectivity

Methodological approach

Appendix I

311

Publication Status

Publication Details

Report EEA

EEA (2014)

Developing a forest naturalness indicator for Europe.

Concept and methodology for a high nature value (HNV) forest

indicator.

EEA Technical report No 13/2014, Luxembourg:

Publications Office of the European Union.

Fabio Lombardi, Gherardo Chrici

Regime 2 - Semi-restricted-access: Data are available for data

contributors only (i.e., custodians of other datasets) but without the

need for a specific permission in each single case.

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Dataset 10

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

UNESCO - Primeval Beech Forests

Francesco Maria Sabatini based on data derived from UNESCO

[email protected]

Humboldt-Universität zu Berlin, Department of Geography. Unter den

Linden 6, 10099, Berlin. Germany

Primeval Beech Forests of the Carpathians and the Ancient Beech

Forests of Germany

MAP

Slovakia, Ukraine, Germany

Partial Survey

2007

The Primeval Beech Forests of the Carpathians and the Ancient Beech

Forests of Germany are a serial property comprising fifteen

components. They represent an outstanding example of

undisturbed, complex temperate forests and exhibit the most

complete and comprehensive ecological patterns and processes of

pure stands of European beech across a variety of environmental

conditions. They contain an invaluable genetic reservoir of beech and

many species associated and dependent on these forest habitats.

PUBLISHED - Data and Description

http://whc.unesco.org/en/list/1133

UNESCO/World Heritage Centre

reserved."

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Dataset 11

DATASET NAME

Name

Extension sites of World Heritage of Primeval Beech Forests of the

Carpathians and other Regions of Europe

Kirchmeir, Hanns

312

Appendix I

e-mail address

Affiliation and contacts

Title of the Dataset

[email protected]

E.C.O. Institute of Ecology, Lakesidepark B07b, 9020 Klagenfurt,

Austria

Shapefile with the proposed primeval and old growth beech forest

from 11 Countries proposed for extension of the existing WH of

primeval beech forests of the Carpathians and Ancient Beech forests

of Germany

MAP

The dataset contains forests from Spain, Italy, Belgium, Austria,

Slovenia, Croatia, Albania, Romania, Bulgaria, Ukraine and Poland

Only strictly protected sites nominated for World Heritage

2016

From 2012-2014 a screening Process of the most representative

primeval and old-growth beech forests was initiated by the German

BfN for an final extension of the existing UNESCO World heritage of

Primeval Beech forests of the Carpathians and Ancient Beech Forests

of Germany. Out of the list of more than 100 potential sites, 67

polygons in 33 "Clusters" (Protected Areas) have been selected for

the UNESCO WH nomination process in 2012-2016.

The screening Process was initiated by a German BfN funded project

from 2012-2014 with experts from all European Countries. Several

workshops on expert level where held in Germany, Italy and Ukraine.

The national experts added basic information on primeval or old

growth beech forests (including mixed beech forests in higher

altitudes). The information given for each site was name, size,

protection status, max. stand age, time of protection, name of

protected area (if given), altitude range. From these more than 100

potential sites the "best of the best" (large, strict protection status,

representative for different European Beech forest Regions) have

been selected through an iterative process. Those sites, selected for

nomination as UNESCO World Heritage have been delineated as

polygons be the national representatives and managers of protected

areas and are collected into this dataset.

UNPUBLISHED

The dataset has multiple authors.

Austrian federal ministry of agriculture, forestry, environment and

water management

Regime 1 - Restricted-access: Data are available for data contributors

only (i.e., custodians of other datasets) and their use depends on the

permission of the respective data custodian.

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Dataset 12

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

FinnishOGForests

Tikkanen, Olli-Pekka

[email protected]

School of Forest Sciences, University of Eastern Finland

Shapefiles of Natural Forests of Finland

DATABASE

Finnland

Appendix I

313

Region(s) covered

Data Completeness

Update status

Object of the dataset

PARTIAL SURVEY

2015

Geographical delineation of areas is based on nature conservation area

network of Finland, wilderness areas and those old growth forest

protection program sites not included in legal protection area network of

Finland. The GIS data is obtained from open data source of Finnish

Environment Institute (http://www.syke.fi/en-US/Open_information).

Original description of databases (metadata) is only in Finnish.

Conservation area network:

http://metatieto.ymparisto.fi:8080/geoportal/catalog/search/resource/det

ails.page?uuid={2627E9FE-B657-48E1-A98D-000D4CD5CA38}

Habitats conservation program sites:

http://metatieto.ymparisto.fi:8080/geoportal/catalog/search/resource/det

ails.page?uuid=%7BC305FA65-F319-4FA0-AAB8-F92AE32B6EE2%7D

Methodological approach

GIS data provided by Finnish Environment Institute contains metadata

about the area of natural forests. Area of natural forests in legal

conservation areas (size over 100 ha) was obtained from Natura 2000

Standard Data Form of the site (accessed by Natura 2000 viewer,

http://natura2000.eea.europa.eu).

Area of following Natura 2000 habitat types 9010 (western taiga), 9020

(Fennoscandian hemiboreal natural old broad-leaved deciduous forests

rich in epiphytes), 9040 (Nordic subalpine forests), 9080 (deciduous

swamp forests, 91D0 (bog woodland) and 91E0 (alluvial forests with

Alnus glutinosa and Fraxinus excelsior) were included in metadata of

this data set.

Large (size over 100 ha) Old Growth Forests Protection Program sites,

which are not legally protected and not included in Natura 2000 network,

were assumed to be predominantly natural forest and small forest areas

(10-99 ha) of Old Growth Forest Protection Programs (current legal

status not checked) were assumed to be solely natural forests. This

assumption is based on the metadata of the compiled data set.

UNPUBLISHED

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Regime 3: Free-access: Data are available to a wider community of

users. These data can be made available on request to non-members

after submitting a request to the FORESTS and CO, with no need for

special approval.

Dataset 13

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

WWF France

Vallauri, Daniel (compiled by Francesco Maria Sabatini)

[email protected]

WWF, 6 rue des Fabres, F-13001 Marseille - France

WWF - Hauts lieux de naturalité en France

MAP

314

Appendix I

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

France (Mediterranean Ecoregion)

Partial Survey

2012

The dataset synthesizes the results of a study that aimed to

characterise naturalness of Mediterranean forests. It was conducted

between 2010 and 2013, in the framework of WWF-France’s

programme on “Ancient Forests” (“Forêts anciennes”;

www.foretsanciennes.fr).

The report attempted to answer a number of

questions: Which method is adapted to analyse naturalness of forests

in the Mediterranean ecoregion? Is this methodology fundamentally

different to that applied in other regions, and if so, how? Is it possible to

develop a simple, reliable and rapid assessment methodology to guide

forest managers? What are the reference values for the main indicators

of naturalness and human footprint in ancient forests of the ecoregion?

How are these values different from those of other forests? Could this

gap inspire management practices?

The methodology was developed with the contribution of the

programme’s scientific and technical committee, as well as a long

process of exchanges, syntheses and tests. It is based on multicriteria

and applied at stand level. For more information, please read the guide

for practical implementation (Rossi & Vallauri, 2013). Thirty-three forests

were selected based on the literature and experts’ knowledge, as well as

using four main criteria (wherever possible): ancientness of forest cover,

nativeness of the

dominant tree species, maturity (old-growthness), and low human

footprint (last harvesting > 60 years). In these forests (figure 1), 52 stands

were fully evaluated (stand with an area of one hectare). The sample

covered all main

forest habitats of the 15 Mediterranean departments concerned, from

meso-Mediterranean to subalpine levels, and aimed to describe the

ecoregion’s hotspots.

In the context of Forests and Co, only those stands meeting the definition

criteria of primary forest (either n6- old-growth forests, or n5-long

untouched forests) were selected.

Methodological approach

Publication Status

Publication Details

PUBLISHED - Data and Description

http://www.foretsanciennes.fr/proteger-mieux/france/

Rossi, M., Bardin, P., Cateau, E. & Vallauri, D. (2013) Forêts anciennes

de Méditerrané e et des montagnes limitrophes: références pour la

naturalité régionale. WWF France, Marseille, France, 144.

Fonds Mondial pour la Nature France (WWF France)

Ownership of the dataset

Funding

acknowledgment

Data Accessibility

Dataset 14

DATASET NAME

Name

e-mail address

FR-RNF

DEBAIVE Nicolas

[email protected]

Appendix I

315

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Réserves naturelles de France, La Bourdonnerie, 2 allée Pierre

Lacroute - CS 67524 – 21075 DIJON cedex

Database describing the old-growth forests within the nature reserve

network in France

DATABASE

France

PARTIAL SURVEY

2013

The objectives of the study were various: (i) update the existing

database of OGF that were known to be protected by nature reserves

(ii) identify the reasons for their evolution, (iii) highlight the role of

nature reserves in the preservation of OGF and (iv) characterize these

OGF on a descriptive level.

The terminology we used for OGF was "Forêts à caractère naturel -

FCN". A natural forest or part of a forest may be identified on the

nature reserve if : (i) the last harvesting operation occurred more than

50 years ago, (ii) an important compartment of old trees and large-

diameter dead wood (> 40 m3 / ha in productive forests and/or a

'deadwood volume'/'live wood volume' ratio> 10%) is found in this

forest, (iii) the stands are characterized by a high forest structure (i.e. :

not a coppice forest) and (iv) the tree cover is predominantly

represented by indigenous species (of local genetic strains).

Data were collected using a questionnaire. A forest could be identified

if the 4 criteria mentioned above were filled in and validated. Note that

some of the criteria could have been filled using estimated data

(versus calculated data).

UNPUBLISHED

Réserves naturelles de France

Regime 2 - Semi-restricted-access: Data are available for data

contributors only (i.e., custodians of other datasets) but without the

need for a specific permission in each single case.

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Dataset 15

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

STRICD

Meyer Peter

[email protected]

Northwest German Forest Research Institute

Old strict forest reserves Germany

DATABASE

Germany

PARTIAL SURVEY

2016

The objective was to identify all forest stands in Germany which

meet the criteria for long untouched forest (n5, s. Buchwald 2005: set

316

Appendix I

aside from active (forestry) management since at least 60 years).

Methodological approach

All representatives of forestry research organizations, which are

members of the working group "Naturwälder", were asked to supply

the required information including coordinates of the forest stands.

UNPUBLISHED

The dataset belongs to several third parties, i.e. the responsible

institutions of the German countries. These are: Forstliche Versuchs-

und Forschungsanstalt Baden-Württemberg, Bayerische

Landesanstalt für Wald und Forstwirtschaft, Ministerium für

Infrastruktur und Landwirtschaft des Landes Brandenburg,

Nordwestdeutsche Forstliche Versuchsanstalt, Landesforst

Mecklenburg-Vorpommern, BT Forstplanung, Versuchswesen,

Informationssysteme, Landesbetrieb Wald und Holz Nordrhein-

Westfalen, Zentralstelle der Forstwirtschaft, Forschungsanstalt für

Waldökologie und Forstwirtschaft, Rheinland-Pfalz

Regime 2 - Semi-restricted-access: Data are available for data

contributors only (i.e., custodians of other datasets) but without the

need for a specific permission in each single case.

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Dataset 16

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

GREEK DATASET

Grigoriadis Nikolaos

[email protected]

Forest Research Institute Thessaloniki Vassilika 57006 Greece

Oldgrowth Forests In North Greece

DATABASE

Dataset of forest service in region of Macedonia (west, central & east)

PARTIAL SURVEY

2012

The main objective of the sub-project is to select data of old growth

forests in Greece and provide sufficient support to FORESTS and CO

project in accessing the data to include it in the map of Europe's

primary forests. The main criteria for selection of the dataset were:

location set aside area / not managed forests and age stand.

To construct the dataset, different sources were used; mainly the

forest plans of the local Forest Services, databases of the protected

sites by the Local Management Authorities and own data (Forest

Research Institute of Thessaloniki).

UNPUBLISHED

N/A

Grigoriadis Nikolaos, Fotiadis Georgios

Regime 1 - Restricted-access: Data are available for data contributors

only (i.e., custodians of other datasets) and their use depends on the

permission of the respective data custodian.

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Appendix I

317

Dataset 17

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Primary forest stands of Hungary

Horváth, Ferenc

[email protected]

MTA Centre for Ecological Research, Institute of Ecology and Botany,

Alkotmány u. 2-4, 2163 Vácrátót, Hungary

Best known old-growth and long untouched forest stands of the strict

forest reserves of Hungary

MAP

Hungary

PARTIAL SURVEY

2016

1. The objectives are to select and delineate well-known old-growth

and long untouched stands of strictly protected, unmanaged core

areas

Strict

Forest

Reserves

Hungary.

2. The criteria of Buchwald (2005) was applied.

Strict Forest Reserves of Hungary were selected and evaluated by an

Interministerial National Committee for Forest Reserves based on

extensive surveys of candidate sites in the 90ies. The proposed forest

reserves based on the 2nd national extensive survey in 1998/1999

were evaluated and classified (Horváth and Bölöni 2002) according to

the forest research strategy (Standovár 2002) in accordance with the

main recommendations of the COST Action E4 - Forest Reserves

Research Network (Parviainen et al. 2000). Than a re-evaluation and

re-classification of 'old-growth' characteristics of core areas and

stands was performed based on all the available reports of the Forest

Reserve Programme Archive. The delineation of strictly protected

designated core areas were considered but modified in the case of

Kékes, Vár-hegy, Őserdő, Szalafő and Vétyem to narrow the shapes

that appropriately meet the criteria for old-growth and/or long

untouched stands. World Imagery (ESRI) was also applied for visual

checking of boundaries.

UNPUBLISHED

Evaluation was based on the following references and websites:

Manuscripts

and

Documents

http://www.erdorezervatum.hu/en/ERA_Documents

Archive,

Methodological approach

Publication Status

Publication Details

Horváth, F., Bidló, A., Heil, B., Király, G., Kovács, G., Mányoki, G.,

Mázsa, K., Tanács, E., Veperdi, G. & Bölöni, J. (2012) Abandonment

status and long-term monitoring of strict forest reserves in the

Pannonian biogeographical region.

Plant Biosystems - An

International Journal Dealing with all Aspects of Plant Biology,

146,

189-200.

Horváth, F. & Bölöni, J. (2002) Az erdőrezervátumok

kutatásszempontú besorolása és rövid jellemzése 1999-ben

(Classification and brief description of forest reserves in 1999 from the

viewpoint of research perspective).

A hazai erdőrezervátum-kutatás

célja, stratégiája és módszerei (Aims, strategy and methods of forest

reserve research in Hungary)

(ed. by F. Horváth and B. A), pp. 276-

287, TermészetBÚVÁR Kiadó, Budapest.

Standovár, T. (2002) Az erdőrezervátum-kutatás stratégiája és

318

Appendix I

módszertana (Strategy and methodology of forest reserve research).

A hazai erdőrezervátum-kutatás célja, stratégiája és módszerei (Aims,

strategy and methods of forest reserve research in Hungary)

(ed. by F.

Horváth and A. Borhidi), pp. 88-99, TermészetBÚVÁR Kiadó,

Budapest.

Juhdöglő-völgy

Forest

Reserve

http://www.erdorezervatum.hu/en/Juhdoglo-volgy_FR

(ER-07),

Kunpeszéri

Tilos-erdő

Forest

Reserve

(ER-15),

http://www.erdorezervatum.hu/en/Kunpeszeri_Tilos-erdo_FR

Vétyem

Forest

Reserve

http://www.erdorezervatum.hu/en/Vetyem_FR

Szalafő

Forest

Reserve

http://www.erdorezervatum.hu/en/Szalafo_FR

Kékes

Forest

Reserve

http://www.erdorezervatum.hu/en/Kekes_FR

Vár-hegy

Forest

Reserve

http://www.erdorezervatum.hu/en/Var-hegy_FR

Őserdő

Forest

Reserve

http://www.erdorezervatum.hu/en/Oserdo_FR

Alsó-hegy

Forest

Reserve

http://www.erdorezervatum.hu/en/Also-hegy_FR

Horváth, Ferenc

(ER-36),

(ER-53),

(ER-56),

(ER-59),

(ER-60),

(ER-68),

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Regime 1 - Restricted-access: Data are available for data contributors

only (i.e., custodians of other datasets) and their use depends on the

permission of the respective data custodian.

Dataset 18

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Region(s) covered

Data Completeness

Update status

Object of the dataset

ItalyNP

Burrascano, Sabina

[email protected]

Sapienza University of Rome, Dept. of Environmental Biology, P.le

Aldo Moro 5 00185 Rome Italy, +390649912845

ForesteVetuste

MAP

Italian National Parks (23 Parks as in 2008)

COMPLETE INVENTORY

2008

The definition of old-growth developed during the project is: "Forests

in which human disturbance is absent or negligible, and in which

natural dynamics create a mosaic of all the forest regeneration

phases, including the senescing one. Such a phase is characterized

by large old trees, deadwood (snags logs and coarse woody debris)

and a vascular plant species composition that is consistent with the

Appendix I

319

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

biogeographical context and includes highly specialized taxa related

to the small-scale disturbance and the microhabitats resulting from

structural heterogeneity"

"The dataset was based on a questionnaire sent to the staff of

National Parks and of State Forestry Corps aimed at gathering

information on the structure and management history of stands

potentially displaying attributes associated to old-growth forests.

Among the stands that were identified by the questionnaires a

selection was performed based on field surveys carried out by forest

scientists and botanists. Stands were divided into three different

classes of old-growth based on: i) diameter distribution of living trees,

ii) amount of deadwood (volume), iii) quality of deadwood

(decomposition classes).

From 0 to 4 points were assigned to each stand. The score depended

on how closely the living structure fitted the diameter correspondence

curves usually associated to old-growth stands, i.e. the reverse J-

shape curve, which attests to the presence of numerous young trees

and a decreasing number of trees with a larger DBH, and the rotated-

sigmoid curve, which is the semilog graph of the tree number per

DBH class (see Lorimer & Frelich 1984). Another point was assigned

on the basis of the number of large trees (DBH > 40 cm) per hectare.

Every site with more than 70 large trees per hectare gained a point.

This threshold was defined following Nilsson et al. (2002). A further

four points were assigned for the amount and the quality of

deadwood, two points being assigned for amount of deadwood per

hectare and two points for the quality, the latter being determined on

the basis of the number of decomposition classes and maximum

class observed. The stands scores thus ranged from a minimum of 0

to a maximum of 9 points. Each stand was then assigned to one of

three old-growth levels: low, medium or high.

PUBLISHED - Only description

Blasi, C., Burrascano, S., Maturani, A. & Sabatini, F.M. (2010)

Old-

growth forests in Italy.

Palombi Editori, Rome, Italy.

The dataset belongs to the Ministry for the Environment, Land and

Sea Protection and to the Interuniversity Research Center

“Biodiversity, Plant Sociology and Landscape Ecology” Sapienza

University of Rome. Sabina Burrascano represents the latter

organization as a custodian.

"Le foreste vetuste nei Parchi Nazionali d'Italia. Caratterizzazione

floristica, vegetazionale, strutturale e linee guida per la conservazione

e la gestione", Ministry for the Environment, Land and Sea Protection

Regime 2 - Semi-restricted-access: Data are available for data

contributors only (i.e., custodians of other datasets) but without the

need for a specific permission in each single case.

Funding acknowledgment

Data Accessibility

Dataset 19

DATASET NAME

Name

e-mail address

Affiliation and contacts

Database of natural forests in Lithuania

Mozgeris Gintautas

[email protected]

Institute

Forest

Management

and

Wood

Science

Aleksandras Stulginskis University Studentų 11, LT-53361

Akademija, Kaunas reg., Lithuania

320

Appendix I

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

File geodatabase describing the location and key attributes of natural

forests in Lithuania

MAP

Lithuania

COMPLETE INVENTORY

2016

The database was compiled following “Technical guidelines for

Contributing Data to WP1” (ver. 1.0), thus, the objectives, definitions

and criteria follow the ones used in the guidelines. However, the

definition of the level of naturalness may differ: i) as a n7 we

identified large areas with old growth stands with no intervention

more than 60 years; ii) n6 we used for areas which are formally

established to protect “old growth forest”; iii) n5 – forests with long

period without intervention. Even some areas were legally

established several decades or less ago, but practically they were

out of forestry activities since 1950-1960s.

The database was compiled in 2016 and is based on geographic

information available from Lithuanian state forest cadaster. The state

forest cadaster is based on data from stand-wise forest inventories.

The state forest cadaster is updated according to occurring changes,

but at least once in 10-year period. Permanent monitoring of forest

stands is supposed to be initiated in the nearest future. First of all,

the borders of all forest stands with potential no intervention status

were selected in the state forest cadaster database and copied to

the ArcGIS (ver. 10.3) file geodatabase. State forest cadaster

contains some information on protection status for all forest stands in

Lithuania. Forest stand polygons belonging to the same tract were

merged and their attribute structure was created following the

technical guidelines. Then two experts (prof. A.Augustaitis and

prof.V.Marozas from Aleksandras Stulginskis university) with

experience in forest ecology were consulted and all polygons one-

by-one were checked whether they could fit at least some level of

naturalness. Next, some areas were removed where no intervention

is legally prescribed due to some other reasons, like the protection of

historical sites, recreational objects, etc. Level of naturalness was

assigned to each polygon in the database. Two additional areas in

Curronian Spit with pine stands 150-200 years old and no

intervention due to specific location but without formal requirement

for no-management were included based on recommendations of

both experts. Then stand level attributes were summarized to

estimate the prevailing tree species. Within the database they

appear in a decreasing order according to their coverage. As the 3rd

species we added some specific tree species for which the area

could be established – e.g. we have one long untouched forest with

Quercus petrea etc. even there could be some other more abundant

tree species. As majority of areas are under administration of some

protected areas, the corresponding websites, legal acts and other

documents were studied to provide descriptions required.

UNPUBLISHED

Information available from Lithuanian State forest cadaster was used

to get the boundaries and attributes of forest stands. This information

is used on the base of license agreement between Aleksandras

Stulginskis university and State Forest Service.

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Appendix I

321

Data Accessibility

Regime 3: Free-access: Data are available to a wider community of

users. These data can be made available on request to non-

members after submitting a request to the FORESTS and CO, with

no need for special approval.

Dataset 20

DATASET NAME

Name

e-mail address

Affiliation and contacts

PriMaFor

Simovski, Bojan

[email protected]

Ss. Cyril and Methodius University in Skopje, Faculty of Forestry in

Skopje, Dept. of Botany and Dendrology, P. O. Box 235, MK-1000

Skopje, Republic of Macedonia

Shapefile of distribution, area and species composition of certain forest

communities (primary forests) in the Mavrovo National Park, Republic of

Macedonia

DATABASE

Mavrovo National Park, Republic of Macedonia

Almost complete inventory (some remote and/or poorly accessible areas

missing)

2014

1. To gather relevant data and map certain forest communities with virgin

forest characteristics in Macedonia - in particular protected areas (NP

Mavrovo); 2. The custodian used the criteria proposed by the FOREST

and CO team - the definition framework defined in Buchwald et al. (2005).

In addition, the custodian's research is also based on 'forests with virgin

forest characteristics' (Simovski, B., 2015: Natural succession processes

/stand dynamics/ of the forests in the National Park Mavrovo, PhD thesis,

Ss. Cyril and Methodius University in Skopje, Faculty of Forestry in

Skopje /in Macedonian/).

Before the start of field research, appropriate existing literature was used,

relating to previous research related to the Mavrovo National Park and it

is listed in the References section. For conducting field surveys were

used topographic maps M = 1:25000 and other printed maps, satellite

and aero-photo imagery (LANDSAT), GPS measurements and more, in

the most part provided by forestry-geomatic laboratory techniques in

QGIS (GIS Lab) at Ss. Cyril and Methodius University in Skopje, Faculty

of Forestry in Skopje. Each field activity was followed by digital

photography; in fact all the dendro-phytosociological data are recorded

with appropriate photographs. Finally, these forest communities’ maps

were digitized, i.e. geo-referenced in the suitable GIS software.

There is no publication of the data concerning these type of forest

communities (as far as custodian concern, only in PhD and MSc thesis of

the custodian, mentioned in reports and briefly in two original scientific

articles)

Reference

list:

Simovski, B. (2015)

Natural succession processes (stand dynamics) of

the forests in the National Park Mavrovo. PhD thesis.

Ss. Cyril and

Methodius University in Skopje, Faculty of Forestry in Skopje. (in

Macedonian)

Simovski, B. & Acevski, J. (2012) The Fir as a Destructor of the Forest

Communities in the Republic of Macedonia.

Kastamonu University

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Methodological

approach

Publication Status

Publication Details

322

Appendix I

Journal of Forestry Faculty,

12,

105-113.

Acevski, J. & Simovski, B. (2012) Forest associations of the National

Park Mavrovo in the Republic of Macedonia.

Proceedings of the

International Conference Integrated Management of Environmental

Resources-Suceava, November,

pp. 17-27.

Acevski, J. & Simovski, B. (2011) Study on revaluation of natural values

of the protected area Mavrovo.

Oxfam Italia (Project: Protection of the

environment, economic development and promotion of sustainable eco-

tourism in the National Park Mavrovo, in Macedonian),

249-264.

Simovski, B. (2011)

Dendrofloristic characteristics of the National Park

Mavrovo. MSc thesis.

Ss. Cyril and Methodius University in Skopje,

Faculty of Forestry in Skopje. (in Macedonian)

Asst. Prof. Dr. Bojan Simovski, Ss. Cyril and Methodius University in

Skopje, Faculty of Forestry in Skopje, Dept. of Botany and Dendrology

(collaborators: Prof. Dr. Jane Acevski, Asst. Prof. Dr. Ivan Minchev)

Some research was previously supported as a part of the project by Unità

e Cooperazione per lo Sviluppo dei Popoli UCODEP (Oxfam Italia):

Protection of the environment, economic development and promotion of

sustainable eco-tourism in the National Park Mavrovo (2008-2011, N.

AID: 8740/Ucodep/MCD)

Regime 3: Free-access: Data are available to a wider community of

users. These data can be made available on request to non-members

after submitting a request to the FORESTS and CO, with no need for

special approval.

Ownership of the

dataset

Funding

acknowledgment

Data Accessibility

Dataset 21

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Norwegian dataset

Midteng, Rein

[email protected]

Asplan Viak AS, Analyse og utredning, Postboks 24, N-1300 Sandvika

WWF-priority_unike_skoger_final.shp

MAP

Norway

PARTIAL SURVEY

2016

1. The dataset consist of the 50 most unique not-protected (by 2015)

old-growth forests. 2. Old-growth forests. Not necessary primeval

forests, but all natural and biological valuable forests.

All forests are surveyed in the field by biologists and reports of the

areas exist in Norwegian

PUBLISHED - Data and Description

Skogkur 2020. Redningsplan for Norges unike skoger

Rein Midteng and WWF Norway

Regime 3: Free-access: Data are available to a wider community of

users. These data can be made available on request to non-members

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Appendix I

323

after submitting a request to the FORESTS and CO, with no need for

special approval.

Dataset 22

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

OGPOL

Szwagrzyk, Jerzy

[email protected]

Institute of Forest Ecology and Silviculture, University of Agriculture,

Krakow, al. 29 listopada 46, 31-425 Krakow

Database of old-growth forests of Poland

DATABASE

Poland

COMPLETE INVENTORY

2000

The database was compiled first as a part of the project ERBC -

Carpathian Ecoregion Initiative - Phase II, Biodiversity Working Group,

Sector: Virgin Forests Sector Coordinator: Dr. Ioan V. Abrudan.

The definition used within the project for virgin forest: forest which

shows natural forest dynamics, such as natural tree composition,

occurrence of dead wood, a natural (un-even) age structure and

natural regeneration processes. Furthermore, it comprises an area

large enough to maintain its natural characteristics and shows no

signs of any human intervention to affect natural processes and

dynamics.

In Case of Poland, all the old-growth stands are located either in forest

reserves, or in national parks

The dataset was compiled using data from the literature (including

unpublished manuscripts) as well as some data collected by the

author and his co-workers

UNPUBLISHED

References:

Breymeyer, A., Denisiuk, Z., Dmowski, K., Dobrowolski, K., Goczoł-

Gontarek, M., Gontarek, M., Jędraszko-Dąbrowska, D., Kot, M.,

Krzan, Z., Okołów, C., Parusel, J., Piotrowska, H., Siarzewski, W.,

Skawiński, P., Stoyko, S., Terry, J. & Wojterski, T. (1994) Biosphere

Reserves in Poland. In: (ed. P.N.M. Committee), Warszawa.

Czubiński, Z., Gawłowska, J. & Zabierowski, K. (1977) Nature

reserves in Poland (in Polish, with English summary).

Studia Naturae,

27,

528 pp.

Dziewolski, J. (1987) The succession of the selected tree stands in the

Babia Góra National Park (in Polish, with English summary).

Ochrona

Przyrody,

45,

76-129.

Dziewolski, J. (1990) Changes in the structure of the forest stands of

the “Nad Kotelniczym Potokiem” nature reserve in the Beskid Sądecki

Mts. in the years 1969-1982 (in Polish, with English summary).

Ochrona Przyrody,

47,

189-200.

Dziewolski, J. (1991) Direction of changes in forest stands in national

Methodological approach

Publication Status

Publication Details

324

Appendix I

parks of Polish Carpathians under strict and partial protection (in

Polish, with English summary).

Prądnik, Prace Muz. Szafera,

9-26.

Holeksa, J. (1986) The sylvan complex at the western border of the

Polish Carpathian Mts. deserves protection (in Polish, with English

summary).

Chrońmy

Przyrodę

Ojczystą,

47,

5-16.

Holeksa, J., Karczmarski, J., Wilczek, Z. & Ciapała, S. (1996) The

Romanka reserve in the Beskid Żywiecki Mountains as an example of

inappropriate forest ecosystem protection (in Polish, with English

summary).

Ochrona

Przyrody,

53,

19-35.

Jaworski, A. (1997) Carpathian forests of primeval character and their

importance in shaping the pro-ecological model of forest management

in mountains. Sylwan, 141, 33-49.

Jaworski, A. & Karczmarski, J. (1990) Constitution and structure of

lower mountain forest zone stands of primeval character in the

National Park of Mt. Babia Góra.

Acta Agraria et Silvestria. Series

Silvestris,

29,

49-64.

Jaworski, A. & Karczmarski, J. (1991) Structure and dynamics of

virgin-type stands in the Pieniny National Park (Exemplified by 4

experimental plots) (in Polish, with English summary).

Zeszyty

Naukowe

Kraków.

Seria

Leśnictwo,

20,

45-83.

Jaworski, A., Karczmarski, J. & Skrzyszewski, J. (1993) Constitution

and structure of common linden forest in the Obrożyska reserve

Acta

Agraria et Silvestria. Series Silvestris,

31,

57-79.

Jaworski, A., Karczmarski, J. & Skrzyszewski, J. (1994) Dynamics and

structure of forest stands in the reserve „Łabowiec”.

Acta Agraria et

Silvestria. Series Silvestris,

32,

3-26.

Jaworski, A., Kołodziej, Z. & Skoczeń, W. (2000) Composition and

structure of upper-mountain spruce forests on the Pilsko crest (in

Polish,

with

English

summary).

Sylwan,

144,

35-53.

Jaworski, A., Pach, M. & Skrzyszewski, J. (1995) Structure of stands

with beech and sycamore maple in the Moczarne forest complex and

below Mount Rabia Skała (Bieszczady Mts).

Acta Agraria et Silvestria.

Series Silvestris,

33,

39-73.

Jaworski, A. & Skrzyszewski, J. (1995) Structure and dynamics of

stands of virgin character in the lower montane forest zone of the

Łopuszna reserve.

Acta Agraria et Silvestria. Series Silvestris,

33,

37.

Jaworski, A., Skrzyszewski, J., Świątkowski, W. & Karczmarski, J.

(1991) Structure of lower montane zone virgin type stands in selected

areas of the Bieszczady Mts. ) (in Polish, with English summary).

Zeszyty Naukowe AR Kraków. Seria Leśnictwo,

20,

17-43.

Karczmarski, J. (1995) Structure of spruce stands of virgin character in

the upper montane forest zone in Tatra valleys of Rybi Potok,

Pańszczyca and Gąsienicowa.

Acta Agraria et Silvestria. Series

Silvestris,

33,

167-198.

Michalik, S. (1967) The „Turbacz” nature reserve in the Gorce Mts.

Appendix I

325

(Polish Western Carpathians) (in Polish, with English summary).

Studia Naturae,

24,

93 pp..

Michalik, S. & Kurzyński, J. (1990) The vegetation of the Puszcza

Bieszczadzka nad Sanem (Bieszczady Primeval Forest on the San

River) Forest Reserve (in Polish, with English summary).

Ochrona

Przyrody,

47,

93-109.

Myczkowski, S. (1967) A project for the network of strict nature

reserves in the Tatra National Park (in Polish, with English summary).

Ochrona Przyrody,

32,

41-88.

Przybylska, K. & Kucharzyk, S. (1999) Species composition and

structure of forests of the Bieszczady National Park (in Polish, with

English summary).

Monografie Bieszczadzkie,

159 pp.

Staszkiewicz, J. (2000) Nature reserves (in Polish).

The Nature of the

Popradzki Landscape Park

(ed. by J. Staszkiewicz), pp. 273-295.

Popradzki Park Krajobrazowy, Stary Sącz.

Szwagrzyk, J., Szewczyk, J. & Bodziarczyk, J. (1995) Structure of

forest stand in the Zarnowka reserve of the Babia Gora National Park.

Folia Forestalia Polonica. Series A. Forestry,

37,

111-123.

Szwagrzyk, J., Szewczyk, J., Hutka, D. & Zielonka, T. (1996) The age

structure of the natural spruce forest in the Tatra National Park (in

Polish, with English summary).

Przyroda Tatrzańskiego Parku

Narodowego a Człowiek) PTPNoZ i TPN,

(ed. by A. Kownacki), pp.

60-62, Kraków, Zakopane.

Ownership of the dataset

Funding acknowledgment

Jerzy Szwagrzyk

ERBC - Carpathian Ecoregion Initiative - Phase II Biodiversity Working

Group Sector: Virgin Forests Sector Coordinator: Dr. Ioan V. Abrudan.

The project was funded by WWF.

Regime 3: Free-access: Data are available to a wider community of

users. These data can be made available on request to non-members

after submitting a request to the FORESTS and CO, with no need for

special approval.

Data Accessibility

Dataset 23

DATASET NAME

Name

e-mail address

Affiliation and contacts

OldForestsPT

Duarte, Inês

[email protected]

Centre for Applied Ecology “Professor Baeta Neves” (CEABN), InBio,

School of Agriculture, University of Lisbon, Tapada da Ajuda 1349-

017, Lisbon, Portugal.

Shapefile describing the distribution of old growth forests in Mainland

Portugal

DATABASE

Mainland Portugal

PARTIAL SURVEY, it was possible to collect the present old growth

forest areas, but we believe that are more old forests existing in

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

326

Appendix I

Portugal

Update status

Object of the dataset

2016

Portugal, as all mediterranean countries, has a long history of land use

changes due to important climatic variations, including glaciation

periods, and to increased human influence through - firewood

collection, grazing with domestic animals, fire, and the development of

agriculture since Neolithic times (Pasquale et al., 2004). Changes

have been accelerating in the last century due to increased land

abandonment related to population migration to urban areas. Long

untouched forests still remain in Mainland Portugal, but in residual

specific spots, like inaccessible areas. The objective of this project

was to identify, as much as possible, areas of autochthonous long-

untouched forest in Mainland Portugal. We used the terminology and

references provided by the coordinator of the Forest &CO Project.

We identified autochthonous forests which corresponded to the

naturalness level classification of Buchwald (2005) n5 - Long

Untouched Forest; n6 - Old-growth Forest.

References

Pasquale, G.D., Martino, P.D. & Mazzoleni, S. (2004) Forest history in

the Mediterranean region.

Recent dynamics of the Mediterranean

vegetation and landscape,

13-20.

After analyzing briefly the National Forest Inventory, where it was

possible to identity the oldest forests, we noted it was not possible to

achieve the objective without the information of management.

We created a questionnaire and passed it on to National forest

services, Forest owners associations and recognized botanists in the

whole of Portugal. The questionnaire collected information about

forest areas with more than 60/80 years: Dominant species; location

(point or area); mean age of the stand; management and additional

information.

This information was also complemented with interviews to experts in

botany with long experience in field. We joint also two areas, known as

protected

by National

legislation

primeval reserves.

From a joint effort it was possible to collect information from National

Forest Services, Forest Owners and Experts, since detailed

descriptions with (observation in situ), GoogleEarth® delimitation; to

simple points where the oldest forests with higher naturalness levels

are present.

The classification of naturalness level by Buchwald (2005) was applied

by our team for each case, following the descriptions received.

UNPUBLISHED

In the data collection, we informed the collaborators about the

objectives of FOREST&CO project, requesting contributions for a

European database of the primeval forest. All the collected data were

provided without any request for restrict access. CEABN, Centro de

Ecologia Aplicada Prof. Baeta Neves, will be the keeper of the data,

providing it to the use in the project Forest & Co.

Regime 2 - Semi-restricted-access: Data are available for data

contributors only (i.e., custodians of other datasets) but without the

need for a specific permission in each single case.

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Appendix I

327

Dataset 24

DATASET NAME

Name

e-mail address

Affiliation and contacts

Laurissilva_PT_forest

Nunes, Leónia

[email protected]

Centre for Applied Ecology “Professor Baeta Neves” (CEABN), InBio,

School of Agriculture, University of Lisbon, Tapada da Ajuda 1349-

017, Lisbon, Portugal

Shapefile describing the distribution of Laurissilva frontier forest in the

Portuguese Macaronesian region.

DATABASE

Madeira Island, Macaronesian Region

COMPLETE INVENTORY

2015

Laurissilva subtropical forests represents a pristine forest that

constitutes the world’s largest remaining area of the Tertiary forests

that covered most of the south of Europe and the North of Africa

(Capelo, 2004; Menezes et al., 2007). This project aims to identify the

Laurissilva primary forest in Madeira Island, within the Macaronesian

region. The terminology used to classify this type of forest follows the

one given by the coordinator of the project FOREST & CO, taking into

account the Naturalness level according to Buchwald (2005). This

forest was classified as n8 - Frontier Forest.

Aguiar, C., Capelo, J., Costa, J.C., Fontinha, S., Espírito-Santo, D.,

Jardim, R., Lousã, M., Rivas-Martínez, S., Mesquita, S. & Sequeira,

M. (2004) A paisagem vegetal da Ilha da Madeira.

Quercetea,

200.

Menezes, D., Freitas, I., Gouveia, L., Mateus, G., Domingues, M.,

Oliveira, P. & Fontinha, S. (2004) A Floresta Laurissilva da Madeira,

Património Mundial.

Parque Natural da Madeira. Madeira,

30.

Buchwald, E. (2005) A hierarchical terminology for more or less

natural forests in relation to sustainable management and biodiversity

conservation.

Proceedings: Third expert meeting on harmonizing

forest-related definitions for use by various stakeholders

Food and

Agriculture Organization of the United Nations, Rome, 17-19 January

2005.

The first steps were to identify the Laurissilva forests in Macaronesia

region and analyse those that belongs to Portugal. This type of forest

occurs in Portugal only in the biogeographic region of Macaronesia

located in archipelagos of Madeira and Azores. The management of

these forests are under the ownership of the Regional Forest

Services. The Forest Services of Madeira provided us information to

identify the primary forests in the Laurissilva based on data from the

Madeira Forest Inventory, IFRAM2 – 2015 (DRFCN, 2015). The

Naturalness level of this forest was identified according to the

classification of Buchwald (2005), taking into account the information

we have from the experts of this forest. The data were organized in a

database and in a map with the structural attributes X and Y, based in

the original a raster format provided by the Forest services.

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Methodological approach

328

Appendix I

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

DRFCN (2015). IFRAM2 - 2º Inventário Florestal da Regiao Autónoma

da Madeira. Secretaria Regional do Ambiente e dos Recursos

Naturais, Funchal. 114 p.

There is a published report about the Madeira Forest Inventory,

IFRAM2 – 2015 (DRFCN, 2015) with information related to the

fieldwork and the main evaluation results of forest areas, like volume

and biomass. The information regarding the Laurissilva forest is also

in this report but has only the main results of the forest data. The raw

data is not published nor even publicly available and the Regional

Forest Services of Madeira have the ownership of the raw data.

Reference: DRFCN (2015). IFRAM2 - 2º Inventário Florestal da

Regiao Autónoma da Madeira. Secretaria Regional do Ambiente e dos

Recursos Naturais, Funchal. 114 p.

DRFCN (2015). IFRAM2 - 2º Inventário Florestal da Regiao Autónoma

da Madeira. Secretaria Regional do Ambiente e dos Recursos

Naturais, Funchal. 114 p.

The Regional Forest Services of Madeira made available to

CEABN/InBIO the Madeira Forest Inventory data (IFRAM2 – 2015) to

be used in the project FOREST & CO in order to contribute to these

European primary forests database and to develop a map of known

primary forest. The CEABN/InBIO can use the data in the context of

this project.

None.

Regime 2 - Semi-restricted-access: Data are available for data

contributors only (i.e., custodians of other datasets) but without the

need for a specific permission in each single case.

Dataset 25

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage - Region(s)

covered

Data Completeness

Update status

Object of the dataset

Radu Nicolae Melu

[email protected]

WWF DCP RO Organization, forest department, Str. Dumitru Zosima,

no.39 Bucuresti

Shapefile describing the old growth forests identified until now in

Romania

DATABASE

Country -Romania ( Brasov, Covasna, Sibiu, Caras-Severin, Gorj,

Meheninti Maramures Counties)

PARTIAL SURVEY

2016

The forest objectives - Support Responsible Forest Management for a

Sustainable Development. The criteria used is here:

http://www.carpathianconvention.org/tl_files/carpathiancon/Downloads/

03%20Meetings%20and%20Events/COP/2014_COP4_Mikulov/Follow

%20Up/DOC13_Criteria_Indicators_virginforests_FINAL_26SEP.pdf

These are the criteria adopted by the Carpathian Convention and in the

same form, is adopted in Minister Order 3397/2012 from Romania

Forests subject to identification were identified in a previous project in

2005

(http://www.mmediu.ro/app/webroot/uploads/files/2015-12-

22_Virgin_forest_Romania_Summary.PDF) as virgin forests. Now their

condition was largely degraded and should be evaluated according to

Methodological approach

Appendix I

329

criteria of the Carpathian Convention and Romanian legislation.

The identification process is divided into 2 steps:

1 GIS identification – analysis of data from forest administration and

satellite images (accessibilities, the age of the forests etc.). In this

phase all the forest which do not meet the criteria were eliminated.

2 Field identification - where the forest specialists evaluate the forest

qualified in first stage in field. The forests which meet the criteria in the

field will be promoted, as virgin / quasi-virgin forests.

Only geographical data

http://www.lemncontrolat.ro/ro/prima-pagina/

Database belongs to the association WWF

Regime 2 - Semi-restricted-access: Data are available for data

contributors only (i.e., custodians of other datasets) but without the

need for a specific permission in each single case.

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Dataset 26

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Prales Database

Vysoky, Juraj (compiled by Francesco Maria Sabatini)

[email protected]

PRALES, Komenskeho 21, 974 01 Banska Bystrica, Slovakia

Old Growth Forests in Slovakia

MAP

Slovakia

Complete Inventory

2015

Data are the result of a comprehensive inventory focused on the

identification of primary forests (n10-n6 interval in Buchwald’s

scheme) in Slovakia. Inventory was done under 2 projects. First

project "Protection of primary forest of Slovakia" was done during

2009-2010 and was focused on identification of primary forests over

25 ha. The second project "Identifying and protecting of primary

forests in Slovakia" was done during 2013-2015 and was focused on

identification of primary forest remnants (5-25 ha) and on updating of

data from first project.

Methodological approach

Publication Status

Field mapping was preceded by a careful selection of the areas with

expected occurrence of the old growth forests. This selection was

based on database of the forests stands of the National Forestry

Centre and a survey of the ortophotomaps. Finally, 324 localities with

area over 53 000 ha was selected for the field mapping. Based on the

field mapping of each site were identified borders of the old growth

forests and filled in forms of data for each locality. These data were

processed in the GIS and the map together with the database of old

growth forests in Slovakia was created.

PUBLISHED - Data and Description

330

Appendix I

Publication Details

Ownership of the dataset

Funding acknowledgment

http://en.pralesy.sk/index.php

PRALES, Komenskeho 21, 974 01 Banska Bystrica, Slovakia

Project was funded by EEA Financial Mechanism, Norway Financial

Mechanism and the State budget of the Slovak republic through

Ekopolis Foundation (www.ekopolis.sk). Project was also supported

by WWF.

Regime 1 - Restricted-access: Data are available for data contributors

only (i.e., custodians of other datasets) and their use depends on the

permission of the respective data custodian.

Data Accessibility

Dataset 27

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding

acknowledgment

Data Accessibility

UNPUBLISHED

Grafični prikaz gozdnih rezervatov

Francesco Maria Sabatini (based on data provided by Rok Pisek)

[email protected]

Slovenia Forest Service, Večna pot 2, 1000 Ljubljana, Slovenia

Slovenian Virgin forests

MAP

Slovenia

Complete inventory

2015

Virgin and long-untouched forests included in the forest reserves of

Slovenia

http://www.zgs.si/slo/gozdovi_slovenije/o_gozdovih_slovenije/gozdni_r

ezervati/index.html

Slovenian Forest Service

Regime 2 - Semi-restricted-access: Data are available for data

contributors only (i.e., custodians of other datasets) but without the need

for a specific permission in each single case.

Dataset 28

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Personal database

Francesco Maria Sabatini, on behalf of Schwendtner, Oskar

[email protected]

Humboldt-Universität zu Berlin, Department of Geography. Unter den

Linden 6, 10099, Berlin. Germany

Personal database

DATABASE

Spain

Appendix I

331

Data Completeness

Update status

Object of the dataset

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Partial Inventory

2015

Old-growth and long-untouched forests of Spain

UNPUBLISHED

Oscar Schwendtner

Regime 1 - Restricted-access: Data are available for data contributors

only (i.e., custodians of other datasets) and their use depends on the

permission of the respective data custodian.

Dataset 29

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Dynamic edge effects on Boreal forest (Sweden)

Ruete, Alejandro

[email protected]

Greensway AB, Ullsvägen 29C 75651 Uppsala Sweden

Shapefile and excel file describing the centroids of old growth forest

set aside from forest production in central Sweden

MAP

Dalarna and Gävleborg counties, Sweden

PARTIAL SURVEY

2009

We selected old-growth spruce dominated forest stands with edges

facing naturally disturbed stands or recurrent clearcuts in order to

estimate the effect of edge dynamics on the richness and occupancy

of dead wood dwelling fungi (among others). Stands should present

similar history without recent fires or alike, and be spruce dominated.

The forest landscape in the region is strongly influenced by modern

forestry, and characterized by even-aged monocultures of conifers,

few old trees, and low amounts of deadwood. Old-growth forest

remnants are typically few and small, occurring isolated in a matrix of

managed forests. 31 old-growth forest stands ranging in size from 0.6

to 398 ha were identified in 1998 as part of a biodiversity monitoring

program of fragmented forests of high conservation value. Stands

were surveyed twice (1998-2000 and again 2010-2011) with 14 m

wide transects divided in segments of 10m, searching for the presence

of deadwood and fungi species on them. The type and distance to the

most influential edge was estimated in the field for each segment of

the transect.

PUBLISHED - Data and Description

Ruete, A., Snäll, T. & Jönsson, M. (2016) Dynamic anthropogenic

edge effects on the distribution and diversity of fungi in fragmented

old‐growth forests.

Ecological Applications,

26,

1475-1485.

The data is own by Dalarna and Gävleborg county administrations,

represented by Olle Kellner and Urban Gunnarsson, it is now stored in

open repository, A. Ruete has been granted extra permit to share the

coordinates

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

332

Appendix I

Funding acknowledgment

Data Accessibility

The study was funded by the Swedish Forest Society

(Skogssällskapet) and the Faculty of Natural Resources and

Agricultural Sciences, SLU

CC 4.0

Dataset 30

DATASET NAME

Name

e-mail address

Affiliation and contacts

WSL-ETH Forest Reserve Network

Stillhard, Jonas

[email protected]

WSL Swiss Federal Institute for Forest, Snow and Landscape

Research

Research Unit Forest Resources and Management

Zürcherstrasse 111

8903 Birmensdorf, Switzerland

The shapefile contains all forest reserves within the WSL-ETH

Forest Reserve Network considered to meet the definition of primary

forests (levels n5-n10, Buchwald (2005)).

MAP

Switzerland

The dataset contains all forest reserves within the WSL-ETH Forest

Reserve Network considered primary forests (levels n5-n10) sensu

Buchmann (2005). It does not include other primary forests in

Switzerland.

2016

The project objectives are to study forest reserves in Switzerland,

focusing on the natural processes of tree regeneration, growth and

mortality, the co-existence of tree species, the impact of natural

disturbances and climate change on forest succession, and on the

value of forest reserves as habitat. This is achieved by continuous

monitoring of forest structure, including permanent plots established

by ETH since 1948 and by comparison with managed forests.

The forests selected for the actual project were all forests

considered as primary forests (n5 – n10 sensu Buchwald (2005))

within the ETH-WSL forest reserve network.

The reserves were selected with respect to forest types, size and

data availability. Forest types were selected according to their

importance for management, to enable comparisons with managed

forests, but include also types for which Switzerland bears a special

responsibility (e.g. Stone pine forests). Larger reserves were

preferred as well as reserves where ETH had already established a

monitoring over forests without monitoring. Most of the reserves

have been monitored at least once (except Tiefenwald and Arena,

which will be monitored in the coming years). Permanent plots were

established in Scatlè, the Swiss National Park, Leihubelwald, St.

Jean, Girstel, Pfynwald, Aletschwald, Tamangur and Murgtal. On

permanent plots, every tree with DBH >3.9 cm was measured,

assessed, stem tagged and stem mapped. Sample plot inventories

were carried out in Val Cama – Val Leggia, Aletschwald, Tamangur

and Selvasecca. Sample plots consist of two concentric circles of

200 m and 500 m . On every sample plot, all living and dead trees

with a DBH >6.9 cm on 200 m and all living and dead trees with a

DBH >35.9 cm on 500 m were measured and stem mapped.

Title of the Dataset

Data Type

Data Coverage - Region(s)

covered

Data Completeness

Update status

Object of the dataset

Methodological approach

Appendix I

333

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

What data accessibility

regime should be assigned

to the contributed dataset?

Additionally, a regeneration survey and a deadwood assessment

was carried out.

The dataset is published online within the ‘Forest reserves’ dataset

of the FOEN (Federal Office of the Environment). The dataset as is

was solely generated for the ForestsCO-network.

Brang, P., Heiri, C. & Bugmann, H. (Eds.) (2011) Waldreservate. 50

Jahre natürliche Waldentwicklung in der Schweiz. Birmensdorf,

Eidg. Forschungsanstalt WSL; Zürich, ETH Zürich; Bern, Stuttgart,

Wien, Haupt. 272 p.

Vanoni, M., Bugmann, H., Nötzli, M. & Bigler, C. (2016) Drought and

frost contribute to aprubt growth decreases before tree mortality in

nine temperate tree species. Forest Ecology and Management,

382,

41-63.

Heiri, C., Wolf, A., Rohrer, L., Brang, P. & Bugmann, H. (2012)

Successional pathways in swiss mountain forest reserves.

European journal of forest research,

131(2),

503-518.

FOEN (Federal Office of the Envirionment), Species, Ecosystems,

Landscape Division.

The Swiss Federal Office of the Environment (FOEN) co-funds the

data acquisition in the reserves within the WSL-ETH Forest Reserve

Network.

Regime 2 - Semi-restricted-access: Data are available for data

contributors only (i.e., custodians of other datasets) but without the

need for a specific permission in each single case.

Dataset 31

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage -

Region(s) covered

Data Completeness

Update status

Object of the dataset

Methodological approach

Publication Status

Publication Details

Oleh

Chaskovskyy

[email protected]

Ukrainian National Forestry University, department of forestry, Gen.

Chuprynky, 103, Lviv-79057, Ukraine

old-growth forests in the Ukrainian Carpathian ecoregion

MAP

Ukrainian Carphatian ecoregion

COMPLETE INVENTORY

2010

forest inventory maps, shapefile describing the distribution of old-

growth forests in the Ukrainian Carpathians

candidate forests were selected and mapped via forest inventory

data

UNPUBLISHED

Hamor, F., Dovhanych, Y., Pokynchereda, V., Sukharyuk, D.,

Bundzyak, Y., Berkela, Y., Voloshchuk, M., Hodovanets, B. & Kabal,

M. (2008) Virgin forests of Transcarpathia. Inventory and

management., 2008th ed. Rakhiv,

WWF Ukraine

Regime 1 - Restricted-access: Data are available for data

contributors only (i.e., custodians of other datasets) and their use

Ownership of the dataset

Funding acknowledgment

Data Accessibility

334

Appendix I

depends on the permission of the respective data custodian.

Dataset 32

DATASET NAME

Name

e-mail address

Affiliation and contacts

Title of the Dataset

Data Type

Data Coverage - Region(s)

covered

Data Completeness

Update status

Object of the dataset

OGF/VF UA

Volosyanchuk Roman

[email protected]

WWF Danube-Carpathian Programme in Ukraine, Mushaka, 42, Lviv,

Ukraine

Shapefile with polygons and attributive data of the Old-Growth

including Virgin Forests in Ukrainian Carpathians

MAP

Ukrainian Carpathians

PARTIAL SURVEY

2016

Mapping of existing areas of pristine/old-growth forests that should be

maintained through non-intervention management. More description at:

http://sfmu.org.ua/ua/projects/deg

Criteria of virgin forests are used according to the Carpathian

Convention, see:

http://www.carpathianconvention.org/tl_files/carpathiancon/Downloads/

03%20Meetings%20and%20Events/COP/2014_COP4_Mikulov/Follow

%20Up/DOC13_Criteria_Indicators_virginforests_FINAL_26SEP.pdf

Criteria of OGF have been developed and harmonized for Ukrainian

and Romanian Carpathian region, see:

http://sfmu.org.ua/files/OGF_PF_metodychka_-A5.pdf

Candidate forests (potential OGF) were selected using the forestry

inventory data and then checked in the field by visiting every plot by

especially trained experts. All the methodology is described at:

http://sfmu.org.ua/files/OGF_PF_metodychka_-A5.pdf (in Ukrainian

language).

Results published at:

http://gis-wwf.com.ua/

raw data are not publicly

available.

http://gis-wwf.com.ua/

WWF Danube-Carpathian Programme Ukraine

1) Open borders for bears between Romanian and Ukrainian

Carpathians, ENPI-East CBC, HUSKROUA/1001/038; 2) Support

Responsible Forest Management for a Sustainable Development in the

Ukrainian Carpathians, DEG-PPP, 9E0710.04-06; 3) Support

Responsible Forest Management for a Sustainable Development in the

Danube – Carpathian Ecoregion, IKEA, 9E0710.05, 100104; 4)

Identification and protection of old-growth and virgin forest in the

Ukrainian Carpathians, Bundesministerium für Umwelt, Naturschutz,

Bau und Reaktorsicherheit / Michael Succow Foundation, P 612 A

1095 / UBA76856

Data are available to a wider community of users. These data can be

made available on request to non-members after submitting a request

to the FORESTS and CO, with special approval from WWF DCP

Ukraine

Methodological approach

Publication Status

Publication Details

Ownership of the dataset

Funding acknowledgment

Data Accessibility

Appendix I

335

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Appendix I

341

Appendix 4 – Definition framework

In this work we adopted the definition framework developed by Buchwald (2005), during the Third

Expert Meeting on Harmonizing Forest-related Definitions, organized by FAO in Rome in January

2005. The meeting aimed at tackling the problem of the ambiguous use of forest-related definitions in

various international processes and we adopted it for being authoritative, and because it proved to be

flexible enough, to allow us overcome methodological and conceptual differences between definitions

used across different European countries.

This framework identifies 14 mutually exclusive levels of forest naturalness, and incorporate the most

logical and generally accepted parts of existing definitions. Below, we report the names of the 14

levels, as well as the definitions for those levels that are the object of our research. We refer the reader

to the original publication for the philosophical background of this approach and additional details

(Buchwald, 2005).

Levels of Naturalness



n10 – Primeval forest



n9 – Virgin forest



n8 – Frontier forest



n7 – Near-virgin forest



n6 – Old-growth forest



n5 – Long-untouched forest



n4 – Newly-untouched forest



n3 – Specially managed forest



n2 – Exploited natural forest



n1 – Plantation-like natural forest



p4 – Partly-natural planted forest



p3 – Native plantation



p2 – Exotic plantation



p1 – Self-sown exotic forest

Buchwald (2005) defines as primary forests all those forests having a naturalness level comprised

between n10 and n5 (bold).

Notes:

1. Every naturalness level applies to a specific spatial scale. Levels n10 and n8 apply at

landscape scale. Levels n9 and n7 apply at forest scale, while all the others apply at stand scale

(Buchwald, 2005). For the purpose of this manuscript, we set as thresholds for the landscape-

scale an extent greater than 500 km

(so to be consistent with the concept of Intact Forest

Landscape (Potapov et al., 2017), and for forest scale a minimum size of 250 ha. This means

that the naturalness levels n10 and n8 could only be assigned to primary forest patches greater

than 500 km

, and the naturalness levels n7 and n9 could only be assigned to primary forest

patches greater than 250 ha.

2. We assumed the definition of primary forest reported by Buchwald as conceptually equivalent

to FAO definition of primary forest (FAO, 2015). For clarity we report below also the

definition of ‘Forest undisturbed by man’ used (FOREST EUROPE, 2015), which is also

considered as being equivalent to FAO’s definition.

3. Given the millennia of anthropogenic activities in European forests, for the scopes of this

manuscript we conservatively avoided to attribute the highest levels of naturalness (n10-n8) to

the primary forest patches we mapped.

342

Appendix I

Buchwald’s definitions of naturalness levels consistent with primary forest (n10-n5)

Definitions below come from Buchwald (2005).

Primary forests

(n10-n5):

Relatively intact forest areas that have always or at least for the past sixty

to eighty years been essentially unmodified by human activity. Human impacts in such forest areas

have normally been limited to low levels of hunting, fishing and harvesting of forest products, and, in

some cases, to historical or pre-historical low intensity agriculture.

n10 Ultimate degree of naturalness – Primeval Forest –

Forest ecosystems never modified by modern

man/civilization even indirectly, where the degree of impact on the ecosystem by indigenous people

has not been significantly higher than the impacts of natural wildfire and of large wild animals (e.g.

beaver or megaherbivores). The fauna includes a rich host of large animal species and is not

significantly affected by human-induced extinctions or changes to animal population densities. Size is

landscape-scale. […]

n9 Extremely high degree of naturalness – Virgin Forest –

Forest ecosystems virtually unmodified by

man, and where the degree of former human impact on the forest – including soil and hydrology – has

been only slightly more significant than the impacts of wildfire and animals (e.g. beaver or

megaherbivores), and is no longer obvious. Wildlife inhabits the area with a fairly natural density and

species composition including large herbivores and carnivores. Size is forest-scale. […]

n8 Very high degree of naturalness – Frontier forest –

A frontier forest is an area meeting the

following criteria: it is primarily forested and predominantly consists of indigenous tree species. It is

big enough to support viable populations of all indigenous species associated with that forest type […]

even in the face of the natural disasters – such as hurricanes, fires, and pest or disease outbreaks –

that might occur there in a century. It is home to most, if not all, of the other plant and animal species

that typically live in this type of forest. Its structure and composition are determined mainly by natural

events, though limited human disturbance by traditional activities of the sort that have shaped forests

for thousands of years -such as low-density shifting cultivation – may be acceptable. As such, it

remains relatively unmanaged by humans, and natural disturbances (such as fire) are permitted to

shape much of the forest. In forests where patches of trees of different ages would naturally occur, the

landscape exhibits this type of heterogeneity. (Rearranged/shortened from World Ress.

Inst.:http://www.wri.org/ffi/lff-eng/).

n7 Very high degree of naturalness – Near-virgin forest –

Forest ecosystems (forest scale) untouched

long enough to have attained structures, dynamics and species composition similar to virgin forest,

even though they may have been significantly modified, e.g. by clearcutting or agriculture at some

time in the past. They are distinguished by a mixture in time and space between different seral stages,

e.g. between old-growth stages and younger stages. Human impact on the forest structures is not

obvious to see. The time necessary in untouched development before this level can be reached depends

on how modified the situation was at the start. It is at least several hundred years if the starting point

is a plantation-like forest.

n6 High degree of naturalness – Old-growth forest –

Ecosystems (stand scale) distinguished by old

trees and related structural attributes. Old growth encompasses the later stages of stand development

that typically differ from earlier stages in a variety of characteristics which may include tree size,

accumulations of large dead woody material, number of canopy layers, species composition, and

ecosystem function. The age at which old-growth develops and the specific structural attributes that

characterise old-growth will vary widely according to forest type, climate, site conditions, and

disturbance regime. […] However, old-growth is typically distinguished from younger growth by

several of the following attributes: 1) large trees for species and site, 2) wide variation in tree sizes

and spacing, 3) accumulations of large-size dead standing and fallen trees that are high relative to

earlier stages, 4) decadence in the form of broken or deformed tops or bole and root decay, 5)

Appendix I

343

multiple canopy layers, and 6) canopy gaps and understory patchiness. Old-growth is not necessarily

“virgin” or “primeval.” Old-growth can develop following human disturbance […]

n5 Quite high degree of naturalness – Long untouched forest –

Relatively intact forest (stand level)

that has been essentially unmodified by human activity for the past sixty to eighty years or for an

unknown, but relatively long time. Signs of former human impacts may still be visible, but strongly

blurred due to the decades without forestry operations. The time limit depends on how modified the

forest was at the starting point. […]

Other definitions relevant to the scopes of this manuscript

Primary forest – FAO definition (FAO, 2015):

Naturally regenerated forest of native species, where

there are no clearly visible indications of human activities and the ecological processes are not

significantly disturbed.

Explanatory note

Some key characteristics of primary forests are:



they show natural forest dynamics, such as natural tree species composition, occurrence of dead

wood, natural age structure and natural regeneration processes;



the area is large enough to maintain its natural characteristics;



there has been no known significant human intervention or the last significant human intervention

was long enough ago to have allowed the natural species composition and processes to have

become re-established.

Forest undisturbed by man (FOREST EUROPE, 2015):

naturally regenerated stands of native species,

with natural dynamics (which requires sufficient area and natural structures). They are always of high

nature conservation value. No clearly visible indications of human activities are acceptable.

Note: Forest undisturbed by man is considered equivalent to FAO’s primary forest in Forest Europe’s

work.

References

Buchwald, E. (2005) A hierarchical terminology for more or less natural forests in relation to

sustainable management and biodiversity conservation.

Proceedings: Third expert meeting on

harmonizing forest-related definitions for use by various stakeholders

Food and Agriculture

Organization of the United Nations, Rome, 17-19 January 2005.

FAO (2015) Global Forest Resources Assessment 2015. Terms and definitions. In:

Forest resources

Assessment Working Paper 180,

p. 36. FAO, Rome.

FOREST EUROPE (2015) State of Europe’s Forests 2015.

Potapov, P., Hansen, M.C., Laestadius, L., Turubanova, S., Yaroshenko, A., Thies, C., Smith, W.,

Zhuravleva, I., Komarova, A., Minnemeyer, S. & Esipova, E. (2017) The last frontiers of

wilderness: Tracking loss of intact forest landscapes from 2000 to 2013.

Science Advances,

344

Appendix I

Supporting Information

To Sabatini et al. 2017. Where are Europe’s last primary forests?

In review for: Diversity and Distributions

Table S1 – Geodatabase architecture

Table S1 – Architecture of the geodatabase of European primary forests

* required fields ‡ required field for point datasets

Variable Name

OBJECTID

FORESTS_NAME*

LOCATION*

NATURALNESS_LEVEL*

Variable_type Description and possible values

Object ID

Text

Short Integer

Name of the forest stand

Municipality in which the the primary forest remnant

is located

Naturalness level according to Buchwald (2005):

n10 - Primeval Forest,

n9 - Virgin Forest,

n8 - Frontier Forest,

n7 - Near-virgin Forest,

n6 - Old-growth Forest,

n5 - Long Untouched Forest

FOREST_EXTENT_MEASURED

‡

Double

The total extent of the primary forest remnant patch

in hectares. This field is only relevant when a

polygon feature IS NOT available for the forest

patch.

The order of magnitude of the extend of a primary

forest remnant patch. This field is only relevant when

a polygon feature IS NOT available for the forest

patch and no precise measurement of the total

extent of the forest remnant is available.

Possible values:

1-10 ha

11-100 ha

101-1000 ha

>1001 ha

FOREST_EXTENT_ESTIMATED

‡

Short Integer

DOMINANT_TREE_SPECIES1*

DOMINANT_TREE_SPECIES2

DOMINANT_TREE_SPECIES3

PROTECTED_SINCE

PROTECTION_STATUS

THREATS_1

Text

Short Integer

Species (latin name) of the dominant tree species of

the overstorey

Species (latin name) of the second dominant tree

species of the overstorey (if any)

Species (latin name) of the third dominant tree

species of the overstorey (if any)

Year since the onset of legal protection

binary, yes\no

Threat (if any) that is most likely to endanger the

primary forest remnant (drop-down list). Possible

values:

Plantation development

Antrhopogenic Fires

Tourism/recreation

Infrastructure development (including touristic)

Appendix I

345

Mismanagement

Illegal logging

Timber and fuelwood extraction

Non-Timber Forest Products extraction

Urbanization and housing construction

Climate change

Biodiversity loss

Diseases/pests

Agricultural expansion

Hunting

Invasive Species

OTHER

THREATS_2

Short Integer

Threat (if any) that is most likely to endanger the

primary forest remnant (drop-down list).

See above for possible values)

LAST_DISTURBANCE1_EVENT_TYPE

Text

If known, type of the last disturbance event (drop-

down list)

Fire

Windthrow

Flood

Landslide Avalanche

Logging\harvesting

Diseases\pests outbreak

OTHER natural

OTHER antrhopogenic

LAST_DISTURBANCE1_YEAR

LAST_DISTURBANCE1_INTENSITY

Short Integer

Year when disturbance event 1 happened

Intensity of disturbance event 1 – (Dropdown list)

1. Light (<20% of the stand disturbed))

2. Moderate (20-70% of the stand disturbed)

3. Stand replacing (>70% of the stand disturbed)

LAST_DISTURBANCE2_EVENT_TYPE

LAST_DISTURBANCE2_YEAR

LAST_DISTURBANCE2_INTENSITY

CONTACT_PERSON*

RELEVANT_LITERATURE

Text

Short Integer

Text

If known, type of the penultimate disturbance event

(drop-down list). See above

Year of disturbance event 2

Intensity of disturbance event 2 – (Dropdown list)

see above

Name of the contact person providing the

information on the stand

Any relevant sources of information describing the

forest remnant (including journal articles, local

reports and websites)

At the time of data submission or update, the

custodians assign one of the following data

availability regimes to the data contributed

(Dropdown list)

Regime 1 – Restricted-Access

Regime 2 – Semi-restricted-Access

Regime 3 – Free Access.

For details please, check the ‘Data Governance

Rules’ of FORESTS and CO

DATA_AVAILABILITY_REGIME*

Short Integer

346

Appendix I

Table S2 – Summary of primary forest datasets collected

Table S2 - List of dataset collected

Country

Balkans

Bulgaria

Custodian

Dataset name

Type of

features

Polygons

# of

features

127

Publication Status

Trotsiuk et al. (2014);

Svoboda et al. (2014)

Bottero et al. (2011)

http://gis.wwf.bg/mobilz_e

Panayotov et al. (2016)

Martin Mikolas Forest Ecology

Group CULS

Matteo

LomJanPerBio

Garbarino

Tzvetan

Zlatanov

Momchil

Panayotov

Bulgaria

Croatia

Czech

Republic

Denmark

Stjepan Mikac

Tomas Vrska

Erik Buchwald

WWF - Old

Polygons

Forests in

Bulgaria

Coniferous Old- Polygons

growth forest of

Rila and Pirin

NP, Bulgaria

Personal

Polygons

database

Czech natural

forests databank

Old-growth &

long untouched

forests of

Denmark

Hemiboreal old-

growth forests

of Estonia

Literature

Review -

Foreste Vetuste

di faggio

UNESCO -

Primeval Beech

Forests

UNESCO –

Primeval Beech

Forests

Extension

Publicly

available data on

OG forests of

Finland

WWF - Hauts

lieux de

naturalité en

France

RNF

Polygons

Points

363

Unpublished

http://naturalforests.cz/czec

h-natural-forests-databank

Unpublished

Estonia

Ann Kraut

Points

23?

Lõhmus and Kraut (2010)

Europe

Fabio

Lombardi

Points

149

EEA (2014)

10 Europe

Hanns

Kirchmeir

Hanns

Kirchmeir

Polygons

http://whc.unesco.org/en/li

st/1133

Kirchmeir and Kovarovics

(2016)

11 Europe

Polygons

12 Finland

Olli-Pekka

Tikkanen

Polygons

681

Derived from

http://www.syke.fi/en-

US/Open_information

http://www.foretsanciennes

.fr/proteger-mieux/france/

13 France

Daniel

Vallauri?

Polygons

14 France

15 Germany

Nicolas

Debaive

Peter Meyer

Polygons

7+7

Unpublished

http://www.naturwaelder.d

Naturwaldreserv Polygons

ate in

+ points

Deutschland

Appendix I

347

16 Greece

17 Hungary

18 Italy

19 Lituania

20 Macedonia

“World Heritage

Beech Forests of

Europe" Greek

candidates

Ferenc Horváth Hungarian

Forest Reserve

monitoring

Sabina

Network Foreste

Burrascano

Vetuste nei

Parchi Nazionali

Italiani

Gintautas

Natural forests

Mozgeris

in Lithuania

Nikolaos

Grigoriadis

Bojan Simovski Personal

database - Old-

growth forests in

Mavrovo NP

Rein Midteng

Old-growth

forests in

Norway outside

protected areas

Jerzy

Database of old-

Szwagrzyk

growth forests

of Poland

Inês Marques

Natural forest

Duarte

areas in Portugal

Leónia Nunes Natural forest

areas in

Portuguese

Macaronesia

region

Costel

WWF -

Bucur\Radu

Lemnocontrolat

Melu

Juraj Vysoky

Rok Pisek

Prales Database

Grafični prikaz

gozdnih

rezervatov

Personal

database

Polygons

Unpublished

Polygons

Unpublished

Polygons

Blasi et al. (2010)

Polygons

+ points

4+1

Unpublished

21 Norway

Polygons

Skogkur 2020.

Redningsplan for Norges

unike skoger

Unpublished

22 Poland

Polygons

23 Portugal

24 Portugal

Polygons

+ points

Polygons

31+6

Unpublished

25 Romania

Polygons

1887

http://www.lemncontrolat.r

o/en/home/

http://en.pralesy.sk/

http://www.zgs.si/slo/gozd

ovi_slovenije/o_gozdovih_

slovenije/gozdni_rezervati/

index.html

Unpublished

Ruete et al. (2016)

Tinner et al. (2010; 2012)

26 Slovakia

27 Slovenia

Polygons

290

168

28 Spain

29 Sweden

30 Switzerland

Oscar

Schwendtner

Alejandro

Ruete

Jonas Stillhard

Points

31 Ukraine

Oleh

Chaskovsky

Roman

Volosyanchuk

32 Ukraine

Strict Forest

Reserves in

Switzerland

Virgin Forests

Transcarpathia

WWF -

Identified old-

growth forests

of Ukrainian

Carpathians

Polygons

Hamor et al. (2008)

Polygons

4914

http://gis-wwf.com.ua/

348

Table S3 – Results of literature review

323.7

1230

10282

1434

347

764

5970

86.9

761

240

268

142

51.1

4747

600

300

261.8

57.2

55.8

300

101

600

old-growth

virgin, old-growth

Location

Island Bjuren

Sercaia village

Kolasin

Evo

Uholko-Shyrokoluzhanskiy massif

Sutjeska National Park

Capracotta

Villavallelonga

Pietrabbondante

Pietracamela

Vico del Gargano

Soriano al Cimino

San Pietro Avellana

Badia Prataglia

Posada de Rengos

Pietracamela

Brcalnik

Nová Sedlica

Rothwald (?)

Müritz National Park

Bieszczadzki National Park

Babia Gora National Park

Kocevje

Bialowieza National Park

Šumava National Park

Harz Mountains

Runcu Grosi Natural Reserve

Sipovo

Drinic

Västerbotten County

Nature Reserve Giumalau

Calimani National Park

Hainich National Park

Domusnovas

Santadi

Orgosolo

Novohradské mountains

Appendix I

First.Author

Blasko

Petritan

Motta

Clear

Peck

Sabatini

Lombardi

Seedre

Vondrak

Nopp-Mayr

Fichtner

Paluch

Westergreen

Bobiec

Janda

Dittrich

Petritan

Keren

Castagneri

Fraver

Svoboda

Schrumpf

Scattolin

Král

Year

2015

2012

2014

Country

Sweden

Romania

Montenegro

Finland

Ukraine

Bosnia-Herzegovina

Italy

Spain

Italy

Czech Republic

Slovakia

Austria

Germany

Poland

Slovenia

Poland

Czech Republic

Germany

Romania

Bosnia-Herzegovina

Sweden

Romania

Germany

Italy

Czech Republic

Forest_name

UNKNOWN

Sinca

Biogradska Gora

Sudenpesankangas

Uholka

Perucica

Abeti Soprani

Cervara

Collemeluccio

Fonte Novello

Area Pavari - Gargano

Monte Cimino

MontediMezzo

Sasso Fratino

Muniellos

Bosco Aschiero

Certovo Lake catchment

Stužica

Wilderness Area Dürrenstein

Serrahn

Baniska

U zrodeł Solinki

Zarnowka

Rajhenavski Rog

Białowieza

Trojmezna

Mt. Brocken

Runcu-Grosi

Janj

Lom

Gardfjället Nature Reserve

Giumalau G1

Calimani C1

Calimani C2

Calimani C3

Calimani C4

Calimani C5

Hainich

Marganai

Gutturu Mannu

Montes

�½ofínsk�½ Prales

author.definition

Primary

Virgin, Primeval

old-growth

Primeval, virgin

old-growth

primary, old-growth

Virgin, old-growth

old-growth

primeval

old-growth

Primeval

old-growth

old-growth, virgin

old-growth

primary forest

Naturalness.level

n6 - Old-growth Forest

n7 - Near-virgin Forest

n6 - Old-growth Forest

n7 - Near-virgin Forest

n5 - Long Untouched Forest

n6 - Old-growth Forest

n5 - Long Untouched Forest

n6 - Old-growth Forest

n5 - Long Untouched Forest

n6 - Old-growth Forest

n5 - Long Untouched Forest

n6 - Old-growth Forest

n7 - Near-virgin Forest

n6 - Old-growth Forest

n7 - Near-virgin Forest

n6 - Old-growth Forest

n5 - Long Untouched Forest

n6 - Old-growth Forest

Page 1 of 7, Table S3

Table S3 – Results of literature review

Location

Vänge

Grmec Mountains

80.5

103.85

22.98

14.05

243.65

74.5

Appendix I

Forest_name

Fiby urskog

Bobija

Svanøy island

Bukacka Forest

Rehberg

Rågetaåsen

Siggaboda

Čorkova

uvala

Dobročsk�½ prales

Gorjanci

Kopa

Šrámková

Dividalen valley

Krokar

Reerisuo-Losusuo Mire Reserve

Badín

Havešová

Kyjov

Raštún

Rožok

Vtáčnik

Coppo del Principe

Fonte Regna

Monte Venere

Gibiel

Lipówka

Igman

Trstionica

Pljesivica

Devcica Tavani

Dobroc

Pecka

Strmec

Bukov Vrh

Lucka Bela

Sumik

Maramuresh Pop-Ivan

30.7

171.3

397.4

550

67.2

245.6

45.1

30.5

38.8

100.5

49.8

59.5

15.6

9.3

19.3

8990

503.94

First.Author

Hytteborn

Visnjic

Hegland

Horak

Bolte

Mikac

Gömöryová

Rugani

Korňan

Sjögren

Diaci

Lankia

Kucbel

Ziaco

Bartkowicz

Diaci

Šebesta

Arnan

Merganičová

Aaakala

Nascimbene

Dodelin

Babia hora

Lake Pyhäjärvi

Paneveggio

La Luine

L'Ubac de Méolans

Year

2014

2013

2012

2015

2011

2013

2012

2011

2010

Country

Sweden

Bosnia-Herzegovina

Norway

Czech Republic

Germany

Sweden

Croatia

Slovakia

Slovenia

Slovakia

Norway

Slovenia

Finland

Slovakia

Italy

Poland

Bosnia-Herzegovina

Croatia

Slovakia

Slovenia

Ukraine

Spain

Slovakia

Finland

Italy

France

author.definition

Primeval

Pristine, virgin

old-growth

Orlicke Mountains

old-growth

Harz National Park

old-growth

Halland

old-growth

Blekinge

old-growth

Plitvice Lakes National Park

virgin\old-growth

Drábsko Sihla

old-growth

Gorjenci massif

old-growth

Kocevski Rog massif

old-growth

Mala Fatra Mts

primeval

Troms county

primeval\old-growth

Kočevska Reka

old-growth

Badinsky prales

old-growth

Beskids Mountains

old-growth

Vihorlat Mountains

old-growth

Sološnica

old-growth

Mountains

old-growth

Opi

old-growth

Roccantica

Secondary old-growth

San Martino al Cimino

Secondary old-growth

Niepołomice Forest

old-growth

Niepołomice Forest

old-growth

old-growth, virgin

old-growth

Zakarpattya province

Primeval

Aigüestortes i Estany de Sant Mauri old-growth

Oravská Polhora

virgin

Pallas-Ylläs national park

old-growth

Paneveggio-Pale di San Martino Naold-growth

Aucelon (Drôme)

old-growth

Méolans-Revel (Alpes-de-Haute-Pr old-growth

Naturalness.level

n6 - Old-growth Forest

n5 - Long Untouched Forest

n6 - Old-growth Forest

n5 - Long Untouched Forest

n6 - Old-growth Forest

n5 - Long Untouched Forest

n6 - Old-growth Forest

n7 - Near-virgin Forest

n6 - Old-growth Forest

n5 - Long Untouched Forest

n6 - Old-growth Forest

349

Page 2 of 7, Table S3

350

Table S3 – Results of literature review

Appendix I

Location

Norrbotten

Västerbotten

Västernorrland

Kremnické vrchy

Pol'ana Mts

Pärnu

Viljandi

Tartu

Kõpu, Viljandimaa

Rägavere, Lääne-Virumaa

Gropha Landscape Reserve

Syvulya mountain range

Åsele SO

Örnsköldsvik NV

Gammtratten reserve

Oyambre Natural Park

Norrbotten

Elbasan, Librazhd district

Corsica

Patvinsuo National Park

Sakkats mountain

Lammi district

North Carelia

Malá Fatra Mts

Gleichen

Querfurt

Hyytiälä forestry field station

Gulf of Bothnia

First.Author

Sjöberg

Jabin

Ingerpuu

Trotsiuk

Johansson

Rozas

Esseen

Westphal

Lohmus

Richard

Komonen

Lundqvist

Laaka-Lindberg

Rouvinen

Kornan

Leuschner

Kuuluvainen

Svenning

Year

2007

2014

2006

2005

2007

2005

2004

2003

2001

Country

Sweden

Slovakia

Estonia

Ukraine

Sweden

Spain

Sweden

Albania

Ukraine

Estonia

France

Finland

Sweden

Finland

Slovakia

Germany

Finland

Sweden

Forest_name

Luottåive

Alpliden

Stenbithöjden

Kålhuvudet

Jämtgaveln

Boky

Pol'ana

Rohy

Urissaare

Loodi

Järvselja

Tipu

Volumäe

GR1

GR2

GR3

SY1

SY2

SY3

Björnlandet

Trolltjärn

Gammtratten A

Gammtratten B

Caviedes forest

Granladet Reserve

Mirdita

Puka

Rrajca

Borzhava

Alam-Pedja Nature Reserve

Fango Valley

Autiovaara

Kirjesålandet

Katinen Nature Reserve

Pönttövaara

Šútovská Dolina National Nature Reserve

Göttinger Wald

Kyffhäuser

Ziegelrodaer Forst

Solling

Lüneburger Heide

Nuijakorpi forest protection area

Västra Kvarken

author.definition

Naturalness.level

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

primeval

n6 - Old-growth Forest

176

primeval

n6 - Old-growth Forest

primeval

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

primeval

n6 - Old-growth Forest

primeval

n6 - Old-growth Forest

primeval

n6 - Old-growth Forest

primeval

n6 - Old-growth Forest

primeval

n6 - Old-growth Forest

primeval

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

old-growth

n5 - Long Untouched Forest

110

old-growth

n7 - Near-virgin Forest

28400

virgin

n6 - Old-growth Forest

virgin

n6 - Old-growth Forest

3.6

old-growth + significant virgin p n7 - Near-virgin Forest

2129.45

virgin

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

virgin

n6 - Old-growth Forest

300

old-growth

n6 - Old-growth Forest

old-growth

n5 - Long Untouched Forest

135

natural

n6 - Old-growth Forest

526.65

old-growth

n5 - Long Untouched Forest

old-growth

n5 - Long Untouched Forest

old-growth

n5 - Long Untouched Forest

old-growth

n5 - Long Untouched Forest

old-growth

n5 - Long Untouched Forest

old-growth

n6 - Old-growth Forest

old-growth

n6 - Old-growth Forest

Page 3 of 7, Table S3

Table S3 – Results of literature review

Forest_name

582

148

33.74

400

442

2200

116

990

166

Location

Seybothenreuth

Betzenstein

Bieszczadzki National Park

Javorniky Mountains

Beskydy mountains

Fontainebleau

Puolanka

Pudasjärvi

Puolanka

Suomussalmi

Kuhmo

Suomussalmi

Czarny szlak pieszy

Appendix I

55.2

Year

2001

2002

2015

2004

2000

2008

2002

15.28

75.76

37.43

34.6

15.86

First.Author

Kaiser

Jaworski

Šamonil

Jankovsky

Topoliantz

Ojala

Maycock

Ostojic

Lahde

Biris

Standovár

Vrska

Panajiotidis

Tsiripidis

Vandekerkhove

2012

2001

2003

2001

Country

Germany

Poland

Czech Republic

France

Finland

Poland

Slovakia

Ukraine

Serbia

Finland

Romania

Hungary

Czech Republic

Greece

Belgium

author.definition

old-growth

Hylaty (Jawornik)

primeval

Tworylczyk

primeval

Razula

virgin

Čertův

ml�½n

virgin

Kněhyně

virgin

La Tillaie

old-growth

Jaurakkavaara

old-growth

Olvassuo

old-growth

Siikavaara west

old-growth

Lososuo

old-growth

Juortaansalo

old-growth

Murronaho

old-growth

Louhenvaara

old-growth

Babia Gora

old-growth

Poreba Wielka-Koninki

old-growth

Turbacz Creek Valley

Konina

old-growth

Kostrza Mt.

Wilkowisko

old-growth

Klodne nad Dunajcer Reserve

Krościenko nad Dunajcem

old-growth

Baniska Reserve

Rytro

old-growth

Labowiec Reserve

Gorczański Park Narodowy

old-growth

Hroncokovy grun

Čierny

Balog

old-growth

Mala Uholka

old-growth

Velyka Uholka

old-growth

Shyrokoluzhankyi Massive

old-growth

Ivankyvci

old-growth

Danilova Kosa

Brštica

virgin

Feljeshana

Jelovac

virgin

Kukavica

Rdovo

virgin

Vinatovača

Strmosten

virgin

Golema Reka

Stara planina Nature park

virgin

Busovata

�½agubica

virgin

Rautavaara

old-growth

Cosava Mica

Semenic Mountain

n10 - Primeval Forest

Kékes Forest Reserve

Kékes, Mátra Mts

Old-growth Forest

Boubín

Bohemian Forest Mountains

n9 - Virgin Forest

Mavrologos

Prionia, Mt Olympus

n7 - Near-virgin Forest

Dasos 1000 dendron Mourion

Mouries, Kilkis

n10 - Primeval Forest

Dasos Apollonias

Apollonia, Lake Volvi

n10 - Primeval Forest

Baruga (Katafyki) Natural Monument

Grammos mountain, Kastoria Prefec n5 - Long Untouched Forest

Frakto

Paranesti

n6 - Old-growth Forest

Sonian Forest, core zone of the Forest ResGroenendaal

n6 - Old-growth Forest

Naturalness.level

n5 - Long Untouched Forest

n6 - Old-growth Forest

n5 - Long Untouched Forest

n6 - Old-growth Forest

n5 - Long Untouched Forest

n6 - Old-growth Forest

n5 - Long Untouched Forest

n7 - Near-virgin Forest

n5 - Long Untouched Forest

351

Page 4 of 7, Table S3

352

Table S3 – Results of literature review

Appendix I

First.Author

Kirchmeir

Year

2016

Country

Albania

Austria

Belgium

Bulgaria

Croatia

Italy

Poland

Romania

Forest_name

Location

author.definition

Naturalness.level

Lumi i gashit

Kukes, Tropojë district

old-growth + fragments of virginn7 - Near-virgin Forest

Rrajca

Elbasan, Librazhd district

old-growth + significant virgin p n7 - Near-virgin Forest

Dürrenstein

Province of Lower Austria

old-growth + significant virgin p n7 - Near-virgin Forest

Kalkalpen -Hintergebirge

Province of Upper Austria

old-growth + fragments of virginn6 - Old-growth Forest

Kalkalpen -Bodinggraben

Province of Upper Austria

old-growth + fragments of virginn6 - Old-growth Forest

Kalkalpen - Urlach

Province of Upper Austria

old-growth + fragments of virginn6 - Old-growth Forest

Kalkalpen - WilderGraben

Province of Upper Austria

old-growth + fragments of virginn6 - Old-growth Forest

Sonian Forest - Forest Reserve “JosephZw

Flanders

old-growth

n5 - Long Untouched Forest

Sonian Forest – Grippensdelle A

Brussels Capital Region

old-growth

n5 - Long Untouched Forest

Sonian Forest - Grippensdelle B

Brussels Capital Region

old-growth

n5 - Long Untouched Forest

Sonian Forest - Réserve forestière du TictoWallonia

old-growth

n5 - Long Untouched Forest

Sonian Forest - Réserve forestière du TictoWallonia

old-growth

n5 - Long Untouched Forest

Boatin Reserve

Lovech

majority virgin

n7 - Near-virgin Forest

Tsarichina Reserve

Lovech

majority virgin

n7 - Near-virgin Forest

Kozya stena Reserve

Lovech

majority virgin

n7 - Near-virgin Forest

Steneto Reserve

Lovech, Plovdiv

majority virgin

n7 - Near-virgin Forest

Stara reka Reserve

Plovdiv

majority virgin

n7 - Near-virgin Forest

Dzhendema Reserve

Plovdiv, Stara Zagora

majority virgin

n7 - Near-virgin Forest

Severen Dzhendem Reserve

Lovech

majority virgin

n7 - Near-virgin Forest

Peeshti skali Reserve

Lovech, Gabrovo

majority virgin

n7 - Near-virgin Forest

Sokolna Reserve

Stara Zagora

majority virgin

n7 - Near-virgin Forest

Hajdučki i Rožanski kukovi

Ličko-Senjska County, Velebit Mountmajority virgin

n7 - Near-virgin Forest

Paklenica National Park - Suva draga-Klim Dinaric region, or Dinaric Alpes

majority virgin

n7 - Near-virgin Forest

Paklenica National Park - Oglavinovac-JavoDinaric region, or Dinaric Alpes

majority virgin

n7 - Near-virgin Forest

Valle Cervara

Abruzzo, Lazio & Molise NP

old-growth + significant virgin p n6 - Old-growth Forest

Selva Moricento

Abruzzo, Lazio & Molise NP

old-growth + significant virgin p n5 - Long Untouched Forest

Coppo del Morto

Abruzzo, Lazio & Molise NP

old-growth + significant virgin p n6 - Old-growth Forest

Coppo del Principe

Abruzzo, Lazio & Molise NP

old-growth + significant virgin p n5 - Long Untouched Forest

Val Fondillo

Abruzzo, Lazio & Molise NP

old-growth + significant virgin p n5 - Long Untouched Forest

Cozzo Ferriero

Region Basilicata - Province of Poteold-growth

n5 - Long Untouched Forest

Foresta Umbra

Region Puglia - Province of Foggia old-growth

n5 - Long Untouched Forest

Monte Cimino

Region Lazio - Province of Viterbo old-growth

n5 - Long Untouched Forest

Monte Raschio

Region Lazio - Province of Viterbo old-growth

n5 - Long Untouched Forest

Sasso Fratino

Region Emilia - Romagna - Provinc old-growth

n6 - Old-growth Forest

Border Ridge and Upper Solinka Valley Bieszczadzki National Park

majority virgin

n7 - Near-virgin Forest

Polonia Wetlińska and Smerek

Bieszczadzki National Park

majority virgin

n7 - Near-virgin Forest

Stream Terebowiec valley

Bieszczadzki National Park

majority virgin

n7 - Near-virgin Forest

Stream Wolosatka valley

Bieszczadzki National Park

majority virgin

n7 - Near-virgin Forest

Cheile Nerei-Beușnița

Caraș Severin County

majority virgin

n7 - Near-virgin Forest

Codrul secular

Șinca

Brașov County

majority virgin

n7 - Near-virgin Forest

Codrul Secular Slătioara

Suceava County

majority virgin

n7 - Near-virgin Forest

Cozia - Masivul Cozia

Vâlcea County

majority virgin

n7 - Near-virgin Forest

Cozia - Lotrisor

Vâlcea County

majority virgin

n7 - Near-virgin Forest

1261.52

2129.45

1867.45

2946.2

890.89

264.82

1149.75

187.34

24.11

37.38

13.98

6.5

1226.88

1485.81

644.43

2466.1

591.2

1774.12

926.37

1049.1

824.9

1289.11

1241.04

790.74

119.7

192.7

104.71

194.49

325.03

95.74

182.23

57.54

73.73

781.43

1482.18

1049.53

200.99

574.33

4292.27

338.24

609.12

2285.86

1103.3

Page 5 of 7, Table S3

Table S3 – Results of literature review

Appendix I

5110.63

3517.36

1104.27

210.55

135.82

4677.21

598.14

74.5

720.24

255.52

71.79

63.97

171.06

213.65

109.58

753.48

384.81

212.01

1588.46

561.62

260.65

454.31

93.97

1164.16

156.84

First.Author

Kirchmeir

Diaci

Jaworski

Panayotov

Tinya

Mazziotta

Janík

Tiscar

Granata

Pittar

Vrska

Janik

Král

Vašíčková

Year

2016

1999

2001

2011

2016

2011

2016

2008

2009

2016

Country

Romania

Slovenia

Spain

Ukraine

Croatia

Poland

Bulgaria

Hungary

Denmark

Czech Republic

Spain

Italy

Slovakia

Czech Republic

Forest_name

Location

author.definition

Naturalness.level

Domogled - Valea Cernei- Domogled-Cor Caraș Severin County

majority virgin

n7 - Near-virgin Forest

Domogled - Valea Cernei- Iauna Craiovei Caraș Severin County and Mehedinț majority virgin

n7 - Near-virgin Forest

Domogled - Valea Cernei- Ciucevele CernCaraș Severin County and Gorj Cou majority virgin

n7 - Near-virgin Forest

Groșii

Țibleșului

- Izvorul

Șurii

Maramureș County

majority virgin

n7 - Near-virgin Forest

Groșii

Țibleșului

- Preluci

Maramureș County

majority virgin

n7 - Near-virgin Forest

Izvoarele Nerei

Caraș Severin County

majority virgin

n7 - Near-virgin Forest

Strîmbu Băiuț

Maramureș County

majority virgin

n7 - Near-virgin Forest

Krokar

Municipality Kočevje

majority virgin

n7 - Near-virgin Forest

Snežnik-�½drocle

Municipality Ilirska Bistrica and Munold-growth + fragments of virginn5 - Long Untouched Forest

Hayedos de Ayllón -Tejera Negra

Autonomous Community of Castilla-Lold-growth + fragments of virginn5 - Long Untouched Forest

Hayedos de Ayllón -Montejo

Providence of Guadaljara, region of old-growth + fragments of virginn5 - Long Untouched Forest

Hayedos de Navarra -Lizardoia

Ochagavia,salazar valley

old-growth + significant virgin p n5 - Long Untouched Forest

Hayedos de Navarra -Aztaparreta

Isaba, Roncal valley

old-growth + significant virgin p n6 - Old-growth Forest

Hayedos de Picos de Europa - Cuesta FríaAutonomous Community of Castilla yold-growth + significant virgin p n6 - Old-growth Forest

Hayedos de Picos de Europa - Canal de AsAutonomous Community of Castilla yold-growth + significant virgin p n6 - Old-growth Forest

Gorgany

Ivano-Frankivsk region, Nadvirna dis majority virgin

n7 - Near-virgin Forest

Roztochya

Lviv region

old-growth + significant virgin p n6 - Old-growth Forest

Satanіvska Dacha

Khmelnytska region, Horodok distric old-growth + fragments of virginn5 - Long Untouched Forest

Synevyr - Darvaika

Zakarpattia region, Mizhgirja

majority virgin

n7 - Near-virgin Forest

Synevyr - Kvasovets

Zakarpattia region, Mizhgirja

majority virgin

n6 - Old-growth Forest

Synevyr - Strymba

Zakarpattia region, Mizhgirja

majority virgin

n6 - Old-growth Forest

Synevyr - Vilshany

Zakarpattia region, Mizhgirja

majority virgin

n6 - Old-growth Forest

Zacharovanyi Krai -Irshavka

Zakarpattia region, Irshava

majority virgin

n6 - Old-growth Forest

Zacharovanyi Krai -Velykyi Dil

Zakarpattia region, Irshava

majority virgin

n7 - Near-virgin Forest

Velika Plješivica - Drenovača

virgin

n6 - Old-growth Forest

Czarna Hala

Babia Gora National Park

Primeval

n6 - Old-growth Forest

Dolny Płay

Babia Gora National Park

Primeval

n6 - Old-growth Forest

Jałowiecki Potok

Babia Gora National Park

Primeval

n6 - Old-growth Forest

Pod Sokolica

Babia Gora National Park

Primeval

n6 - Old-growth Forest

Parangalitsa

Bistritsa valley, Rila National Park old-growth

n6 - Old-growth Forest

Szalafoi Oserdo Forest Reserve

Orség National Park

old-growth

n5 - Long Untouched Forest

Tofte Skov

Lille Vildmose reserve

old-growth

n6 - Old-growth Forest

Høstemark Skov

Lille Vildmose reserve

old-growth

n6 - Old-growth Forest

Ranšpurk

South Moravian alluvium

old-growth

n5 - Long Untouched Forest

Cahnov–Soutok

South Moravian alluvium

old-growth

n5 - Long Untouched Forest

Poyo de Santo Domingo

Cazorla, Segura and Las Villas Natuold-growth

n5 - Long Untouched Forest

Bosco Siro Negri

Zerbolò

old-growth

n5 - Long Untouched Forest

Nefcerská dolina

Tatra National Park

virgin

n6 - Old-growth Forest

Mionší

Beskydy mountains

old-growth

n5 - Long Untouched Forest

�½ákova hora

Bohemian-Moravian Highland

old-growth

n5 - Long Untouched Forest

Salajka

Beskydy mountains

old-growth

n5 - Long Untouched Forest

Polom

�½elezné Mountains

old-growth

n5 - Long Untouched Forest

250

89.5

234.8

74.5

169

353

Page 6 of 7, Table S3

354

Table S3 – Results of literature review

Forest_name

Stožec

Milešice

NP Podyjí - Lipina

NP Podyjí - Zlámaná skála

Location

Bohemian Forest Mountains

South Moravian province

author.definition

old-growth

Naturalness.level

n6 - Old-growth Forest

n5 - Long Untouched Forest

First.Author

Vašíčková

Vrska

Matula

Year

2016

2001

2017

2015

Country

Czech Republic

Appendix I

Page 7 of 7, Table S3

Appendix I

355

Table S4 – Summary of primary forest extent by country

Table S4 – Summary of primary forest extent by country. For each country we reported the total area

of primary forest, the fraction of country area covered by primary forest, the percentage of primary

forest under legal protection, and under strict protection (IUCN category I), the number of mapped

forest remnants and their median extent.

Primary

forest

(1,000 ha)

Fraction of

country

covered by

primary

forests (%)

0.13%

0.08%

0.00%

0.01%

0.03%

0.50%

0.17%

8.87

1.69

0.05

985.95

5.50

5.93

1.79

0.28

5.56

32.55

0.83

2.83

0.08

105.83

21.27

16.21

51.67

0.21

11.02

9.58

6.80

30.08

21.54

70.11

0.10

1474.70

0.11%

0.04%

0.00%

2.92%

0.01%

0.02%

0.01%

0.00%

0.02%

0.50%

0.03%

0.20%

0.00%

0.33%

0.07%

0.18%

0.22%

0.00%

0.22%

0.47%

0.01%

0.07%

0.52%

0.12%

0.00%

0.25%

Protected

(%)

Protected

under

IUCN

regime I

(1,000 ha)

34.6%

10.9%

96.7%

0.0%

5.6%

75.8%

24.7%

6.7%

19.8%

0.0%

55.2%

0.6%

0.0%

42.9%

0.0%

35.5%

75.2%

0.0%

3.5%

0.2%

24.8%

0.1%

11.2%

56.2%

27.0%

92.3%

0.1%

76.6%

26.7%

0.0%

45.9%

216

161

280

168

590

3794

2834

398

109

110

105

283

115

1442

219

Num.

remnants

Median

extent

(ha)

Albania

Austria

Belarus

Belgium

Bosnia and Herzegovina

Bulgaria

Croatia

Cyprus

Czech Republic

Denmark

Estonia

Finland

France

Germany

Greece

Hungary

Ireland

Italy

Latvia

Lithuania

Luxembourg

Macedonia

Malta

Moldova

Montenegro

Netherlands

Norway

Poland

Portugal

Romania

Serbia

Slovakia

Slovenia

Spain

Sweden

Switzerland

Ukraine

United Kingdom

Total

3.64

7.13

0.06

0.27

1.75

55.71

9.83

93.1%

100.0%

98.4%

100.0%

91.5%

99.2%

100.0%

98.5%

100.0%

95.7%

98.9%

100.0%

97.8%

100.0%

99.9%

100.0%

98.8%

5.6%

100.0%

93.3%

94.9%

71.2%

96.7%

98.0%

100.0%

97.7%

87.8%

44.4%

65.0%

89.3%

121

1020

356

Appendix I

Table S5 – Summary of primary forest extent by biogeographical region

Table S5 – Summary of primary forest extent by biogeographical region. For each region we

reported the total area of primary forest, the fraction of country area covered by primary

forest, the percentage of primary forest under legal protection, and under strict protection

(IUCN category I), the number of mapped forest remnants and their median extent.

Primary

forest

(1,000 ha)

Fraction of

region

covered by

primary

forests (%)

0.58%

0.02%

0.61%

0.99%

0.03%

1.47%

0.01%

0.00%

0.25%

Protected

(%)

Protected

Num.

Median

under

remnants extent

IUCN

(ha)

regime I

(1,000 ha)

45.2%

1473

50.4%

91.9%

48.6%

1556

9.4%

355

26.7%

165

17.4%

163

0.0%

45.9%

3794

Alpine

Atlantic

Black sea

Boreal

Continental

Macaronesia

Mediterranean

Pannonian

Global

389.89

15.28

6.84

977.51

61.08

15.10

8.46

0.53

1474.70

68.9%

66.7%

100.0%

96.8%

96.7%

93.3%

99.0%

100.0%

89.3%

Appendix I

357

Table S6 – Summary of BRT accuracy and precision for increasing likelihood

thresholds

Table S6 – Comparison of accuracy, true and false positive rates, true and false negative

rates, and precision when different thresholds were used for discriminating between predicted

primary forest occurrence vs. absence. TPR: True Positive rate; FPR: False Positive Rate;

TNR: True Negative Rate; FNR: False Negative Rate. Accuracy is defined as the square root

of the product between TPR and TNR. Precision is calculated as the ratio between true

positives and the sum of false positives and true positives.

Percentile Likelihood

Threshold

100

0.009

0.057

0.076

0.095

0.115

0.136

0.159

0.182

0.207

0.234

0.263

0.296

0.332

0.373

0.42

0.473

0.536

0.61

0.703

0.818

0.992

TPR

FPR

TNR

FNR

Accuracy Precision

0.991

0.986

0.983

0.978

0.975

0.973

0.97

0.953

0.93

0.914

0.908

0.891

0.873

0.85

0.834

0.794

0.733

0.662

0.575

0.412

0.997

0.992

0.98

0.964

0.952

0.941

0.916

0.897

0.861

0.827

0.798

0.761

0.72

0.677

0.622

0.547

0.467

0.388

0.288

0.148

0.003

0.008

0.02

0.036

0.048

0.059

0.084

0.103

0.139

0.173

0.202

0.239

0.28

0.323

0.378

0.453

0.533

0.612

0.712

0.852

0.009

0.014

0.017

0.022

0.025

0.027

0.03

0.047

0.07

0.086

0.092

0.109

0.127

0.15

0.166

0.206

0.267

0.338

0.425

0.588

0.056

0.088

0.141

0.187

0.217

0.24

0.286

0.314

0.36

0.398

0.428

0.461

0.494

0.524

0.562

0.6

0.625

0.637

0.64

0.593

0.498

0.501

0.504

0.506

0.509

0.514

0.515

0.519

0.525

0.532

0.539

0.548

0.557

0.573

0.592

0.611

0.631

0.667

0.735

358

Appendix I

Figure S1 – Fraction of the study area were systematic inventories of primary

forests were performed (bias mask)

Figure S1 – European forest area (grey) at a 1x1 km resolution and bias mask (pink) used for

selecting the background points used to train the Boosted Regression Tree model. The pink

areabias mask represents the fraction of the study area were systematic inventories of

primary forests were performed. More than 96% of the primary forests occurred within the

mask. The forest mask was derived from Hengeveld et al. (2015)

Appendix I

359

Figure S2 – Barplot of primary forest area per forest type and naturalness level

Figure S2 – Extent of primary forest in the compiled dataset, according to Forest types (EEA,

2006) and naturalness levels (Buchwald, 2005). Forest types follow (EEA, 2006): FT1-

Boreal forest, FT2 - Hemiboreal and nemoral coniferous-mixed forest, FT3 - Alpine

coniferous, FT4-5 - Mesophytic deciduous and acidophilous forest, FT6 – Beech forest, FT7 -

Mountainous beech forest, FT8 - Thermophilous deciduous forest, FT9 - Broadleaved

evergreen forest; FT10 - Coniferous Mediterranean forest; FT12 – Floodplain forest, FT13 -

Non-riverine alder, birch or aspen, NA-NC - NoData/Unclassified. Naturalness levels: n7 -

Near-virgin forest, n6 – Old-growth forest, n5 – Long-undisturbed forest.

360

Appendix I

Figure S3 – Barplot of primary forest patches (counts) per forest type and

naturalness level

Figure S3 – Number of primary forest remnants mapped across Forest types (EEA, 2006) and

naturalness levels (Buchwald, 2005). Forest types follow Forest types: FT1- Boreal forest,

FT2 - Hemiboreal and nemoral coniferous-mixed forest, FT3 - Alpine coniferous, FT4-5 -

Mesophytic deciduous and acidophilous forest, FT6 – Beech forest, FT7 - Mountainous beech

forest, FT8 - Thermophilous deciduous forest, FT9 - Broadleaved evergreen forest; FT10 -

Coniferous Mediterranean forest; FT12 – Floodplain forest, FT13 - Non-riverine alder, birch

or aspen, NA-NC - NoData/Unclassified. Naturalness levels: n7 - Near-virgin forest, n6 –

Old-growth forest, n5 – Long-undisturbed forest.

Appendix I

361

Figure S4 – Precision and Accuracy of BRT model for increasing thresholds of

relative likelihoods

Figure S4 – Graph plotting BRT model accuracy and precision when different thresholds

were considered for discriminating between predicted primary forest occurrence vs. absence.

362

Appendix I

Figure S5 – Map of congruency between predicted (top 10

percentile) and

observed primary forests

Figure S5 - Map of congruency between predicted (top 10

percentile) and observed primary

forests.

Appendix I

363

Figure S6 – Map of the protections status of the areas predicted to host

primary forests

Figure S6 –Share of predicted (top 10

percentile) primary forests being currently under

protection and strict (IUCN level I) protection.

364

Appendix I

Cited References

Blasi, C., Burrascano, S., Maturani, A. & Sabatini, F.M. (2010)

Old-growth forests in Italy.

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Editori, Rome, Italy.

Bottero, A., Garbarino, M., Dukic, V., Govedar, Z., Lingua, E., Nagel, T.A. & Motta, R. (2011) Gap-

phase dynamics in the old-growth forest of Lom, Bosnia and Herzegovina.

Silva Fennica,

45,

875-887.

Buchwald, E. (2005) A hierarchical terminology for more or less natural forests in relation to

sustainable management and biodiversity conservation.

Proceedings: Third expert meeting on

harmonizing forest-related definitions for use by various stakeholders

Food and Agriculture

Organization of the United Nations, Rome, 17-19 January 2005.

EEA (2006) European forest types. Categories and types for sustainable forest management reporting

and policy. In. EEA Technical Report No 9/2006. EEA, Copenhagen.

EEA (2014)

Developing a forest naturalness indicator for Europe. Concept and methodology for a

high nature value (HNV) forest indicator.

EEA Technical report No 13/2014, Luxembourg:

Publications Office of the European Union.

Hamor, F., Dovhanych, Y., Pokynchereda, V., Sukharyuk, D., Bundzyak, Y., Berkela, Y.,

Voloshchuk, M., Hodovanets, B. & Kabal, M. (2008) Virgin forests of Transcarpathia.

Inventory and management., 2008th ed. Rakhiv,

Hengeveld, G.M., Gunia, K., Didion, M., Zudin, S., Clerkx, A.P.P.M. & Schelhaas, M.J. (2015)

Gridded maps as derived from a compilation of global forest biomass estimates 1950-2010. In:

(ed. O.R. Ornl Daac, Tennessee, USA. Http://Dx.Doi.Org/10.3334/Ornldaac/1296)

Kirchmeir, H. & Kovarovics, A. (ed.^eds) (2016)

Nomination Dossier "Primeval Beech Forests of the

Carpathians and Other Regions of Europe“ as extension to the existing Natural World

Heritage Site “Primeval Beech Forests of the Carpathians and the Ancient Beech Forests of

Germany” (1133bis),

Klagenfurt.

Lõhmus, A. & Kraut, A. (2010) Stand structure of hemiboreal old-growth forests: Characteristic

features, variation among site types, and a comparison with FSC-certified mature stands in

Estonia.

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155-165.

Panayotov, M., Tsvetanov, N., Gogushev, G., Tsavkov, E., Zlatanov, T., Anev, S., Ivanova, A.,

Nedelin, T., Zafirov, N., Aleksandrov, N., Dountchev, A., Vasileva, P., Shishkova, V.,

Stoyanov, B., Sotirova, N., Vatov, A., Bebi, P. & Yurukov, S. (2016)

Mountain coniferous

forests in Bulgaria – structure and natural dynamics.

University of Forestry and Geosoft,

Sofia.

Ruete, A., Snäll, T. & Jönsson, M. (2016) Dynamic anthropogenic edge effects on the distribution and

diversity of fungi in fragmented old-growth forests.

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Svoboda, M., Janda, P., Bače, R., Fraver, S., Nagel, T.A., Rejzek, J., Mikoláš, M., Douda, J., Boublík,

K., Šamonil, P., Čada, V., Trotsiuk, V., Teodosiu, M., Bouriaud, O., Biriş, A.I., S�½kora, O.,

Uzel, P., Zelenka, J., Sedlák, V. & Lehejček, J. (2014) Landscape-level variability in historical

disturbance in primary Picea abies mountain forests of the Eastern Carpathians, Romania.

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Tinner, R., Stalder, A. & Brang, P. (2010) Aufnahmemethode für Kernflächen in schweizerischen

Naturwaldreservaten. In. Version

Tinner, R., Streit, K., Commarmot, B. & Brang, P. (2012) Stichprobeninventur in Schweizer

Naturwaldreservaten–Anleitung zu Feldaufnahmen.

Birmensdorf, Eidg. Forschungsanstalt für

Wald, Schnee und Landschaft WSL,

Trotsiuk, V., Svoboda, M., Janda, P., Mikolas, M., Bace, R., Rejzek, J., Samonil, P., Chaskovskyy, O.,

Korol, M. & Myklush, S. (2014) A mixed severity disturbance

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APPENDIX II

Udviklingen i antallet af ynglefugle i

Danmark 1800-2012 II

Lars Dinesen, Tom S. Romdal

Michael B. Grell & Erik Buchwald

(Dansk Ornitologisk Forenings Tidsskrift 110 (2016): 201-206)

366

Previous page: Netta rufina (photo by Erik Buchwald)

Appendix II

367

Udviklingen i antallet af ynglefugle i Danmark 1800-2012 II

ARS

INESEN

S. R

OMDAL

ICHAEL

B. G

RELL

& E

RIK

UCHWALD

(With a summary in English: Development in the number of breeding bird species in Denmark 1800-2012 II)

Indledning

I DOFT 107: 281-290, 2013 publicerede de første tre for-

fattere TSR, LD og MBG en artikel, der belyste udviklin-

gen i antallet af ynglefugle i Danmark mellem 1800 og

2012 baseret på et litteraturstudie af de væsentligste or-

nitologiske datakilder frem til 2012 (Romdal

et al.

2013).

Metoden er beskrevet i detaljer i denne artikel og er i

korthed en analyse af udviklingen i antallet af ynglende

arter i Danmark. Som kriterium er de arter medtaget, der

har haft en bestand på fem sandsynlige ynglepar eller

derover siden år 1800 i udvalgte skæringsår. Som skæ-

ringsår er anvendt 1800, 1850, 1900, 1945 og årene fra

de to atlasprojekter 1971-74 og 1993-96 gennemført af

Dansk Ornitologisk Forening. Siden 1993 er antallet af

sandsynlige ynglepar for hver art opgjort årligt. Som et

mål for robustheden af resultatet blev gennemført til-

svarende analyser for henholdsvis 1 og 50 sandsynlige

ynglepar (se Romdal

et al.

2013).

Siden udgivelsen af Romdal

et al.

(2013) er forfatterne

blevet opmærksomme på nye data for nogle arter. Det

har således vist sig, at der for otte arter var behov for en

revision af vurderingen af antal sandsynlige ynglepar for

2011 og 2012. En art blev desuden tilføjet listen, nemlig

Hvidvinget Korsnæb

Loxia leucoptera,

der ynglede med

fem sandsynlige par i 2012 (Nyegaard

et al.

2014). For at

undersøge om dette nye datasæt ville ændre de oprinde-

lige konklusioner, blev analyserne i Romdal

et al.

(2013)

gentaget, og resultatet er publiceret i nærværende artikel.

Det fulde reviderede datasæt samt en revideret over-

sigt over de arter, der indenfor undersøgelsesperioden

har eller har haft en bestand mellem fem og 50 sandsyn-

lige ynglepar findes i Tab. 1 og Appendiks 1. Analyserne i

nærværende meddelelse er alle baseret på kriterieret for

mindst fem sandsynlige ynglepar i et givent år.

Metode

For en nærmere redegørelse af metoden henvises til

Romdal

et al.

(2013). Publikationer udgivet herefter og

frem til og med september 2015 er gennemgået for nye

oplysninger om ynglepar i 2011 og 2012. Disse omfatter

oversigten i

Fugleåret 2012

(Lange 2013), en gennem-

gang af truede og sjældne ynglefugle i Danmark (Nye-

gaard

et al.

2014) og oversigten i

Fugleåret 2011

(Lange

2012). Ligeledes er der inddraget nye resultater publice-

ret efter 2013 i diskussionen.

Dansk Orn. Foren. Tidsskr. 110 (2016): 201-206

368

Appendix II

202

Ynglefugle i Danmark

Tab. 1. Danske ynglefuglearter 1800-2012, der på et af skæringstidspunkterne har haft en bestand på mellem fem og 50 sand-

synlige ynglepar fordelt efter habitatkategori (se Grell

et al.

2005). For hver art er bestanden i 2012 vurderet (forsvundet 0; 1-4

sandsynlige par (≥ 1); 5-49 sandsynlige par (≥ 5); ≥ 50 sandsynlige par). Dertil kommer en angivelse af minimumsvurdering af

bestanden i 2011 eller tidligere baseret på den danske EU-rapportering (Pihl & Fredshavn 2015), og tidspunkt for indvandring eller

forsvinden er ligeledes angivet.

Habitat

Art

Species

2012-kategori og 2011-bestandsvurdering

og tidspunkt for

kolonisering/forsvinden

Population category in 2012, minimum population estimate 2011 or earlier

according to Danish EU reporting and time for colonizing or disappearance

≥ 50; 100; indvandret i 1970erne; ingen sikre ynglefund i 1800-tallet

≥ 50; 66; Ynglefugl i 1800-tallet; tilbagegang til 10-15 par 1970erne

≥ 5; 100; første kendte ynglefund 1918

0; sidste kendte ynglepar af den oprindelige bestand 1950erne

≥ 5; indvandret i 1950erne; ingen sikre ynglefund i 1800-tallet

≥ 1; 3; sidste kendte par af den oprindelige bestand 1916

≥ 5; 38; ikke over 50 par siden 1800

≥ 50; 100; vidt udbredt men ikke almindelig i 1800-tallet

≥ 1; 3; formentlig indvandret i 1960-70erne; ikke over 50 par

≥ 50; 39; ikke med sikkerhed over 50 par i 1800-tallet

≥ 5; 30; bestandstop midt/sidst i 1900-tallet

≥ 5; 50; indvandret i 1960erne; ingen sikre ynglefund i 1800-tallet

0; sidste kendte ynglepar af den oprindelige bestand i 1959-60

≥ 50; 225; indvandret i 1960erne; ingen sikre ynglefund i 1800-tallet

≥ 5; 17; tilbagegang fra slutningen af 1800-tallet

≥ 5; 10; indvandret i sidste halvdel 1800-tallet; tilbagegang

0; ikke over 50 par siden 1800

≥ 50; 90 000; formentlig indvandret i slutningen af 1800-tallet

≥ 50; 15 000; indvandret i slutningen af 1800-tallet

≥ 50; 500; Indvandret i 1970-80erne

≥ 5; 25; ikke over 50 par siden 1800

≥ 50; 1500; ekspanderet i 1900-tallet

≥ 50; 500; indvandret ca. 1960; ingen sikre ynglefund i 1800-tallet

≥ 5; 50; truffet siden 1960erne

≥ 50; 15 000; indvandret i de første årtier af 1900-tallet

≥ 50; 6000; indvandret i sidste halvdel af 1900-tallet

≥ 50; 850; indvandret efter midten af 1800-tallet

≥ 5; ikke rapporteret; første gang registreret i 2012

≥ 5; 20; første kendte fund i 1940erne; toppede i 1970erne

≥ 50; 200; indvandret i sidste halvdel af 1800-tallet

≥ 50; 3600; næsten forsvundet i 1920erne; 3-4 par i 1926

≥ 1; ikke rapporteret; indvandret efter 2000

≥5; 8; indvandret i 1940erne; ikke over 50 par siden 1800

≥ 50; 280; ekspanderet sidst i 1800-tallet

≥ 50; 900; ekspanderet i begyndelsen af 1900-tallet

≥ 50; 250; første kendte ynglefund 1870erne

≥ 5; 28; stor tilbagegang og fluktuerende antal

≥ 50; 3600; indvandret omkring 1865

≥ 50; 300; næsten forsvundet omkring år 1900; 10-20 par 1970erne

0; ikke over 50 par siden 1800

≥ 5; 17; indvandret i 1970erne;

≥ 50; 53; meget markant tilbagegang i perioden

≥ 50; 650; bestand < 50 par år 1900

≥ 5; 6; indvandret i 1960erne

≥ 50; 2000; indvandret i 1960erne

≥ 5; 20; indvandret i 1970erne

≥ 5; ikke rapporteret; indvandret i 1990erne

≥ 50; 1100; kendt fra 1800-tallet; genindvandret i 1940erne

≥ 1; 7; første kendte fund 1861

0; 1; første kendte ynglefund 1953

≥ 50; 265; genindvandret i 1990erne

≥ 50; 400; indvandret i begyndelsen af 1900-tallet

Skov/krat

Forest/

scrub

Hvinand

Bucephala clangula

Stor Skallesluger

Mergus merganser

Turteldue

Streptopelia turtur

Sort Stork

Ciconia nigra

Svaleklire

Tringa ochropus

Fiskeørn

Pandion haliaetus

Havørn

Haliaeetus albicilla

Rød Glente

Milvus milvus

Perleugle

Aegolius funereus

Stor Hornugle

Bubo bubo

Vendehals

Jynx torquilla

Lille Flagspætte

Dendrocopos minor

Mellemflagspætte

Dendercopos medius

Sortspætte

Dryocopus martius

Lærkefalk

Falco subbuteo

Pirol

Oriolus oriolus

Nøddekrige

Nucifraga caryocatactes

Sortmejse

Parus ater

Topmejse

Parus cristatus

Fyrremejse

Parus montanus

Rødtoppet Fuglekonge

Regulus ignicapillus

Parktræløber

Certhia brachydactyla

Sjagger

Turdus pilaris

Karmindompap

Carpodacus erythrinus

Dompap

Pyrrhula pyrrhula

Gråsisken

Acanthis flammea

Lille Korsnæb

Loxia curvirostra

Hvidvinget Korsnæb

Loxia leucoptera

Gulirisk

Serinus serinus

Grønsisken

Carduelis spinus

Knopsvane

Cygnus olor

Sangsvane

Cygnus cygnus

Rødhovedet And

Netta rufina

Taffeland

Aythya ferina

Troldand

Aythya fuligula

Sorthalset Lappedykker

Podiceps nigricollis

Plettet Rørvagtel

Porzana porzana

Rørhøne

Gallinula chloropus

Rørdrum

Botaurus stellaris

Mudderklire

Actitis hypoleucos

Sorthovedet Måge

Larus melanocephalus

Sortterne

Chlidonia niger

Rørhøg

Circus aeruginosus

Pungmejse

Remiz pendulinus

Skægmejse

Panurus biarmicus

Savisanger

Locustella luscinioides

Flodsanger

Locustella fluviatilis

Græshoppesanger

Locustella naevia

Drosselrørsanger

Acrocephalus arundinaceus

Vandstær

Cinclus cinclus

Blåhals

Luscinia svecia

Bjergvipstjert

Motacilla cinerea

Sø/mose/å

Lake/bog/

river

Appendix II

369

Ynglefugle i Danmark

203

Danish breeding bird species 1800-2012 with a presumed breeding population of between five and 50 pairs some time during 1800-2012

distributed on primary habitat categories (from Grell

et al.

2005). For each species, the presumed breeding population is estimated in

2012 in the following intervals (none 0; 1-4 presumed par (≥ 1); 5-49 (≥ 5) presumed pairs; ≥ 50 presumed pairs). A specific population

estimate is given based on the Danish bird report to the EU (Pihl & Fredshavn 2015) based on a minimum value. Moreover, the time of

colonization or disappearance from Denmark is stated.

Habitat

Art

Species

2012-kategori og 2011-bestandsvurdering

og tidspunkt for

kolonisering/forsvinden

Population category in 2012, minimum population estimate 2011 or earlier

according to Danish EU reporting and time for colonizing or disappearance

≥ 50; 500; indvandret i 1900-tallet;

0; første kendte ynglefund i 1916

0; markant tilbagegang; sidste kendte ynglefund 1998

≥ 50; 114; formentlig forsvundet i 1800-tallet; genindvandret 1952

≥ 1; 1; meget markant tilbagegang

0; 3; meget stor tilbagegang

≥ 50 par; 330; ynglefugl i 1800-tallet; genindvandret 1920erne

≥ 50 par; 43; stor tilbagegang; under 50 par flere år i seneste årti

0; meget markant tilbagegang; sidste kendte ynglefund 1902

≥ 50 par; 94; tilbagegang siden 1800; under 50 par i 2000

≥ 1; 2; uregelmæssig ynglefugl i 1900-tallet

0; 1; stor tilbagegang

0; meget sjælden ynglefugl; ikke over 50 par siden 1800-tallet

≥ 5; 5; sandsynligvis stor tilbagegang

0; forholdsvis almindelig i starten af 1800-tallet

0; spredt ynglefugl i 1800-tallet til starten af 1900

≥ 5; 5; ikke over 50 par; første kendte ynglefund 1927

0; stor tilbagegang; sidste kendte fund i 1998

≥ 50 par; 58; tidligere mindre almindelig

≥ 1; 1; stor tilbagegang

≥ 50; 2000; indvandret i 1990erne

≥ 50; 101; genindvandret 1996

≥ 50; 32 548; forsvundet 1870erne; genindvandret1930erne

≥ 50; 56; stor tilbagegang; flere år i 1990-2000erne under 50 par

≥ 5; 30; større bestand i 1800-tallet; vurderet 100 par omkring 1900

≥ 50; 1300; koloni forsvandt omkring 1900; genindvandret 1920erne

≥ 50; 2900; koloni forsvandt omkring 1900; genindvandret 1920erne

≥ 50; 340; indvandret i 1940erne

≥ 50; 5000; fåtallig i 1800-tallet

≥ 50; 1800; indvandret i 1930erne

≥ 5; ikke rapporteret; ynglefugl i 1800-tallet; genindvandret 2008

≥ 5; 4; ynglefugl siden 1800; sidste ynglefund 1972; genindvandret i 2001

≥ 50; 26 000; indvandret i 1950erne

≥ 1; 2; indvandret midt i 1800-tallet; vidt udbredt; stor tilbagegang

≥ 50; 500; indvandret i sidste halvdel af 1800-tallet

≥ 50; 1830; tilbagegang siden 1800; nogle år under 50 par

≥ 50; 150; tilbagegang fra 1800; nogle år under 50 par

≥ 5; 22; indvandret sidst i 1800-tallet

≥ 50; 55; fluktuerende; enkelte år i 1990erne under 50 par

≥ 5; 43; stor tilbagegang siden 1800

0; ynglet sporadisk i 1900-tallet; stabilt i perioden 1998-2004

≥ 50; 1000; nede på en anslået bestand på 16 par i 1950

Hede/

eng/

overdrev

Heathland/

meadow/

commons

Knarand

Anas strepera

Pibeand

Anas penelope

Urfugl

Tetrao tetrix

Trane

Grus grus

Hvid Stork

Ciconia ciconia

Hjejle

Pluvialis apricaria

Storspove

Numenius arquata

Brushane

Calidris pugnax

Tredækker

Gallinago media

Tinksmed

Tringa glareola

Dværgmåge

Larus minutus

Sandterne

Gelochelidon nilotica

Blå Kærhøg

Circus cyaneus

Mosehornugle

Asio flammeus

Hærfugl

Upupa epops

Ellekrage

Coracias garrulus

Stor Tornskade

Lanius excubitor

Høgesanger

Sylvia nisoria

Sortstrubet Bynkefugl

Saxicola torquata

Markpiber

Anthus campestris

Bramgås

Branta leucopsis

Skestork

Platalea leucorodia

Skarv

Phalacrocorax carbo

Hvidbr. Præstekrave

Charadrius alexandrius

Stenvender

Arenaria interpres

Alk

Alca torda

Lomvie

Uria aalge

Ride

Rissa tridactyla

Sildemåge

Larus fuscus

Svartbag

Larus marinus

Rovterne

Sterna caspia

Vandrefalk

Falco peregrinus

Tyrkerdue

Streptopelia decaocto

Toplærke

Galerida cristata

Husrødstjert

Phoenicurus ochruros

Kyst/hav

Coastal

areas

By/have

Urban

areas

Agerland

Vagtel

Coturnix coturnix

Arable land

Engsnarre

Crex crex

Hedehøg

Circus pygargus

Slørugle

Tyto alba

Kirkeugle

Athene noctua

Biæder

Merops apiaster

Ravn

Corvus corax

Bestandsvurderingen følger størrelse og udvikling af fuglebestande i Danmark (Pihl & Fredshavn 2015), der udgør den danske

rapportering til EU efter fuglebeskyttelsesdirektivet. Bestandsvurderingen er en minimumsvurdering foretaget i 2011 eller nær-

meste foregående år, der rummer grundlag for en vurdering.

370

Appendix II

204

Ynglefugle i Danmark

Resultater

Den reviderede analyse har resulteret i en ændret status

for otte arter sammenlignet med Romdal

et al.

(2013):

Rødhovedet And

Netta rufina

(2012), Plettet Rørvagtel

Porzana porzana

(2011), Sortterne

Chlidonias niger

(2011), Kirkeugle

Athene noctua

(2011), Mosehornugle

Asio flammeus

(2012), Flodsanger

Locustella fluviatilis

(2012), Hvidvinget Korsnæb (2012) og Gulirisk

Serinus

serinus

(2012). Årstallene refererer til det år, der er sket

en ændring i artens kategorisering som følge af gen-

nemgang af ny litteratur. Det samlede datasæt er vist

i Appendiks 1.

De nye resultater viser fortsat, at der er en stærk stig-

ning i antallet af ynglende fuglearter i Danmark på små

30 arter fra 149 i 1800 til 178 i 2012 (Fig. 1). De to største

år er 1994 med 176 arter og 2012 med 178 arter. Stig-

ningen er stærkt signifikant (lineær regression n = 7, t =

5,69, p > 0,01). I modsætning til den oprindelige analyse

i Romdal

et al.

(2013) ses imidlertid ingen tilbagegang i

det samlede artsantal de sidste 20 år (lineær regression

n = 20, t = -1,25, p = 0,22).

Antallet af ikke-efterstræbte arter, navnlig spurve-

fugle, defineret i Romdal

et al.

(2013), stiger i perioden,

og stigningen topper i 1994 (Fig. 2). Stigningen er signi-

fikant i perioden fra 1800 til 2012 (lineær regression n =

7, t = 6,94, p < 0,01). Der ses dog stadig et signifikant fald

(lineær regression n = 20, t = -3,33, p < 0,01) i de sidste 20

år fra 98 arter i 1994 til 94 i 2012, på trods af at flertallet af

de otte arter med ændret status er registreret med

flere

end fem sandsynlige ynglepar.

Derimod viser antallet af efterstræbte arter, her-

under rovfugle og ugler, måger, ande- og vadefugle,

duer, kragefugle og flere andre arter, en ikke lineær

udvikling. Set over hele perioden er udviklingen ikke-

signifikant stigende og når et minimum i 1900 med 70

arter og et maksimum i 2012 med 84 arter som følge

af genindvandring af en række ynglefuglearter til

Danmark. Set over hele perioden er stigningen heller

ikke signifikant (lineær regression n = 7, t = 1,72, p =

0,14). Til gengæld er stigningen i de sidste 20 år signifi-

kant (lineær regression n = 20, t = 2,31, p < 0,05) modsat

analysen i Romdal

et al.

(2013).

Den reviderede udvikling i antal sandsynlige yng-

lepar fordelt på seks habitatkategorier, som omfatter

skov/krat, sø/mose/å, hede/eng/overdrev, kyst/hav, by/

have og agerland, er vist i Fig. 3. Resultaterne for skov/

krat og sø/mose/å er forskellige fra de tilsvarende i

Romdal

et al.

(2013), fordi artsantallet i disse levesteder

også de seneste år er steget modsat den stagnation, der

blev vist i de tilsvarende figurer i Romdal

et al.

(2013).

For øvrige habitattyper er ændringerne mere beskedne

eller ikke-eksisterende.

En revideret liste over danske ynglearter, hvis be-

stand har været mellem fem og 50 sandsynlige par i et

af skæringsårene i perioden 1800-2012 er vist i Tab. 1.

Samtidig er angivet en egentlig bestandsvurdering ba-

seret på den danske rapportering til EU efter fuglebe-

skyttelsesdirektivet (Pihl & Fredshavn 2015).

Diskussion

En gennemgang af ny litteratur har vist, at kategorise-

ringen i Romdal

et al.

(2013) burde ændres for otte arter.

Denne artikel bringer derfor resultaterne af en ny ana-

200

190

110

100

180

170

160

150

140

130

120

1800

1850

1900

1950

2000

1800

1850

1900

1950

2000

Fig. 1. Udviklingen i antallet af ynglende fuglearter i Danmark.

Alle arter med minimum fem sandsynlige ynglepar 1800-2012.

The development in the number of breeding bird species in Den-

mark. All species with minimum five presumed pairs 1800-2012.

Fig. 2. Udviklingen i antallet af ynglende fuglearter i Danmark

1800-2012 fordelt på efterstræbte arter (blå) og ikke efter-

stræbte arter (rød) med > 5 sandsynlige ynglepar.

The development in the number of persecuted (blue) and

non-persecuted (red) bird species in Denmark 1800-2012 for

species with > 5 presumed breeding pairs a year.

Appendix II

371

Ynglefugle i Danmark

205

lyse af det reviderede datasæt. De fleste af konklusio-

nerne i Romdal

et al.

(2013) holder efter den nye analyse,

og de få undtagelser diskuteres nedenfor.

I følge denne nye analyse er der ikke længere belæg

for, at det samlede antal ynglefuglearter er faldet de

sidste 20 år. Resultatet skal dog stadig ses i lyset af et

signifikant fald i antallet af ikke-efterstræbte arter og en

stigende ornitologisk aktivitet.

Udviklingen af onlineværktøjer som DOFbasen har

sammen med årlige bearbejdninger i fx

Fugleåret

(fx

Lange 2012) indenfor denne periode medført en langt

bedre dokumentation af ynglefugle. Samtidig har flere

truede danske ynglefugle formentlig overlevet pga. en

målrettet bevaringsindsats som fx opsætning af rede-

kasser og beskyttelse af reder. Det er således tvivlsomt,

om arter som Hvinand

Bucephala clangula,

Stor Skal-

lesluger

Mergus merganser,

Hvid Stork

Ciconia ciconia,

Hvidbrystet Præstekrave

Charadrius alexandrinus,

Brus-

hane

Calidris pugnax,

Engryle

Calidris alpina schinzii

og Hedehøg

Circus pygargus

ville være registreret som

ynglefugle i hele den seneste 20 års-periode, uden den

aktive forvaltningsindsats, der er foregået for disse en-

keltarter.

Ses på udviklingen i de sidste 20 år (1993-2012), er

der ifølge den reviderede analyse sket en signifikant

fremgang for gruppen af efterstræbte arter. Vi ser for

disse arter en udvikling, hvor det mest markante er et

lavpunkt i antallet af arter omkring år 1900, som afløses

af en stigning i artsantal. Gruppen af ikke efterstræbte

arter udviser som nævnt en signifikant tilbagegang.

Set over hele perioden er det navnlig fuglearter

knyttet til skovene (navnlig nåletræsarter; se også Hald-

Mortensen 1971) og tilgroede vådområder, der forår-

sager stigningen fra 149 til 178 arter i perioden 1800

til 2012 (se Romdal

et al.

2013 for en uddybning), idet

deres fremgang mere end opvejer tilbagegangen i art-

santallet for arter hovedsagelig knyttet til heder, enge

og overdrev. Det er dog værd at bemærke, at alminde-

lige nåleskovsspecialister som fx Fuglekonge

Regulus

regulus

og Topmejse

Parus cristatus

de seneste årtier er

Skov/krat

1800

1850

1900

1950

2000

Sø/mose/å

1800

1850

1900

1950

2000

1800

1850

1900

1950

2000

Hede/eng/overdrev

Kyst/hav

1800

1850

1900

1950

2000

By/have

Fig. 3. Antallet af ynglende fuglearter i Danmark 1800-2012

fordelt på forskellige habitatkategorier. Alle arter er tildelt en

primær og nogle også en sekundær habitat som defineret af

Grell

et al.

(2005). De øverste røde linjer er summen af arter

i primær- og sekundærhabitat. De blå linjer er alene arter i

primærhabitaten.

The development in the number of breeding bird species in

Denmark 1800-2012 divided into habitat categories. All species

were given a primary and some species also a secondary habitat

category as defined by Grell

et al.

(2005). Upper red lines represent

the sum of species in both categories, while blue lines represent

the species in primary habitat only. For translation of habitats,

see Tab. 1.

1800

1850

1900

1950

2000

Agerland

1800

1850

1900

1950

2000

372

Appendix II

206

Ynglefugle i Danmark

begyndt at gå tilbage muligvis som et resultat af, at kva-

liteten af nåleskov synes forringet (Eskildsen

et al.

2013).

Indvandringen af nåleskovsarter skete navnlig i sidste

halvdel af 1800-tallet som følge af tilplantning (Levin &

Normander 2008).

Det ser således fortsat ud til, at stigningen i artsantal-

let af danske ynglefugle er bremset op i perioden 1993-

2012 trods en fortsat betydelig naturindsats og en mere

omfattende registrering.

Et studie af bestandsudviklingen af europæiske fugle

gennem de sidste 30 år (1980-2009) viser, at de almin-

delige fugle går markant tilbage i antal (i alt skønnes

ca. 421 mio. individer at være forsvundet), hvorimod

sjældne arter er gået frem (Inger

et al.

2015). Den i nær-

værende analyse fundne fremgang i antallet af fuglear-

ter (artsrigdom) i Danmark kan således godt dække over

tilbagegang i individrigdom for almindelige arter.

Danmarks nylige afrapportering af fuglebeskyttel-

sesdirektivet til EU-kommissionen (Pihl & Fredshavn

2015) viser fx for perioden 1999-2011 en bred tilbage-

gang bl.a. for hovedparten af de ynglende spurvefugle,

der overvintrer i Afrika syd for Sahara, samt for mange

ynglende vadefugle.

Analyser viser, at det kan tage adskillige årtier fra

beskyttelsen øges, og til bestandene af efterstræbte

arter vender tilbage til deres tidligere udbredelsesom-

råde (Brasseur

et al.

2015). Denne tendens peger i lighed

med andre undersøgelser på, at naturbeskyttelseslov-

givning, fx fredning af rovfuglene i 1967, tiltrædelse af

internationale naturbeskyttelseskonventioner og im-

plementeringen af EU’s naturdirektiver (fx Donald

et al.

2007) og naturforvaltningstiltag herunder genopret-

ning af vådområder fra 1990erne har en direkte om end

ofte forsinket effekt på fuglebestandene (Sanderson

al.

2016) og artsrigdommen i Danmark.

mark increased significantly from 149 in 1800 to 178 in 2012.

Split into habitat types, the new results showed a continued

increase in numbers of species in forest and wetland habitats in

recent years. The largest decline in species numbers occurred

within the group heathland/meadow/commons. The general

stagnation in species richness reported in the last 20 years by

Romdal

et al.

(2013) was not supported. The number of non-

persecuted species, however, did show a statistically significant

decline in the last 20 years. By contrast, the species categorized

as (previously) persecuted increased significantly in the revised

analysis over the last 20 years – indicating a comeback from an

all-time low around 1900. Enhanced legal protection, landscape

change, nature management and a change in mindset among

the general public are believed to be some of the major drivers

contributing to the long term increase in Danish bird species

richness.

Referencer

Brasseur, M.J.M, T.D. van Polanen Petel, T. Gerrodette, E.H.W.G.

Meesters, P.J.H. Reijnders & G. Aarts 2015: Rapid recovery of

Dutch gray seal colonies fueled by immigration. – Mar. Mam-

mal Sci. 31: 405-426.

Donald, P.F., F.J. Sanderson, I.J. Burfield, S.M. Bierman, R.D. Gregory

& Z. Waliczky 2007: International conservation policy delivers

benefits for birds in Europe. – Science 217: 810-813.

Eskildsen, A., J.D. Larsen & H. Heldbjerg 2013: Use of an objective

indicator species selection method shows decline in bird pop-

ulations in Danish habitats. – Dansk. Orn. Foren. Tidsskr. 107:

191-207.

Grell, M.B., K. Flensted, T. Nyegaard, H. Heldbjerg & T. Vikstrøm

2005: Status over Danmarks ynglefugle 2005. – Dansk Orni-

tologisk Forening.

Hald-Mortensen, P. 1971: Status over Danmarks Ynglefugle. Pp

167-197 i H. Hvass (red.): Status over Danmarks Dyreverden.

– Rosenkilde og Bagger.

Inger, R., R. Gregory, J.P. Duffy, I. Stott, P. Vorisek & K.J. Gaston 2015:

Common European birds are declining rapidly while less abun-

dant species numbers are rising. – Ecology Letters 18(1): 28-36.

Lange, P. 2012: Fugle i Danmark 2011. – Fugleåret 6: 21-96.

Lange, P. 2013. Fugle i Danmark 2012. – Fugleåret 7: 22-102.

Levin, G. & B. Normander 2008: Arealanvendelse i Danmark siden

slutningen af 1800-tallet. – Faglig rapport fra DMU nr. 682.

Nyegaard, T., H. Meltofte, J. Tofft & M.B. Grell 2014: Truede og

sjældne ynglefugle i Danmark 1998-2012. – Dansk Orn. Foren.

Tidsskr. 108: 1-144.

Pihl, S. & J.R. Fredshavn 2015: Størrelse og udvikling af fuglebe-

stande i Danmark. – Videnskabelig rapport fra DCE nr. 176.

Romdal, T.S., L. Dinesen & M.B. Grell 2013: Udviklingen i antallet af

ynglende fuglearter i Danmark 1800-2012. – Dansk Orn. Foren.

Tidsskr. 107: 281-290.

Sanderson, F.J., R.G. Pople, C. Leronymidou, I. Burfield, R.D. Greg-

ory, S.G. Willis

et al.

2016: Assessing the Performance of EU

Nature Legislation in Protecting Target Species in an Era of

Climate Change. – Conservation Letters 9: 172-180.

Appendiks 1:

http://dof.dk/doft/2016/4.appendiks1

Forfatternes adresser:

Lars Dinesen ([email protected]), Åvej 1, 4470 Svebølle

Tom Romdal, Hvidovrevej 526, 2650 Hvidovre

Michael B. Grell, Jagtvej 193, st. tv, 2100 København Ø

Erik Buchwald, Smedievej 251, 3400 Hillerød

Tak

Tak til to anonyme referees for konstruktive kommenta-

rer til en tidligere version, og til Nick Quist for sproglig

revision af vores engelske tekster.

Summary

Development in the number of breeding bird species in Den-

mark 1800-2012 II

In DOFT 107: 281-290, 2013 the three first authors (LD, TSR

and MBG) presented a paper on development in the numbers

of species of Danish breeding birds 1800-2012 (Romdal

et al.

2013). Information published subsequently on Danish breed-

ing birds made a re-analysis of the extended dataset relevant,

and resulted in new categorization for eight species. The new

results confirmed that numbers of breeding bird species in Den-

APPENDIX III

Hvor skal vi gøre hvad i statsskov?

Erik Buchwald

(moMentum+ nr. 3, 2015. Temanummer om biodiversitet og skov)

374

Previous page: Trollius europaeus in Bolderslev Forest interior (photo by Erik Buchwald)

Appendix III

375

AF ERIK BUCHWALD

Hvor skal vi gøre

hvad i statsskov?

Naturstyrelsens prioriteringer skal danne grundlag for en revision

af biodiversitetsindsatsen på styrelsens skovarealer,

herunder udlæg af yderligere urørt skov og

Skov-perlemorsommerfugl er et

anden biodiversitetsskov

eksempel på en art, som kan spille

en særlig rolle i processen, idet den

Naturstyrelsen har på de arealer, den administrerer (statsskovene),

gennem årene gjort meget for at give gode kår for arter og natur. Men

de politiske vinde og forskeranbefalinger peger på, at der skal mere

til. Ikke mindst hvis FN og EUs 2020 mål om at standse tilbagegan-

gen i biodiversitet skal understøttes.

Siden 2014 har Naturstyrelsen arbejdet mere fokuseret med en da-

tabaseret prioritering af naturindsatser for at opnå bedre nytte af sine

begrænsede midler, bedre inddragelse af eksterne interessenter og med

stærkere fokus på opfyldelse af 2020 biodiversitetsmålene.

Denne prioritering ønskes ikke mindst at danne grundlag for en

revision af biodiversitetsindsatsen på Naturstyrelsens skovarealer,

herunder udlæg af yderligere urørt skov og anden biodiversitetsskov,

så disse udlæg fokuseres på steder med relevante trængende arter og

opfylder de stedlige arters økologiske behov.

er rødlistet og i fortsat tilbagegang.

ErhvervsPhD

For at skaffe et bedre grundlag for den forventede øgede indsats søgte

og ﬁk Naturstyrelsen derfor i 2014 en 3-årig ErhvervsPhD bevilling

fra Innovationsfonden.

Projektet »Analyse

og prioritering af fremtidig indsats for biodiversitet -

med særligt henblik på Naturstyrelsens arealer«

påbegyndtes 1. marts 2015

ved forfatteren, som efter ca. 23 år som forstfuldmægtig i styrelsen

blev indskrevet på KU som ph.d.-studerende. Vejledere er lektor

Jacob

Heilmann-Clausen

og professor

Carsten Rahbek

fra KU-CMEC (Cen-

ter for Makroøkologi, Evolution og Klima) foruden kontorchef

Mads

Jensen

og skovrider

Jens Bjerregaard Christensen

fra Naturstyrelsen.

Projektets formål er at undersøge og analysere, hvor og hvordan

man i Danmark kan gøre en effektiv indsats for at hindre tab af bio-

diversitet - særligt på de ca. 2.000 km

af Danmark, som ejes af staten

(ved Naturstyrelsen).

Her vil konkrete forslag til målrettet indsats kunne gennemføres

FOTOS: ERIK BUCHWALD

BIODIVERSITET OG SKOV

NR. 3 2015

entum

376

Appendix III

Beskyttelse af den biologiske

mangfoldighed er et af

de vigtige formål med

Naturstyrelsens arealer

relativt hurtigt, idet beskyttelse af den biologiske mang-

foldighed, herunder et rigt og varieret dyre- og planteliv,

er et af de vigtige formål med Naturstyrelsens arealer.

Statsskovenes rolle

De 200.000 ha, Naturstyrelsen administrerer, svarer

til ca. fem pct. af Danmarks areal. Andelen af skov og

natur er dog meget højere og op mod 100 pct. for visse

specielle naturtyper. Det betyder, at indsatsen på Naturstyrelsens arealer

kan have afgørende indﬂydelse også på nationalt plan.

For at kunne hjælpe de truede arter forvaltningsmæssigt er det nødven-

digt at kende deres krav til levested og hvilke trusler, der er vigtige for dem.

Oplysninger herom er af Aarhus Universitet samlet i en rødliste-data-

base (se tabel 1), hvis data dog ikke kan stå alene. Derfor inddrages også

bl.a. detaillitteratur om en mindre del af arterne.

De Økonomiske Råd har i relation til bevarelse af den danske biodi-

versitet konkluderet, at en stor indsats i skovene bør prioriteres højt, og

at der er brug for mere målrettet indsats i den åbne natur.

Rådets analyser var i høj grad baseret på data for Danmark opdelt i 10 x

10 km kvadrater. For at gøre det mere forvaltningsegnet er der behov for

at komme længere ned i skala til konkrete naturarealer, herunder skove.

Et prioritet katalog

Projektet vil derfor for Naturstyrelsens arealer gå dybere end de oven-

nævnte overordnede analyser og vurderinger ved at analysere og priori-

tere de truede arter og deres konkrete levesteder i forhold til viden om

arternes økologiske behov, udbredelse og klimakrav, relation til driftsfor-

mer samt hidtidige udvikling i Danmark.

Speciel fokus ofres på arter, som ser ud til at være af særlig betydning

for, at Danmark kan understøtte det politisk vedtagne 2020-mål om at

standse tabet i biodiversitet.

Projektets målsætning er tredelt og vil belyse:

•

Beskyttelsen af arter og naturtyper (ikke kun skov), der er særligt vig-

tige i relation til

det politiske 2020-mål

(1) om at standse tabet af bio-

diversitet, og

•

At undersøge spørgsmål, som vil være af betydning for forvaltningen

af truede

skovarter

(2) generelt i Danmark, og speciﬁkt på Natursty-

relsens arealer, så der kan indføres en mere målrettet og videnbaseret

indsats for arterne, samt

•

At anvende resultater fra forskningen til at udforme et

prioriteret katalog

over anbefalet indsats

(3), som hurtigt kan omsættes til konkret natur-

beskyttelse på Naturstyrelsens arealer

Truet

(CR,EN,VU)

881

645

1526

17,5

21,5

Næsten truet

(NT)

240

192

432

4,8

6,4

Livskraftig Data deﬁcient

(LC)

(DD)

3322

1842

5164

66,1

61,3

446

175

621

8,9

5,8

I alt

5026

3006

8032

100,0

100,1

Tabel 1: Rødlistedatabasens

arter fordelt efter, om da-

tabasen angiver en slags

skov som levested eller ej

i feltet for levested. Arter

uden levestedskode er

udeladt.

Antal arter

pr. levested

Skov

Ikke skov

Sum

Relativ andel

Pct. af skovarter

Pct. af ikke skovarter

Uddød

(RE)

137

152

289

2,7

5,1

entum

NR. 3 2015

BIODIVERSITET OG SKOV

Appendix III

377

Ad. 1: Det politiske 2020-mål (skov og andre naturtyper):

Til brug for

den fremtidige naturindsats på Naturstyrelsens arealer gennemføres

hot spot-, komplementaritets- og gap-analyse for forekomst og beskyt-

telse af truede arter, som spiller en særlig rolle for at nå det politiske

2020-mål om at standse tabet af biodiversitet. Analyserne vil belyse,

hvor godt de hidtidigt naturbeskyttede arealer dækker de truede arter,

og hvor der er mangler. Resultaterne danner grundlag for mere mål-

rettet planlægning af naturbeskyttelsen på Naturstyrelsens arealer.

Ad. 2: Skovarter:

Data for Danmarks truede arter analyseres for

habitattilknytning og -præferencer ud fra data for arternes forekomst

sammenholdt med Naturstyrelsens GIS-data for habitater og skovdrifts-

typer. De resulterende habitatpræferencer anvendes til at målrette

beskyttelsen af naturmæssigt særlig værdifuld skov på Naturstyrelsens

arealer med henblik på at tilgodese de truede arters behov - fx ved

beskyttelse som urørt skov eller ved speciel naturvenlig drift.

Ad. 3: Prioriteret katalog over anbefalet indsats:

Projektet vil dermed

som noget nyt sammenkæde prioriteringsanalyser for biodiversitet

på det overordnede niveau (10 x 10 km felter) eller andre storskala

geograﬁske enheder, med konkret og operationel forvaltning på lokal

skala i form af et prioriteret katalog over anbefalet indsats i konkrete

områder og naturtyper. Dermed bliver det direkte

relevant for selve den praktiske naturforvaltningsind-

sats ude i virkeligheden, som Naturstyrelsen står for.

Det prioriterede katalog over anbefalede supple-

rende indsatser for truede arter kan anvendes ved

udmøntning af et evt. nyt nationalt skovprogram,

herunder en evt. revision af områder udpeget i med-

før af naturskovstrategien, og bruges til prioriteringer

i det omfang, Naturstyrelsen får ressourcer til sup-

plerende indsatser.

Af øvrige aktiviteter kan nævnes, at Naturstyrelsens

praktikere involveres og informeres for at opnå hur-

tig effekt ude i praksis og for at inddrage de mange

erfaringer, som ikke kan ﬁndes i litteraturen. Det

vil ske ved interne møder, workshops og ved delta-

gelse i ekskursioner i bl.a. PROSILVA netværket for

skovbrugere, som arbejder med naturnær skovdrift i

praksis. Endvidere skal der selvfølgelig skrives forsk-

ningsartikler ud fra projektet.

Skovgæster ved en storbladet lind i

Bolderslev Skov. Visse truede arter kan

have habitatpræference for skovpartier

med sjældne træarter eller for urørt skov.

Hvad siger 2020 målene mere

konkret?

For EU er biodiversitetsstrategiens mål nr. 1 det afgø-

rende i relation til projektet. Det handler om inden

2020 at forbedre status for Natura 2000 naturtyper,

arter og fugle, dvs. naturtyperne og arter på habitat-

direktivets bilag 2, 4 og 5, samt fugle omfattet af fug-

lebeskyttelsesdirektivet - dvs. alle vilde fuglearter.

Tabel 2 s. 30 giver et overblik over måltal og sta-

tus. Den danske ændring for naturtyper (fra 17 til 5

pct. rapporteret gunstig) skyldes primært metode-

forandring mellem rapporteringerne i 2007 og 2013,

hvilket er uheldigt for sammenligninger.

BIODIVERSITET OG SKOV

NR. 3 2015

entum

378

Appendix III

EU-tallene er opgjort i de biogeograﬁske regioner og ikke pr. land.

Det medfører, at EU-tallene ikke direkte kan oversættes til nationalt

niveau, men i praksis vil være absolut minimum for hvert enkelt land,

idet alle, eller i hvert fald ﬂertallet af lande, er nødt til at opnå posi-

tiv status for, at EU samlet kan få det på regionsniveau.

FNs biodiversitetsmål for 2020 omfatter i relation til arter bl.a. at

undgå uddøen af truede rødlistearter inden 2020 og at forbedre arter-

nes status. Målet drejer sig, jf. teknisk baggrundsdokument COP/10/

INF/12/Rev.1, både om globalt og om nationalt truede arter.

Biodiversitet med samfundsværdi

Undersøgelserne relateret til de politiske 2020-mål vil hjælpe styrel-

sen til en indsats, som kan øge sandsynligheden for at nå målet om at

standse tilbagegangen i biodiversitet i år 2020.

Bl.a. De Økonomiske Råds undersøgelser har vist, at biodiversitet re-

præsenterer en stor samfundsværdi, der kan måles i milliarder af kroner.

Tab af arter og biodiversitet udgør tilsvarende et samfundsøkono-

misk tab, som det er relevant at bruge væsentlige ressourcer på at

forebygge alene af økonomiske grunde, men også pga. de etiske og

moralske aspekter af naturbeskyttelse samt menneskers muligheder

for at kunne opleve en rig og varieret natur.

Tabel 2. Breakdown af EUs 2020 mål nr. 1

sammenholdt med status i Danmark. Jf. EUs

impact assessment til strategien, dokument

SEC(2011) 540 ﬁnal af 3/5 2011, henholdsvis

»DK_National Summary for Article 17.pdf« af

3. juni 2014.

Forstkandidat Erik Buchwald er ErhvervsPhD studerende

og forstfuldmægtig ved Naturstyrelsen.

Andel med

gunstig status

Natura 2000 naturtyper

Natura 2000 arter

Fuglearter

Basistal

Stigning,

(2004-2007) 2020 mål afrundet

17 pct.

52 pct.

33 pct.

25 pct.

80 pct.

100 pct.

50 pct.

DK status

2007

17 pct.

24 pct.

Ej opgjort

DK status

2013

5 pct.

30 pct.

70 pct.

Skovbryn med en blanding af buske og træarter samt dødt ved er et andet

eksempel på en habitat, som mange truede arter har præference for.

entum

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BIODIVERSITET OG SKOV