KEF, Alm.del - 2020-21 - Bilag 359: Henvendelse af 20/6-21 fra European Energy om udpegning af fuglebeskyttelsesområde i Smålandsfarvandet

Klima-, Energi- og Forsyningsudvalget 2020-21
KEF Alm.del Bilag 359
Offentligt

Technical Report Prepared for:

European Energy

Document Number:

EEN-01-01-TRP-001-1

Omø Syd Offshore Wind Farm Ornithology Impact

Bird Modelling - Final Report

KEF, Alm.del - 2020-21 - Bilag 359: Henvendelse af 20/6-21 fra European Energy om udpegning af fuglebeskyttelsesområde i Smålandsfarvandet

KEF, Alm.del - 2020-21 - Bilag 359: Henvendelse af 20/6-21 fra European Energy om udpegning af fuglebeskyttelsesområde i Smålandsfarvandet

Omø Syd Offshore Wind Farm Ornithology

Impact

Bird Modelling - Final Report

Revision History

Rev.

17-Jun-21

10-Jun-21

08-Jun-21

31-May-21

28-May-21

Date

Update following additional client comments

Update following client review

Client Issue

Internal review

Description

Checked

Approved

MHL

EPConsult Energies

Østerbrogade 212

2100 Copenhagen Ø

Denmark

T: +45 5359 3555

T: +44 20 7582 5555

www.ep-consult.dk

[email protected]

EEN-01-01-TRP-001-1

KEF, Alm.del - 2020-21 - Bilag 359: Henvendelse af 20/6-21 fra European Energy om udpegning af fuglebeskyttelsesområde i Smålandsfarvandet

Omø Syd Offshore Wind Farm Ornithology

Impact

Bird Modelling - Final Report

Table of Contents

4.1

4.2

4.3

4.4

4.5

4.6

4.7

5.1

5.2

5.3

5.4

Introduction ......................................................................... 7

Phase 1 Baseline Data: Birds ..........................................10

Phase 1 Baseline Data: Environment..............................12

Baseline Data Description ...............................................13

Seabed Sediment Type ...................................................... 13

Water Depth ....................................................................... 13

Seabed slope...................................................................... 14

Seabed current speed........................................................ 14

Seabed suitability index for filter-feeding bivalves (DHI) .. 15

Distance from shore........................................................... 15

Shipping activity (AIS 2016 all-vessel shipping data) ....... 16

Phase 2 Developing the Spatial Model ...........................23

Water depth ........................................................................ 24

Seabed slope...................................................................... 25

Seabed current speed........................................................ 26

Seabed suitability index for filter-feeding bivalves (DHI

model, Skov

et al.

2012) ................................................... 27

Distance from shore........................................................... 28

Shipping activity (AIS 2016 all-vessel shipping data) ....... 29

Spatial Autoregressive (SAR) Modelling ............................ 30

Model Validation ................................................................ 31

Phase 3: Application of the spatial model to test the

SPA alternatives ................................................................36

Summary and Conclusions ...............................................38

References.........................................................................39

5.5

5.6

5.7

5.8

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Omø Syd Offshore Wind Farm Ornithology

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Bird Modelling - Final Report

Table of Tables

Table Number and Title

Table 3-1: Summary of environmental data included in the spatial modelling analysis

Table 4-1: Seabed sediment type within the study area and within the proposed SPA.

Table 5-1: Summary of the results of the Spatial Autoregressive Modelling for Common Eider

Table 5-2: Summary of the results of the Spatial Autoregressive Modelling for Red-necked Grebe

Table 6-1: Common Eider population estimates and densities for each of the SPA options

Table 6-2: Red-necked Grebe population estimates and densities for each of the SPA options

Page

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Omø Syd Offshore Wind Farm Ornithology

Impact

Bird Modelling - Final Report

Table of Figures

Figure Number and Title

Figure 1-1: Bird Protected Areas

Figure 1-2: Aerial Bird Survey Transects, 2014-15 and 2020-21

Figure 4-1: Water Depths within the study area

Figure 4-2: Seabed slope within the study area

Figure 4-3: Seabed current speed within the study area

Figure 4-4: Filter-feeding bivalve suitability index (source: DHI) within the study area

Figure 4-5: Distance from the shore within the study area.

Figure 4-6: Shipping activity levels (source: AIS 2016) within the study area

Figure 4-7: Seabed Sediment Type

Figure 4-8: Bathymetry

Figure 4-9: Seabed Slope (degrees)

Figure 4-10: Seabed Current Speed (m/s)

Figure 4-11: Filter Feeding Bivalve Suitability Index

Figure 4-12: Shipping Activity (All Vessels AIS 2016)

Figure 5-1: Common Eider density across seabed sediment types

Figure 5-2: Red-necked Grebe density across seabed sediment types

Figure 5-3: Common Eider density and water depth.

Figure 5-4: Red-necked Grebe density and water depth

Figure 5-5: Common Eider density and seabed slope

Figure 5-6: Red-necked Grebe density and seabed slope

Figure 5-7: Common Eider density and mean bottom current speed

Figure 5-8: Red-necked Grebe density and mean bottom current speed

Figure 5-9: Common Eider density and DHI filter-feeding bivalve habitat suitability index

Figure 5-10: Red-necked Grebe density and DHI filter-feeding bivalve habitat suitability index

Figure 5-11: Common Eider density and distance from the shore

Figure 5-12: Red-necked Grebe density and distance from the shore.

Figure 5-13: Common Eider density and shipping activity index (AIS 2016)

Figure 5-14: Red-necked Grebe density and shipping activity index (AIS 2016)

Figure 5-15: Common Eider: density surface model

Figure 5-16: Red-necked Grebe: survey densities 2020-2021

Figure 5-17: Wind farm options and possible SPA exclusion zones

Page

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KEF, Alm.del - 2020-21 - Bilag 359: Henvendelse af 20/6-21 fra European Energy om udpegning af fuglebeskyttelsesområde i Smålandsfarvandet

Omø Syd Offshore Wind Farm Ornithology

Impact

Bird Modelling - Final Report

Introduction

The proposed Omø Syd wind farm lies within an area proposed as a Special Protection Area/Bird Protection

Area (SPA) for birds under the EU Birds Directive. This report investigates the possible exclusion of the wind

farm site from the SPA and whether that exclusion could be offset through alternative extensions to the

SPA, to provide quantitative evidence to assist in the overall assessment process. Common Eider and Red-

necked Grebe both occur in the area in internationally important numbers (>1% of the flyway population),

and hence meet the qualifying thresholds for designation.

The aim of this study is to compare the numbers of the two qualifying species (Common Eider

Somateria

mollissima

and Red-necked Grebe

Podiceps grisegena)

within three Special Protection Area (SPA) option

areas: (a) designation as currently proposed, (b) an alternative designation with the wind farm and a 1km

buffer excluded from SPA; and (3) the exclusion proposed in (b) plus additional extension options. The study

area for this work was defined to include all of the proposed SPA and its surrounds (see Figure 1-1), where

baseline bird and environmental data were available.

The work had three specific phases:

•

Phase 1 - Integration of all relevant bird survey data to estimate bird densities in grid squares within

the area surveyed.

Phase 2 - Spatial modelling with environmental factors (including seabed habitat type, water depth,

seabed topography, distance from shore and distance from potential sources of disturbance (e.g. main

shipping channels, designated hunting areas) - to understand the factors affecting bird distribution and

predict bird densities in unsurveyed areas.

Phase 3 - Application of the model to test the SPA alternatives.

•

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Omø Syd Offshore Wind Farm Ornithology

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Figure 1-1: Bird Protected Areas

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Omø Syd Offshore Wind Farm Ornithology

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Bird Modelling - Final Report

Figure 1-2: Aerial Bird Survey Transects, 2014-15 and 2020-21

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Phase 1 Baseline Data: Birds

The first phase of the work was to collate and process the available bird survey data to be used in the spatial

modelling exercise. This included:

•

Assessing the coverage of the baseline bird surveys in relation to the maximum extent of the proposed

SPA, to identify the gaps (in space and time).

Determining an appropriate size for a grid square overlay of the SPA, optimised for the scale of data

available on birds and habitats/environmental data. A grid square of 1x1km was chosen for the

modelling in line with the spatial scale of the study and the bird surveys. Grid squares were centred on

the survey transects.

Collation of the raw bird survey data and survey effort (transect routes, dates of surveys).

Standardising the data for survey effort.

Taking into account the seasonality of use of the area.

Calculation of an overall value of bird density for grid squares covered by the baseline surveys

–

expressed as mean count per unit area (with confidence intervals).

•

Two survey programmes have been undertaken by the developer of the Omø Syd wind farm. Both were

carried out by BioConsult, an international leader in seabird aerial survey techniques. The survey transects

are shown in Figure 1-2. Surveys comprised:

•

2020-21 - survey area included wind farm site plus buffer and most of the pSPA (a total area of about

1,050km

). Nine surveys were carried out over the whole of this area, at approximately 3-4 weeks

intervals between October 2020 and April 2021.

2014-15 - carried out to inform the original project EIA. These covered a reduced survey area of 550km

but the same survey methodology and transects as the 2020-21 surveys (within the reduced area).

Figure 1-2 shows the transect routes followed in each year. Five surveys were carried out in total,

between October and December 2014, and in March and April 2015.

•

All surveys were carried out as visual aerial surveys to maintain consistency over time and allow comparison

with the Danish national monitoring scheme (NOVANA), using a standard survey protocol. Two observers

were used, each covering one side of the aircraft. Flight altitude during surveys was standardised at 78m

(250 feet) at a cruising speed approximately 185 km (100 knots, Kahlert

et al.

2000), to enable rapid

approach to birds sitting on the sea, causing minimal disturbance but still allowing time for identification.

Birds were recorded to three standardised distance intervals out from the track-line taken by the aircraft:

49-174m (band A), 175-459m (band B) and 460m-1km (band C). Observers could not observe a band of

width 49 m on either side of the flight track since this was obscured by the body of the plane. The limits of

each band were determined using a clinometer which enabled the measurement of predetermined angles

below the horizontal measured abeam (at 78m altitude, the 49m cut-off is an angle of 60° from the

horizontal, 174m is 25° and 459m represents 10° declination). Position along the transect was recorded

by noting the precise time (to the nearest second) at which the bird or flock of birds is perpendicular to the

observer using watches synchronised with GPS. The time at which each observation along the transect was

made can be converted into a position by interpolating the GPS data and placing observations into a

predetermined distance from the track-line according to the band in which the bird was recorded.

No correction was made for diving/submerged birds, so the values should be treated as minimum

estimates, but this would not be expected to affect the proportionate distributions (and hence the key

conclusions of the report).

The visual aerial surveys raised two specific data issues:

•

Declining detectability from the survey platform - birds were recorded to distance bands from the survey

aircraft, but this was effectively only two bands for these surveys and so few birds were recorded in

band C - only 3% of Common Eider records and zero Red-necked Grebe were recorded in band C during

2020-21, and 6.5% of Common Eider in 2014-15. No Grebes were again recorded in band C in 2014-

15.

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Whilst it would be possible to adopt a simple approach and use only the Band A data to calculate

bird densities (Diederichs

et al

2002), this would mean discarding large amounts of data that were

recorded in Band B. Options were therefore explored to maximise the use of the data, use the

principles of distance sampling to estimate correction factors to incorporate the Band B data as

well as that from Band A (though with effectively only two bands DISTANCE software itself could

not be used). Distance correction factors were calculated for Band B for each survey, standardising

to the same overall density as Band A. Data from Band C was not used in the density estimates

(as this comprised only a very small proportion of the data set and was insufficient to generate

reliable estimates).

•

Identification to species level - whilst Common Eiders were readily recognisable from a survey plane, it

is difficult to separate Red-necked Grebe from another grebe species that occurs frequently in this

area, Great Crested Grebe. Whilst some observers were able to do this confidently, others recorded

only to ‘grebe species’.

Further examination of the survey data showed that there was a marked seasonal pattern of

occurrence of confirmed identifications of these species, that could be used to classify the

unidentified records. Overall, from the 2020-21 data, 91% of confirmed Great Crested Grebe

records occurred before 15 November, whilst 94% of Red-necked Grebe were seen after this date,

so this was used as a cut-off to classify the unidentified records to species.

Older data from the NOVANA monitoring and from the Smålandsfarvandet Offshore Wind Farm proposal

(which did not go ahead) were also collated for this study but time constraints (and issues with data

temporal and spatial coverage) meant that they could not be included explicitly in the modelling work. There

may, however, be an opportunity for further model testing and refinement using those data during the EIA

updating exercise. For this current exercise, the 2020-21 data have been used for the initial model building

as the most up-to-date baseline and with widest coverage, then that model has been tested with the 2014-

15 data over the reduced area that those earlier surveys covered.

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Phase 1 Baseline Data: Environment

Data on the environmental factors that could affect Common Eider and Red-necked Grebe distribution

across the study area were collated and are summarised in Table 3-1. They included:

•

seabed sediment type;

water depth;

seabed slope;

seabed current speed;

seabed suitability index for filter-feeding bivalves (DHI model - Skov

et al.

2012)

distance from shore;

shipping activity (AIS 2016 all-vessel shipping data);

hunting and hunting-free areas.

All were available across the whole of the study area.

Table 3-1: Summary of environmental data included in the spatial modelling analysis

Environmental Data

Seabed sediment type

Index of seabed suitability for filter-

feeding bivalves

Water Depth

Seabed slope;

Seabed current speeds

Shipping activity

Distance from shore

Designated hunting areas and

shooting-free refuges in the region;

Source

GEUS download

DHI model (Skov

et al.

2012)

COWI

DHI

AIS data 2016 (all

vessels)

https://www.soefartsstyrelsen.dk/sikkerhed-til-

soes/sejladsinformation/download-data/datasaet

Calculated from coastline

https://miljoegis.mim.dk/spatialmap?profile=miljoegis-

skovdrift

Derived from a DHI hydrodynamic/nutrient

flow/ecological productivity model that incorporates

data on tide, salinity, temperature and nutrient run-off

and loadings (Skov

et al.

2012)

Bathymetry by 50m grid

Calculated from bathymetry data to 50m grid

Comments

It had been intended to investigate the effect of hunting disturbance as an additional variable in the

modelling, but as no quantitative data were available on hunting levels or locations within the proposed

SPA, this was not investigated further.

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4.1

Baseline Data Description

Seabed Sediment Type

The seabed sediment types across the study area are shown in Figure 4-7. The total areas of each habitat

within the proposed SPA are given in Table 4-1.

Table 4-1: Seabed sediment type within the study area and within the proposed SPA.

Seabed sediment type

Mud and muddy sand

Muddy sand

Sand

Till/diamicton

Area (study area) km

140

127

338

% of study area

11%

21%

19%

50%

4.2

Water Depth

The study area bathymetry is shown in Figure 4-8, with deeper areas shown in blue and shallower in red.

The range of water depths across the survey area is shown in the histogram in Figure 4-1. Most of the study

area was relatively shallow, with only a small area of deeper water, predominantly in the western part of the

area (Figure 4-1).

Figure 4-1: Water Depths within the study area

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4.3

Seabed slope

Seabed slope was calculated on a 50m grid basis by COWI for input into the modelling. Slopes across the

study area are mapped in Figure 4-9. The large majority of the seabed across the study area was flat, with

only a small area of areas of higher slope (Figure 4-2).

Figure 4-2: Seabed slope within the study area

4.4

Seabed current speed

Figure 4-10 shows the distribution of seabed current speeds within the study area, and these are

summarised in Figure 4-3. Most areas had low current speeds, with areas of higher speed largely restricted

to the western part of the survey area.

Figure 4-3: Seabed current speed within the study area

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4.5

Seabed suitability index for filter-feeding bivalves (DHI)

The seabed suitability index for filter-feeding bivalves from the DHI model (Skov

et al.

2012) is mapped

across the study area in Figure 4-11 and summarised in Figure 4-4. Large parts of the study were classed

as very low suitability, with two separate zones of higher suitability (shown in Figure 4-11 as red/orange

areas).

Figure 4-4: Filter-feeding bivalve suitability index (source: DHI) within the study area

4.6

Distance from shore

Figure 4-5 summarises the distribution of distances from the shore across the study area. The most frequent

category was within 1km of the shore, and the majority within 5km, up to a maximum of 10km.

Figure 4-5: Distance from the shore within the study area.

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4.7

Shipping activity (AIS 2016 all-vessel shipping data)

Shipping activity within the study area (as determined from the 2016 AIS data) was concentrated within the

main shipping channel on the western edge (Figure 5-15 and Figure 4-6), with much less within the

proposed SPA.

Figure 4-6: Shipping activity levels (source: AIS 2016) within the study area

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Figure 4-7: Seabed Sediment Type

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Figure 4-8: Bathymetry

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Figure 4-9: Seabed Slope (degrees)

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Figure 4-10: Seabed Current Speed (m/s)

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Figure 4-11: Filter Feeding Bivalve Suitability Index

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Figure 4-12: Shipping Activity (All Vessels AIS 2016)

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Phase 2 Developing the Spatial Model

The first stage of the modelling was an initial exploration of the bird densities of each of the two key species

(Common Eider and Red-necked Grebe) and each environmental variable.

Figure 5-1 shows the mean (+ standard error) Common Eider density in each of the four main seabed

sediment types. The till/diamicton areas held the highest Eider densities, and the mud/sandy mud the

lowest, i.e., there were mode Eiders on hard substrates. Common Eider densities were statistically

significantly different between sediment types (Kruskal Wallis test: H=15.6, p=0.0014, n=509).

Figure 5-1: Common Eider density across seabed sediment types

There was no clear relationship between Red-necked Grebes density and seabed sediment type (Figure 5-

2), with no statistically significant difference (Kruskal Wallis test: H=6.7, p=0.08, n=509).

Figure 5-2: Red-necked Grebe density across seabed sediment types

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5.1

Water depth

Common Eider densities were higher in shallower water (<10m depth) and lowest below 20m (Figure 5-3).

This difference was statistically significant (Kruskal Wallis test: H = 140.4, p<0.0001, n = 509).

Figure 5-3: Common Eider density and water depth.

Red-necked Grebes were found at reduced density in the shallowest waters (0-5m), and there were no

records at all from deeper waters (more than 20m) (Figure 5-4). This difference was statistically significant

(Kruskal Wallis test: H = 27.5, p=0.0001, n = 509).

Figure 5-4: Red-necked Grebe density and water depth

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5.2

Seabed slope

There was a trend for higher Common Eider densities where seabed slopes were shallower, but this was

only a weak relationship. Densities in the 0.25-0.5 degree mean slope zone were significantly higher than

those in both flatter and more sloping areas (Kruskal Wallis test: H = 49.6, p<0.0001, n = 509).

Figure 5-5: Common Eider density and seabed slope

Red-necked Grebe density was also highest in the 0.25 - 0.5 degree class (Figure 5-6), and this was

statistically significant (Kruskal Wallis test: H = 12.5, p=0.028, n = 509).

Figure 5-6: Red-necked Grebe density and seabed slope

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5.3

Seabed current speed

Common Eider density was generally higher in areas of higher seabed current speed (possibly as a result of

the effect of current on food availability/feeding conditions (Kruskal Wallis test: H = 12.5, p=0.028, n =

509), Figure 5-7.

Figure 5-7: Common Eider density and mean bottom current speed

Red-necked Grebe density was generally similar across the range of seabed current speed, though with the

notable exception of the mid-range speed class which was significantly higher (Kruskal Wallis test: H = 14.2,

p=0.015, n = 509), Figure 5-8.

Figure 5-8: Red-necked Grebe density and mean bottom current speed

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5.4

Seabed suitability index for filter-feeding bivalves (DHI model, Skov

et al.

2012)

Common Eider density was higher in areas with higher DHI filter-feeding bivalve habitat suitability index

values (Figure 5-9), a result that was highly statistically significant (Kruskal Wallis test: H = 161.9,

p<0.0001, n = 509), and one that would be expected given that filter-feeding bivalves such as mussels

would be likely to form an important part of the Common Eider diet.

Figure 5-9: Common Eider density and DHI filter-feeding bivalve habitat suitability index

Red-necked Grebe density did not show any clear trends with the DHI filter-feeding bivalve habitat suitability

index at lower values (Figure 5-10), with no significant difference between classes (Kruskal Wallis test: H =

8.4, p=0.21, n = 509) (though this would be expected given that this species feeds predominantly on fish).

Figure 5-10: Red-necked Grebe density and DHI filter-feeding bivalve habitat suitability index

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5.5

Distance from shore

Common Eider densities were higher within 3km of the shore, and also, to a lesser extent in the further

(>7km from shore) zone (Figure 5-11). Differences were statistically significant (Kruskal Wallis test: H =

48.0, p<0.0001, n = 509).

Figure 5-11: Common Eider density and distance from the shore

Red-necked Grebe densities were generally higher further from the shore (Figure 5-12). Differences were

statistically significant (Kruskal Wallis test: H = 25.1, p=0.0007, n = 509).

Figure 5-12: Red-necked Grebe density and distance from the shore.

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5.6

Shipping activity (AIS 2016 all-vessel shipping data)

Common Eider densities were generally higher in areas of lower shipping activity, particularly in comparison

with the highest shipping level (Figure 5-13). Differences were statistically significant (Kruskal Wallis test:

H = 61.9, p<0.0001, n = 509).

Figure 5-13: Common Eider density and shipping activity index (AIS 2016)

Higher Red-necked Grebe densities were generally found in the mid-range of shipping activity, with fewer at

both lower and higher shipping levels (Figure 5-14), but these differences were not statistically significant

(Kruskal Wallis test: H = 7.1, p=0.31, n = 509).

Figure 5-14: Red-necked Grebe density and shipping activity index (AIS 2016)

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5.7

Spatial Autoregressive (SAR) Modelling

Spatial Autoregressive Modelling (StataCorp 2021) was used to analyse the bird and environmental data to

investigate how Common Eider and Red-necked Grebe abundance was related to these explanatory

environmental variables. It enabled the location of each grid square to be included in the modelling to take

into account any spatial autocorrelation in the data.

Table 5-1 summarises the results of the SAR for Eider. The model was highly statistically significant overall

(with a pseudo-r2 of 0.295, and p<0.0001), with the main environmental variables associated with

Common Eider density being the DHI filter-feeding bivalve suitability index (strong positive association;

coefficient = 1.32, p=0.005), and distance from the shore (with higher Common Eider densities further from

the shore; coefficient = -0.0047, p=0.015).

Table 5-1: Summary of the results of the Spatial Autoregressive Modelling for Common Eider

Spatial autoregressive model

GS2SLS estimates

Number of obs

Wald chi2(10)

Prob > chi2

Pseudo R2

509

= 150.96

= 0.0000

= 0.2954

EI_mean_2021

Depth_mean

Slope_mean

DHIFilterFeed_mean

Current_mean

ShoreDistance

AIS2016_mean

SedimentClass

Muddy sand

Sand

Till/diamicton

_cons

W_contig

EI_mean_2021

e.EI_mean_2021

Coefficient

Std. err.

P>|z|

[95% conf. interval]

.7369312

2.554448

1.322938

345.9383

-.0046661

-.0284401

.7817558

8.091822

.4742779

225.5702

.0019229

.047201

0.94

0.32

2.79

1.53

-2.43

-0.60

0.346

0.752

0.005

0.125

0.015

0.547

-.795282

-13.30523

.3933707

-96.17116

-.0084348

-.1209523

2.269144

18.41413

2.252506

788.0477

-.0008973

.0640721

-2.935428

-16.95449

-10.1745

7.409122

3.362433

12.11959

8.404718

5.804013

-0.87

-1.40

-1.21

1.28

0.383

0.162

0.226

0.202

-9.525675

-40.70846

-26.64744

-3.966533

3.654819

6.799479

6.298449

18.78478

.6254558

-.415968

.1658303

.1706669

3.77

-2.44

0.000

0.015

.3004343

-.750469

.9504773

-.0814669

Wald test of spatial terms:

chi2(2) = 14.23

Prob > chi2 = 0.0008

Note:

‘EI_mean2021’ = mean Common Eider density derived from the 2020-21 survey data; ‘Depth_mean’

= Mean sea depth;

‘Slope_mean = Mean seabed slope; ‘DHIFilterFeed_mean’ = mean filter-feeding

bivalve

suitability index (DHI); ‘Current_mean’ = mean bottom current speed; ‘ShoreDistance’ = ditnace of central

point of grid square to shore; ‘AIS2016_mean’ = mean shipping activity

(AIS 2016 data, all vessels),

‘SedimentNo’ = seabed habitat type, with classes given and tested against ‘Mud and sandy mud’ baseline;

W_contig details the spatial correlation matrix.

The SAR results for Red-necked Grebe are shown in Table 5-2. There was a much weaker relationship

between grebe density and the environmental data than there was for Common Eider (with a pseudo-r2

value of only 0.033), though the analysis did still identify a statistically significant relationship between

grebe density and the DHI filter-feeding bivalve suitability index (a weak negative association; coefficient =

-0.00704, p=0.004). The data for Red-necked Grebes were statistically problematic as well, as they

contained a high proportion of zero values (82% of surveyed grid squares held zero r Red-necked Grebes).

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Table 5-2: Summary of the results of the Spatial Autoregressive Modelling for Red-necked Grebe

Spatial autoregressive model

GS2SLS estimates

Number of obs

Wald chi2(10)

Prob > chi2

Pseudo R2

509

= 63.60

= 0.0000

= 0.0332

RNG_mean_2021

Depth_mean

Slope_mean

DHIFilterFeed_mean

Current_mean

ShoreDistance

AIS2016_mean

SedimentClass

Muddy sand

Sand

Till/diamicton

_cons

W_inv

RNG_mean_2021

e.RNG_mean_2021

Coefficient

Std. err.

P>|z|

[95% conf. interval]

.0100775

-.0195533

-.0070359

.7270698

9.14e-06

.0007597

.0098401

.0774475

.002432

.6814129

.0000123

.0007378

1.02

-0.25

-2.89

1.07

0.74

1.03

0.306

0.801

0.004

0.286

0.459

0.303

-.0092088

-.1713476

-.0118026

-.6084749

-.000015

-.0006864

.0293638

.132241

-.0022692

2.062614

.0000333

.0022058

.0530355

-.1312287

-.0702287

-.3485771

.0892147

.0951864

.0897957

.1064883

0.59

-1.38

-0.78

-3.27

0.552

0.168

0.434

0.001

-.1218222

-.3177906

-.246225

-.5572903

.2278932

.0553332

.1057675

-.1398639

3.233291

-1.557351

.7353747

1.34616

4.40

-1.16

0.000

0.247

1.791983

-4.195777

4.674599

1.081074

Wald test of spatial terms:

chi2(2) = 19.67

Prob > chi2 = 0.0001

5.8

Model Validation

The outputs from these models were tested initially against the 2020-21 data, to compare the model

predictions with the observed densities of each species. A further independent test of the predictions was

carried out with the 2014-15 data, to see how the model predictions compared with the observed densities

in that winter (only the 2020-21 data were used in the model development), though this could only be

carried out for the reduced survey area that was covered in that year.

Both the 2014-15 and the 2020-21 survey data were strongly correlated with the predicted densities for

Common Eider (r

= 0.44 and r

= 0.49 respectively, p<0.0001 and n=509 in both years). The predicted

values from the model matched well to the observed survey data, validating the model predictions.

For Red-necked Grebe the SAR model predictions were a poor fit for both the 2014-15 (r

= -0.089, p=0.05,

n=509) and the 2020-21 data (r

= 0.082, p=0.07, n=509), showing no statistically significant correlation.

The predicted values did not match well to the observed survey data, indicating that the model predictions

were not reliable for this species. This means that the model cannot be used to produce reliable predictions

of Red-necked Grebe densities outside the areas surveyed.

As a reliable model has been produced for Common Eider, this has been used to construct a density surface

model, using a combination of the observed data from the 2020-21 surveys (that covered a wider survey

area) and filling the gaps with the model-predicted densities. The results are shown in Figure 5-15.

The lack of a statistically reliable model for Red-necked Grebe using the environmental variables to predict

grebe density meant that an alternative approach had to be adopted. This was based on the assumption

that the surveyed grid squares within each zone were representative of Red-necked Grebe densities across

the whole of each zone. Population estimates and bird densities were calculated directly from the survey

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data (including correction for distance sampling where data had been collected across more than a single

distance band, as in the main analysis above). The Red-necked Grebe densities (derived directly from the

2020-21 aerial surveys, hence the gaps in coverage) are mapped in Figure 5-16.

Further analysis of the Red-necked Grebe data was undertaken to test if their numbers were consistent in

areas between years. If that were the case, then the grid square densities from the two years (2014-15 and

2020-21) would be expected to be correlated. There was statistically significant correlation between Red-

necked Grebe densities in 2014-15 and 2020-21 (r

=0.16, p=0.002, n=507), suggesting that they were

showing some preference for similar areas between years, but this relationship was weak, probably

reflecting variability in their primary food resource (fish).

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Figure 5-15: Common Eider: density surface model

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Figure 5-16: Red-necked Grebe: survey densities 2020-2021

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Figure 5-17: Wind farm options and possible SPA exclusion zones

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Phase 3: Application of the spatial model to test the

SPA alternatives

The spatial model was used to derive robust population estimates and measures of overall bird use for the

proposed Special Protection Area (pSPA), and for an alternative area with the Omø Syd wind farm excluded

from the pSPA. The excluded area also included a 1km buffer around the wind farm, incorporated to avoid

the possibility of disturbance effects into the SPA. Additional potential SPA extensions were also investigated

to see if they might offset the negative effect of the exclusion area on the SPA.

Three SPA options were therefore considered:

•

Option 1 - SPA as current proposed boundary

Option 2 - SPA with exclusion area around current wind farm location;

Option 3 - SPA with exclusion area and alternative potential SPA extension areas..

These options are mapped in Figure 5-17.

The aim of this phase of the work was to determine how many Common Eider and Red-necked Grebe each

of the SPA alternatives would support, and hence what the proportionate reduction would be in the SPA

Eider and Red-necked Grebe populations for the Option 2 and Option 3 alternatives.

The modelling data were also used to determine whether removing the wind farm area from the SPA makes

a material difference to the overall quality of the SPA, in terms of its ability to achieve its threshold

population and to deliver and maintain its conservation objectives (for each of the two alternative options).

The contribution from each of the four possible extension areas is shown in the Table.

Table 6-1 summarises the modelled Common Eider population sizes and densities within the pSPA options.

The key comparisons are the Common Eider populations for each of the options, and the proportionate

change that this represents from the current pSPA. Option 2, with the wind farm and a 1km buffer excluded

from the SPA would result in a reduced total SPA mean population (20,711 compared with 22,531), but

this would still represent about 91.9% of the current pSPA.

The alternative Option 3 with the wind farm exclusion but also with four potential extensions to the SPA

gave a predicted net gain to the Common Eider SPA population, with the updated SPA predicted to hold a

mean population of 23,960 (106.3% of the current pSPA). The contribution from each of the four possible

extension areas is shown in the Table.

Table 6-1: Common Eider population estimates and densities for each of the SPA options

Option

Option 1: current pSPA

Option 2: pSPA with Omø Syd Wind Farm exclusion

Effective loss

Extension 1 gain

Extension 2 gain

Extension 3 gain

Extension 4 gain

Total potential gain (1-4)

Option 3: All extensions combined + Option 2

Mean

population

22,531

20,711

1,820

766

908

1,500

3,249

23,960

33.6

712

Mean

density

33.3

34.2

Sample

size

677

605

3.4

% current

pSPA

100%

91.9%

-8.1%

+3.4%

+4.0%

+6.7%

+0.3%

+14.4%

106.3%

Table 6-2 summarises the estimated Red-necked Grebe population sizes and densities within the three

pSPA options. As for Common Eider, the key comparisons are the Red-necked Grebe populations for each

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of the three options, and the proportionate change that this represents from the current pSPA. Option 2,

with the wind farm and a 1km buffer excluded from the SPA would result in a reduced total SPA population

(145 compared with 165), which would represent about 87.9% of the current pSPA.

For reference, the threshold of international importance given in the pSPA documentation (Petersen

et al.

2016 and 2019) for Red-necked Grebe is 500 (though with the flyway population estimated at 30,000-

45,000). The Red-necked Grebe populations derived from the recent aerial surveys fall well below this

threshold. Option 2 reduced the grebe numbers to 87.9% of the current pSPA levels. From the information

provided in Petersen

et al

2016, 2019 and 2020, it appears to be being suggested that ship-based surveys

produce rather higher populations estimates for this species (with about 1,110-1,900 individuals reported

from surveys in 2014). With problems of species identification and this apparent under-recording of this

species from aerial surveys, it is difficult to draw any firm conclusions for Red-necked Grebe, though the

available evidence would suggest that the additional exclusion areas would reduce the grebe numbers

supported within the SPA by 12.1%.

Table 6-2: Red-necked Grebe population estimates and densities for each of the SPA options

Option

Option 1: current pSPA

Option 2: pSPA w/o current

Omo Syd WF

Mean count

165

145

Mean density

0.244

0.240

Standard error

0.042

0.046

Sample size

677

280

% current pSPA

100.0%

87.9%

The possible SPA extensions have the potential to support small numbers of Red-necked Grebe, and

probably sufficient to offset the reduction from excluding the wind farm from the SPA (given the large overall

total area in comparison with the area lost to the exclusion), but it was not possible to quantity this using

spatial modelling, as the model was not sufficiently reliable (primarily due to the limitations of the baseline

data).

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Summary and Conclusions

This study has provided a quantitative comparison of the numbers of the two qualifying species (Common

Eider and Red-necked Grebe) within the two SPA option areas that were being investigated: (a) the current

proposed designation, and (b) an alternative designation with the wind farm and a buffer excluded from

SPA. A third option was also identified during the study, with the wind farm exclusion and four possible SPA

extensions also considered to offset the effect of the exclusion area.

Analysis of the aerial bird survey data has enabled bird densities to be estimated across the survey area,

and, for Common Eider, this has been developed with a spatial autoregression model to predict bird

densities in areas of the SPA that have not been surveyed (on the basis of a range of environmental

variables including the suitability of the seabed conditions to support filter-feeding bivalves, distance from

shore, water depth, current speed, seabed sediment type and shipping activity).

Removing the wind farm area from the SPA (Option 2) made no material difference to the overall quality of

the SPA, in terms of its ability to achieve its threshold population and to deliver and maintain its conservation

objectives. The SPA would considerably exceed the threshold population of 9,800 individuals for all three

options. Comparing the proportionate change in Common Eider populations, the wind farm in its current

position (plus a 1km buffer) excluded from the SPA (Option 2) would result in a reduced total SPA population

to about 91.9% of the current pSPA. The alternative option with the wind farm exclusion but also the SPA

extensions (Option 3) would result in a net gain for the Common Eider SPA population to 106% of the current

pSPA. For Red-necked Grebe, Option 2, would result in a reduction to 87.9% of the current pSPA population.

Quantification of the benefits of the possible SPA extensions was not possible given the limitations of the

baseline survey data, but it is very likely that this combination of exclusion and extension areas would allow

the SPA Red-necked Grebe population to achieve its required conservation status (exceeding the threshold

for designation and meeting its conservation objectives).

The initial recommendation would be to exclude the current proposed wind farm site and a 1km buffer from

the pSPA, as shown in Figure 5-17. This would result in an 8.1% reduction in the SPA Common Eider

population, and a 12.1% reduction in its Red-necked Grebe population.

An alternative option was identified, to extend the SPA to include additional areas and offset the reduction

from the wind fam exclusion. Four possible SPA extensions were identified, which together could deliver a

6% increase in the SPA population of Common Eider in combination with the proposed wind farm exclusion.

Such an approach would also likely offset the reduction in the Red-necked Grebe population within the SPA

as a result of the proposed wind farm exclusion.

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Kahlert, J., Desholm, M., Clausager, I. & Petersen, I.K. 2000. Environmental impact assessment of an

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Petersen, IK 2020. Academic contribution regarding. designation of marine bird protection areas. Aarhus

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