SEANSE

Cumulative displacement impacts on seabirds

Prepared for Bundesamt für Seeschiffahrt und Hydrographie

Represented by Ms Marie Dahmen, Advisor

20 January 2020

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certified by Bureau Veritas to comply with ISO 9001 (Quality Management)

© DHI. All rights reserved. The document may not be reproduced or transmitted in any form or by any means, in part or in full, outside the recipient’s

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SEANSE

Cumulative displacement impacts on seabirds

Authors Florence Cuttat, Henrik Skov

Approval date 20/01/2020

Revision 02

Classification Public

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CONTENTS

1 Introduction ................................................................................................................. 3

2 Methods ....................................................................................................................... 3

3 Results ........................................................................................................................ 5

4 Discussion .................................................................................................................. 9

5 References ................................................................................................................ 10

Appendices ................................................................................................................................. 12

Appendix A – Displacement calculations by wind farm ........................................................... 13

FIGURES Figure 1 Scenario-2 wind farms included in the assessment ........................................................................ 3 Figure 2 Close-up of the wind farm polygons within the Hollandse Kust Zuid Holand cluster ........................ 4 Figure 3 Example of intersection between the wind farm polygons and seabird density grids ....................... 4 Figure 4 Displaced numbers of Red-throated Divers per wind farm .............................................................. 6 Figure 5 Displaced numbers of Common Guillemots per wind farm .............................................................. 6 Figure 6 Comparison between densities before/after displacement of Red-throated Divers .......................... 7 Figure 7 Comparison between densities before/after displacement of Common Guillemots .......................... 8 Figure 8 Close up comparison between densities before/after displacement of Common Guillemots in

the region of Horns Rev, Denmark ............................................................................................ 8

TABLES Table 1 Estimated numbers of displaced Red-throated Divers and Common Guillemots per country ............ 5

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1 Introduction

As part of the feasibility study on methods and models for the assessment of cumulative impacts

from offshore wind farm development under the SEANSE project, cumulative displacement

impacts have been assessed for the two target species: Red-throated diver Gavia stellata and

common guillemot Uria aalge. The calculations were conducted using updated post-construction

data on displacement ranges of the two species and information on planned offshore wind farms

in the North Sea until 2030 (SEANSE Scenario 2). The focus was put on the spring season for

the red-throated diver (February-March) and on the winter season for the common guillemot

(December-January), as densities are highest in these seasons. Although the red-throated diver

is assessed at the species level, the aerial and ship-based seabird survey data did not allow for

separation of most sightings of red-throated and black-throated divers, and hence the results

should be seen as covering both species.

2 Methods

The Scenario-2 wind farm GIS data were used as a basis for the displacement calculations. The

following geo-processes were undertaken using scripts in Python 3.7.0 with Spyder 3.3.6:

For each windfarm:

• Set buffer and OWF area

• Intersect with selected bird densities (buffer, OWF)

• Multiply density by area and factor of displacement

• “Dissolve” and sum the individuals displaced

• Add individuals displaced for buffer and OWF

An overview of the SEANSE Scenario-2 wind farms included in the assessment is shown in Figure

1, and a close-up of wind farm polygons within the Hollandske Kust Zuid Holand cluster is shown

in Figure 2.

Figure 1 Scenario-2 wind farms included in the assessment

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Figure 2 Close-up of the wind farm polygons within the Hollandse Kust Zuid Holland cluster

Using the KEC-2018 seabird density data, which consists of interpolated survey data from

European Seabirds At Sea (ESAS) and aerial survey data from the Dutch sector 1991-2017,

displacement calculations of the two target species were automated using Python scripts at a

resolution of 5 km2. The cumulative number of displaced birds was estimated by removing the

displaced proportion of birds from the displacement radii around all wind farms installed annually

in the North Sea by 2030.

The seabird density grid contained bimonthly bird densities for 10 different bird species.

North Sea densities for divers (Euring 59 and 710) were extracted for season 4 (Feb-Mar) and for

common guillemot (Euring 6340) for season 3 (Dec-Jan) and processed separately as

GeoDataFrames (GDF). An example of the intersected wind farm polygon and seabird density

grid is shown in Figure 3.

Figure 3 Example of intersection between the wind farm polygons and seabird density grids

The displacement calculations for the red-throated diver were undertaken using a displacement

range around the perimeter of the wind farms, and two series of calculations were undertaken.

Calculations were made both with 100% displacement within the wind farm and within the 5.5 km

buffer following the findings of Garthe et al. (2018) in the German EEZ of the North Sea and with

99% within the offshore wind farm and 50% in the 5.5 km buffer as indicated by Petersen et al.

(2014) from the post-construction monitoring at Horns Rev 2 in the Danish part of the North Sea.

A buffer zone of 5.5 km was created around each windfarm using the Geopandas method.

The common guillemot displacement levels and ranges are 75% displacement within the offshore

wind farm and 50% in the 2 km buffer based on the findings of Heinänen & Skov (2018) from the

post-construction monitoring at offshore wind farms in the Dutch sector of the North Sea.

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The selected GDFs were clipped to the wind farms and/or buffer polygons using GeoPandas’s

“overlay” method as intersection. The number of individuals displaced was calculated by

multiplying the density in each polygon by the area and the displacement factor as described in

the different scenarios. The final number of birds displaced in each windfarm + buffer area was

calculated using the “dissolve” method combined with the sum as aggregation function for

attributes. The GeoPandas “difference” method was used to separate the buffer only zone to the

windfarm. The buffer and the windfarm areas were then processed separately using the same

procedure as above with their respective displacement factors. The addition of birds displaced in

the buffer and in the windfarm gave the total amount of birds displaced per windfarm site.

3 Results

The results of the displacement scenarios are summarised per country in Table 1. Detailed results

are found in Appendix A. The estimated total number of displaced divers due to wind farms in the

North Sea during the spring season by 2030 was 12,439 in displacement scenario 1 and 7,684 in

displacement scenario 2 (Table 1). The difference in the estimated displacement impact between

the two scenarios is equivalent to 38.2%. The estimated total number of displaced guillemots due

to wind farms in the North Sea during the winter season by 2030 was 163,169 (Table 1).

The distribution of the estimated numbers of displaced divers and guillemots are displayed in

Figure 4 and Figure 5. Large numbers of divers are displaced within the main habitats used by

divers in the German Bight, off the Dutch coast and in the offshore sectors of the estuaries of the

Thames and the Wash. Contrary to this, the highest numbers of guillemots are displaced from

areas in proximity to major breeding colonies in the Moray Firth and Firth of Forth. For both species

it is seen that the estimated number of displaced birds is also related to the size of the wind farm

polygon.

Comparisons of the densities of divers and guillemots in the North Sea before and after

displacement are mapped in Figure 6, Figure 7 and Figure 8. In diver displacement scenario 1 the

change in diver densities is obviously very clear with all birds being removed from the wind farm

perimeters as well as from the buffer zones. In displacement scenario 2 some divers remain in

the buffer zones, although in areas of intense wind energy development the difference between

wind farm and buffer zones is blurred by the fact that some buffer zones extend into wind farms.

Table 1 Estimated numbers of displaced Red-throated Divers and Common Guillemots per country

Country Divers scenario 1 (100% displacement in the wind farm and 5.5 km buffer)

Divers scenario 2 (99% displacement in the wind farm and 50% in 5.5 km buffer)

Common Guillemot (75% displacement in the wind farm and 50 % in 2 km buffer)

BE 187 105 4,676

DE 4,894 2,627 15,581

DK 4,426 3,241 7,686

NL 343 194 18,729

UK 2,589 1,517 116,495

Total 12,439 7,684 163,169

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Figure 4 Displaced numbers of Red-throated Divers per wind farm

Figure 5 Displaced numbers of Common Guillemots per wind farm

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Figure 6 Comparison between densities before/after displacement of Red-throated Divers

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Figure 7 Comparison between densities before/after displacement of Common Guillemots

Figure 8 Close up comparison between densities before/after displacement of Common Guillemots in the region of Horns Rev, Denmark

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4 Discussion

Displacement of seabirds during construction and operation of offshore wind farms is widely

recognized as one of the main negative impacts on wildlife from this emerging industry (Dierschke

et al. 2016). Regulatory requirements responding to potential displacement impacts include short-

term (focused on construction period) and long-term (focused on operational period) monitoring

with the aims to validate predictions made in the environmental impact assessment, detect any

unforeseen impacts and/or ensure compliance with measures identified to mitigate significant

impacts (MMO 2014). Although a variety of monitoring approaches and methods is applied during

construction and post-construction monitoring from offshore wind farms, they all share the

statistical challenge of detecting a potentially small displacement effect in the presence of seabird

movements and the dynamics of marine habitats. In complex and dynamic habitats like the ones

typically found in the development areas for offshore wind in the North Sea, a spatially explicit

model design is preferred which includes all the factors causing the large variability and account

for any unexplained spatial autocorrelation (Perez-Lapena 2010). Yet, even with long-term

monitoring data and with a considerable spatial coverage the challenge remains to disentangle

the displacement effect from natural variability in the abundance of seabirds at the site of the wind

farm as the effect of changing habitat may exceed the displacement effect. As a consequence,

the evidence of the wind farm induced displacements of seabirds experienced today in the North

Sea and which is likely to take place in the future is precarious.

Although model-based assessment of monitoring data on seabirds are increasingly used in

relation to offshore wind farms (e.g. MRSea Package in R

https://github.com/lindesaysh/MRSea/releases/tag/v1.0-beta), confounding effects of wind farm

and dynamic oceanographic habitat features on local seabird abundance are typically not

accounted for, causing a risk for ambiguous monitoring results prone both to potential type I or II

errors. In shelf environments local animal abundance typically changes over the scale of less than

one day (Markones et al. 2008, Skov & Thomsen 2008). Hence, taking account of such short-term

changes in local oceanography and its effect on seabird distribution is a key constraint for

detecting the actual displacement taking place. Information on key habitat features like

hydrographic fronts and eddies enhance the probability for prey encounters by seabirds and have

to be incorporated in the model in very high temporal resolution. As the seabird distribution data

used for this assessment are interpolated mean values of survey data collected over a 25-year

period these data are inadequate for accurately estimating the degree of cumulative displacement

which is likely to take place. Using mean long-term densities for estimating cumulative

displacement impact may result in overestimation outside patches of higher densities and

underestimation inside these patches. In addition, it should be pointed out that this assessment

only estimated the numbers of seabirds displaced without considering the change in densities of

seabirds in areas surrounding the wind farms caused by the displacement.

With respect to red-throated divers, the issue of habitat dynamics is less of a problem due to large-

scale displacement impacts. However, there is a high degree of uncertainty regarding the level of

displacement, although there is general agreement that the species seems more sensitive than

other seabirds to the presence of wind farms. Adding to this, there is a complete lack of

understanding of the underlying process behind the displacement, i.e. answering the question

whether the displacement is caused by a behavioural response by the divers or by a change in

prey availability. Garthe et al. (2018) found on the basis of post-construction aerial and ship-based

surveys that the divers seemed to be entirely (100%) displaced within the wind farms as well as

within a 5.5 km buffer. The evidence from another important area for divers at Horns Rev in the

Danish part of the North Sea Petersen et al. (2014) found a 99% displacement within the Horns

Rev 2 wind farm and 50% in a 6 km buffer. Hence, the two displacement scenarios simulated for

the red-throated divers express the range of uncertainty regarding the scale of displacement

impact on this species. Although habitat dynamics are less likely to have biased the assessment

of cumulative displacements impacts on divers by 2030 it should be noted that neither the

assessment by Garthe et al. nor by Petersen et al. took the variability of the local oceanography

between the field surveys into account. Estimation of the displacement of common guillemots is

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more problematic due to the limited scale of displacement. The displacement rates used in this

assessment, i.e. 75% displacement in the wind farm and 50% in the 2 km buffer were based on

the findings of Heinänen & Skov (2018) who analysed long-term monitoring data on the Dutch

shelf incorporating the oceanographic variability experienced during each survey campaign. The

result contrasts those of Vallejo et al. (2017) and Leopold (2018) who reported

a lack of displacement impact on the species when analysing pre- and post-construction

monitoring data irrespective of habitat variability. Although we have used displacement rates from

a monitoring study applying a dynamic modelling approach the estimates of cumulative

displacement effects were based on static mean densities and should therefore only be regarded

as indicative.

This feasibility study has highlighted the need for more large-scale data on seabird distrution and

abundance and inclusion of dynamic habitat data into the modelling framework in order to

decrease the probability of both type I and type II errors in the assessments of displacement

impacts. At the same time, it should be stressed that displacement estimates made without a

dynamic modelling framework should be used with care for planning and impact assessments

associated with OWFs. The need for integration of dynamic habitat data is evidently important

both in relation to determination of displacement scale and in relation to identification of sensitive

habitats. For many seabird species for which the scale of displacement appears to be discrete

and certainly below the scale of distribution changes due to oceanographic variability the

avoidance of sensitive habitats is most likely the most efficient planning approach. Hence,

establishing strong evidence for the location of sensitive habitats could be seen as a priority in

the attempt to reduce the risk of large displacement impacts on seabirds.

5 References

Dierschke, V., Furness, R.W., Garthe, S. 2016. Seabirds and offshore wind farms in European

waters: Avoidance and attraction. Biol. Conserv. 202: 59-68.

Garthe, S., Schwemmer, H., Müller, S., Peschko V, Markones, N., Mercker, M. 2018.

Seetaucher in der Deutschen Bucht: Verbreitung, Bestände und Effekte von Windparks. Bericht

für das Bundesamt für Seeschifffahrt und Hydrographie und das Bundesamt für Naturschutz.

Heinänen, S & Skov, H. 2018. Offshore Windfarm Eneco Luchterduinen Ecological monitoring of

seabirds. T3 (Final) report. Commissioned by Eneco. DHI.

Leopold M.F., 2018. Common Guillemots and offshore wind farms: an ecological discussion of

statistical analyses conducted by Alain Zuur (WOZEP Birds-1). Wageningen, Wageningen

Marine Research (University & Research centre), Wageningen Marine Research report

C093/18.

Markones, N., Garthe, S., Dierschke, V., Adler. S. 2008. Small scale temporal variability of

seabird distribution patterns in the south-eastern North Sea. In: Wollny-Goerke K, Eskildsen K

(eds) Marine mammals and seabirds in front of offshore wind energy. MINOS—Marine warm-

blooded animals in North and Baltic Seas. Teubner, Wiesbaden, p 115–140.

MMO. 2014. Review of post-consent offshore wind farm monitoring data associated with licence

conditions. A report produced for the Marine Management Organisation, pp 194. MMO Project

No: 1031. ISBN: 978-1-909452-24-4.

Pérez-Lapenã, B. K. Wijnberg, M., Hulscher, S. J. M. H & Stein, A. 2010. Environmental impact

assessment of offshore wind farms: a simulation-based approach. Journal of Applied Ecology

47: 1110–1118.

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Petersen, I.K., Nielsen, R.D. & Mackenzie, M.L. 2014. Post-construction evaluation of bird

abundances and distributions in the Horns Rev 2 offshore wind farm area, 2011 and 2012.

Aarhus University, Aarhus.

Skov, H. & Thomsen, F. 2008. Resolving fine-scale spatio-temporal dynamics in the harbour

porpoise Phocoena phocoena. Mar Ecol Prog Ser 373:173–186.

Vallejo, G.C., Grellier, K., Nelson, E.J., 2017. Responses of two marine top predators to an

offshore wind farm. Ecol Evol. 2017;7:8698–8708. https://doi.org/10.1002/ece3.3389.

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Appendices

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Appendix A – Displacement calculations by wind farm

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Project Country Divers scenario 1

Divers scenario 2

Common Guillemot

Aberdeen Offshore Windfarm (EOWDC) UK 0 0 596

Alpha Ventus Nord DE 0 0 155

Alpha Ventus Süd DE 0 0 157

Amrumbank West DE 1,538 837 238

BARD Offshore 1 DE 234 138 70

Beatrice UK 0 0 15,760

Belwind BE 8 5 168

Belwind Alstom Haliade Demonstration BE 4 2 50

Blyth Offshore Wind Demonstration site UK 0 0 871

Borkum Riffgrund I DE 0 0 400

Borkum Riffgrund II DE 3 1 457

Borkum Riffgrund West 1 DE 25 14 1,478

Borkum Riffgrund West 2 DE 33 17 1,498

Borkum West II Phase 1 DE 3 1 423

Borkum West II Phase 2 DE 0 0 831

Borssele 1 NL 11 6 504

Borssele 2 NL 36 19 658

Borssele III NL 9 6 686

Borssele IV NL 5 3 808

Borssele V NL 1 1 61

Butendiek DE 158 86 240

DanTysk DE 226 127 919

Deutsche Bucht DE 81 42 13

Dogger Bank - Creyke Beck A UK 0 0 1,016

Dogger Bank - Creyke Beck B UK 0 0 1,319

Dudgeon UK 18 10 39

East Anglia One UK 26 15 889

East Anglia ONE North UK 28 18 1,170

East Anglia Three UK 1 0 4,792

East Anglia TWO UK 22 13 692

EnBW He Dreiht DE 12 6 215

EnBW Hohe See DE 0 0 342

Eneco Luchterduinen NL 1 0 533

Fairy Bank 1 BE 13 8 486

Fairy Bank 2 BE 15 9 781

Fairy Bank 3 BE 10 6 698

Firth of Forth UK 0 0 19,000

Galloper UK 41 24 207

Gemini East NL 8 4 1,026

Gemini West NL 30 22 741

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Project Country Divers scenario 1

Divers scenario 2

Common Guillemot

Global Tech 1 DE 8 4 432

Gode Wind 03 DE 293 151 443

Gode Wind 04 DE 204 105 281

Gode Wind 1 and 2 DE 176 95 1,346

Greater Gabbard UK 33 20 192

Gunfleet Sands Demonstration Project UK 141 72 31

Gunfleet Sands I + II UK 211 113 70

Hollandse Kust (west) NL 6 3 2,582

Hollandse Kust Noord (zoekgebied) NL 86 45 2,831

Hollandse Kust Zuid Kavel 1 NL 3 1 1,033

Hollandse Kust Zuid Kavel 2 NL 14 9 603

Hollandse Kust Zuid Kavel 3 NL 18 12 624

Hollandse Kust Zuid Kavel 4 NL 7 4 846

Horns Rev 1 DK 596 327 46

Horns Rev 2 DK 396 220 71

Horns Rev 3 DK 347 229 195

Horns Rev Reserved Area DK 3,036 2,439 7,127

Hornsea Project Four UK 0 0 9,272

Hornsea Project One UK 0 0 4,693

Hornsea Project Three UK 0 0 7,122

Hornsea Project Two UK 0 0 4,174

Humber Gateway UK 1 1 94

Hywind 2 Demonstration UK 0 0 49

IJmuiden Ver NL 1 1 2,956

Inch Cape UK 0 0 1,319

Inner Dowsing UK 101 53 7

Kaskasi II DE 618 325 173

Kentish Flats 1 UK 77 40 22

Kentish Flats 2 UK 79 41 25

Kincardine Offshore Windfarm Project UK 0 0 1,173

Lincs UK 198 111 22

London Array 1 UK 423 271 409

Lynn UK 90 47 4

Meerwind SüdOst DE 64 36 109

Merkur Offshore DE 0 0 1,126

Moray Firth Eastern Development Area UK 0 0 14,130

Moray Firth Western Development Area UK 0 0 11,499

N-3.5 DE-tender 2025 DE 0 0 390

N-3.6 DE-tender 2024 DE 0 0 282

N-3.7 DE-tender 2026 DE 168 88 556

N-3.8 DE-tender 2022 DE 0 0 499

N-6.6 DE-tender 2026 DE 24 12 73

N-6.7 DE-tender 2029 DE 367 194 20

N-7.2 DE-tender 2027 DE 4 2 467

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Project Country Divers scenario 1

Divers scenario 2

Common Guillemot

Neart na Gaoithe UK 0 0 1,424

Nobelwind BE 10 6 291

Nordergründe DE 12 6 3

Nordsee One DE 0 0 414

Nordsee Ost DE 213 107 155

Norfolk Boreas UK 143 103 2,562

Norfolk Vanguard UK 0 0 5,074

Norther BE 59 34 314

Northwester 2 BE 7 4 191

Northwind BE 6 4 178

OWEZ NL 63 33 526

OWP West DE 8 4 907

Prinses Amaliawindpark NL 10 5 240

Race Bank UK 385 228 145

RENTEL BE 8 4 349

Riffgat DE 41 21 119

Roenland DK 4

Sandbank 24 DE 195 107 305

Scroby Sands UK 0 0 83

SeaGreen Alpha UK 0 0 803

SeaGreen Bravo UK 0 0 1,061

Seastar BE 10 5 188

Sheringham Shoal UK 71 40 58

Teesside UK 0 0 55

Teesside A UK 0 0 1,866

Teesside B UK 0 0 1,453

Ten Noorden van de Waddeneilanden (2)

NL 34 22 1,472

Thanet UK 165 95 185

Thanet Extension UK 261 155 358

Thornton Bank I BE 3 1 151

Thornton Bank II BE 22 11 375

Thornton Bank III BE 6 3 224

THV Mermaid BE 4 2 233

Triton Knoll UK 76 45 511

Veja Mate DE 185 102 43

Vesterhav Nord DK 24 13 162

Vesterhav Syd DK 23 13 85

Westermost Rough UK 0 0 199

TOTAL 12,439 7,684 163,169