ANDREW TONGUE SCCS15 POSTER
- 1. Offshore wind: The importance of bird density and breeding season definitions Andrew Tongue, RSPB Centre for Conservation Science, RSPB UK HQ, The Lodge, Sandy, Bedfordshire SG19 2DL andrew.tongue@rspb.org.uk 2. How was it researched? Using the data provided in Environmental Impact Assessment (EIA) documentation for six UK wind farms, collision risk outputs using peak densities were derived for three species (northern gannet Morus bassanus, lesser black-backed gull Larus fuscus and black-legged kittiwake Rissa tridactyla). The results were compared with those obtained by developers using mean densities. Developers’ definitions of the breeding season / length of colony occupation of SPA populations were referenced against those given in the standard ornithological literature. 4. What were the conclusions? Offshore wind is a new and rapidly-developing sector, with implications for cumulative seabird population impacts. Although there is arguably an urgent need for the development of decarbonised, sustainable energy sources, this research has pinpointed certain inadequacies within the offshore Environmental Impact Assessment process. It is arguable that the relative year-on-year stochasticity of seabird density seriously devalues density-based offshore collision risk models. It may also serve to complicate assessments of the post-construction impacts of wind farms on seabird populations. Offshore collision risk may be more accurately estimated in the future by the development of habitat use models incorporating a variety of factors, including hydrodynamics and weather patterns, perhaps replacing a measured density estimate with a modelled density estimate. Greater clarity and rigour is required when defining species’ breeding seasons (or colony occupation periods) for the purposes of attributing collision impacts to particular Special Protection Area populations. 3. What were the results? Figures 2,3 and 4 (right) compare differences in monthly collision risk modelling outputs derived via both mean and peak densities. The use of a peak density figure in collision risk modelling often generated significantly greater collision estimates compared with the use of a mean figure. Disparities were also found between some developers’ definitions of the breeding season for SPA populations and those given for the same species in the standard literature, as shown in Tables 1 and 2, below. This has implications in terms of how and whether birds recorded at certain times of the year should be apportioned to a particular SPA for collision risk modelling purposes. 1. What is the problem? Globally-unprecedented numbers of offshore wind turbines are being planned, proposed and constructed in the southern North Sea - an area hosting important numbers of several seabird species. Many of these species are long- lived and have low annual productivity. Avian collision risk at UK offshore wind farms is usually derived via a monthly mean density figure following two years of survey. This figure may be considerably lower than monthly peak density, therefore effectively excluding potentially significant numbers of birds from the Environmental Impact Assessment (EIA) process. Estimates of collision impacts attributable to Special Protection Area (SPA) breeding populations are influenced by the period of time considered to constitute the breeding season. Sometimes, developers’ definitions of this period have differed considerably from those given in the literature. This also has implications in terms of the number of birds considered to be at risk of collision. Figure 1: Lesser black-backed Larus fuscus graellsii, herring Larus argentatus argenteus, common Larus canus and black-headed gulls Chroicocephalus ridibundus flying in close proximity to wind turbines Figure 3: Northern gannet Morus bassanus: Comparison of estimated annual collision mortality between mean and peak densities at six UK offshore wind farms Figure 2: Lesser black-backed gull Larus fuscus: Comparison of estimated annual collision mortality between mean and peak densities at six UK offshore wind farms Figure 4: Black-legged kittiwake Rissa tridactyla: Comparison of estimated annual collision mortality between mean and peak densities at six UK offshore wind farms 5. Acknowledgements Benedict Gove (RSPB Centre for Conservation Science) and Claire Appleby (The Open University) supervised this research. Rhys Green and Matthew Carroll at the RSPB Centre for Conservation Science also provided important advice and insight. 6. Further information This research is summarised in further detail on the British Ornithologists’ Union website: http://www.bou.org.uk/offshore-wind/ Literature cited Band, W. (2012) Using a collision risk model to assess bird collision risks for offshore wind farms. Prepared for The Crown Estate as part of the Strategic Ornithological Support Services (SOSS) programme, project number: SOSS-02A. Banks, A.N., Clough, S., Dwyer, R., Lee, K., Rowell, H., Jowett, M., McCormack, D., McGovern, S., Barlow, E., Morris, K., and Rehfisch, M. (2012) East Anglia One Wind Farm: Environmental Statement Volume 2 Chapter 10 Ornithology, Marine and Coastal Appendices. Document 7.3.7b. Draft. APEM Ltd. Johnston, A., Cook, A.S.C.P., Wright, L.J., Humphreys, E.M. and Burton, N.H.K. (2014) ‘Modelling flight heights of marine birds to more accurately assess collision risk with offshore wind turbines’, Journal of Applied Ecology 51: 31-41. Cramp & Simmons (1977 – 1994) Birds of the Western Palearctic, Oxford University Press, Oxford. Background photograph: Brookings Institution Alan D. Wilson 0 50 100 150 200 250 0 50 100 150 200 250 0 5 10 15 20 25 30 35 40 0 5 10 15 20 25 30 0 200 400 600 800 1000 1200 0 50 100 150 200 250 Jan April July October Jan April July October Jan April July October Jan April July October Jan April July October Jan April July October Dark blue bars: Predicted collision mortality using peak density Pale blue bars: Predicted collision mortality using mean density (used by developers) b.)a.) c.) d.) e.) f.) Estimatedannualcollisionmortality(no.ofbirds) Henry Bucklow Spwickstrom.com 0 2 4 6 8 10 12 14 16 18 20 0 20 40 60 80 100 120 140 0 2 4 6 8 10 12 14 16 18 0 20 40 60 80 100 120 140 0 200 400 600 800 1000 1200 1400 1600 1800 a.) b.) c.) d.) e.) f.) Estimatedannualcollisionmortality(no.ofbirds) Jan April July October Jan April July October Jan April July October Jan April July October Jan April July October Jan April July October Dark blue bars: Predicted collision mortality using peak density Pale blue bars: Predicted collision mortality using mean density (used by developers) d.) e.) f.) Estimatedannualcollisionmortality(no.ofbirds) 0 5 10 15 20 25 30 35 40 45 50 0 50 100 150 200 250 300 0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 100 120 140 0 100 200 300 400 500 600 700 800 900 0 50 100 150 200 250 300 350 400 Jan April July October Jan April July October Jan April July October Jan April July October Jan April July October Jan April July October Dark blue bars: Predicted collision mortality using peak density Pale blue bars: Predicted collision mortality using mean density (used by developers) Figures 2-4 : In-flight densities expressed at a precautionary 98% avoidance rate using Option 2 of Band (2012) with Johnston et al. (2014)-modelled flight height distributions (best-estimate) across six offshore wind farms in the southern North Sea: a.) Galloper; b.) Triton Knoll; c.) Dogger Bank Creyke Beck A; d.) Dogger Bank Creyke Beck B; e.) East Anglia One; f.) Hornsea Project 1. Large array correction applied. a.) b.) c.) Ashley Cooper / ALAMY Table 1: Northern gannet Morus bassanus collision risk estimates for East Anglia One offshore wind farm, based on a breeding season defined by the developer (Banks et al., 2012). Month May June July August Collision rate (no. of birds) 0 0 1 1 Total annual predicted breeding season collision mortality: 2 birds Table 2: Northern gannet Morus bassanus collision risk estimates for East Anglia One offshore wind farm, based on a breeding season defined by Nelson cited in Cramp & Simmons (1977 – 1994). Month J F M A M J J A S O N Collision rate (no. of birds) 1 1 4 0 0 0 1 1 1 9 75 Total annual predicted breeding season collision mortality: 93 birds Figures for Tables 1 and 2 derived using a 99% avoidance rate with Option 2 of Band (2012) with Johnston et al. (2014)-modelled flight-height distributions (best estimate). Large array correction applied. Total annual predicted breeding season collision mortality: 2 birds Total annual predicted breeding season collision mortality: 93 birds