Understanding seabird behaviour at sea part 2: improved estimates of collision risk model parameters

Report detailing research using GPS tags to track Scottish seabirds at sea.

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6. References

Alerstam, T., Rosén, M., Bäckman, J., Ericson, P. G. P., & Hellgren, O. (2007). Flight Speeds among Bird Species: Allometric and Phylogenetic Effects. PLoS Biology, 5(8), e197. https://doi.org/10.1371/journal.pbio.0050197

Babcock, M., Aitken, D., Lloyd, I., Wischnewski, S., & Barratt. (2018). Flamborough and Filey Coast SPA Seabird Monitoring Programme 2018 Report.

Band, B. (2012). Using a collision risk model to assess bird collision risks for offshore wind farms. Retrieved August 23, 2017, from https://www.bto.org/sites/default/files/u28/downloads/Projects/Final_Report_SOSS02_Band1ModelGuidance.pdf

Band, W. (2012). Using a collision risk model to assess bird collision risks for offshore windfarms. SOSS, The Crown Estate, London, UK.

Band, W., Madders, M., & Whitfield, D. P. (2007). Developing field and analytical methods to assess avian collision risk at Wind Farms. In M. de Lucas, G. F. E. Janss, & M. Ferrer (Eds.), Birds and wind farms: risk assessment and mitigation. Madrid: Quercus.

Bivand, R., & Lewin-Koh, N. (2021). maptools: Handling Spatial Objects. R package version 1.1-2. Retrieved from https://cran.r-project.org/package=maptools

Bodey, T. W., Cleasby, I. R., Bell, F., Parr, N., Schultz, A., Votier, S. C., & Bearhop, S. (2018). A phylogenetically controlled meta-analysis of biologging device effects on birds: Deleterious effects and a call for more standardized reporting of study data. Methods in Ecology and Evolution, 9(4), 946–955. https://doi.org/10.1111/2041-210X.12934

Borkenhagen, K., Corman, A.-M., & Garthe, S. (2018). Estimating flight heights of seabirds using optical rangefinders and GPS data loggers: a methodological comparison. Marine Biology, 165(1), 17. https://doi.org/10.1007/s00227-017-3273-z

Brooks, M. E., Kristensen, K., van Bentham, K. J., Magnusson, A., Berg, C. W., Nielsen, A., … Bolker, B. M. (2017). glmmTMB Balances speed and flexibility among packages for zero-inflated Generalized Linear Mixed Modelling. The R Journal, 9, 378–400.

Chamberlain, D. E., Rehfisch, M. R., Fox, A. D., Desholm, M., & Anthony, S. J. (2006). The effect of avoidance rates on bird mortality predictions made by wind turbine collision risk models. Ibis, 148, 198–202.

Chivers, L. S., Lundy, M. G., Colhoun, K., Newton, S. F., Houghton, J. D. R., & Reid, N. (2012). Foraging trip time-activity budgets and reproductive success in the black-legged kittiwake. Marine Ecology Progress Series, 456, 269–277. https://doi.org/10.3354/MEPS09691

Cleasby, I. R., Morrissey, B. J., Bolton, M., Owen, E., Wilson, L., Wischnewski, S., & Nakagawa, S. (2021). What is our power to detect device effects in animal tracking studies? Methods in Ecology and Evolution, 12(7), 1174–1185. https://doi.org/10.1111/2041-210X.13598

Cleasby, I. R., Wakefield, E. D., Bearhop, S., Bodey, T. W., Votier, S. C., & Hamer, K. C. (2015). Three-dimensional tracking of a wide-ranging marine predator: flight heights and vulnerability to offshore wind farms. Journal of Applied Ecology, 52(6), 1474–1482. https://doi.org/10.1111/1365-2664.12529

Clewley, G. D., Clark, N. A., Thaxter, C. B., Green, R. M., Scragg, E. S., & Burton, N. H. K. (2021). Development of a weak-link wing harness for use on large gulls (Laridae): methodology, evaluation and recommendations. Seabird, 33, 18–34.

Cook, A.S.C.P., Ward, R. M., Hansen, W. S., & Larsen, L. (2018). Estimating Seabird Flight Height Using LiDAR. Scottish Marine and Freshwater Science, 9(14). Retrieved from https://data.marine.gov.scot/dataset/estimating-seabird-flight-height-using-lidar

Cook, A S C P, Humphreys, E. M., Masden, E. A., & Burton, N. H. K. (2014). The Avoidance Rates of Collision Between Birds and Offshore Turbines. Edinburgh. Retrieved from http://www.gov.scot/Resource/0046/00464979.pdf

Cook, Aonghais S.C.P., Humphreys, E. M., Bennet, F., Masden, E. A., & Burton, N. H. K. (2018). Quantifying avian avoidance of offshore wind turbines: Current evidence and key knowledge gaps. Marine Environmental Research, 140, 278–288. https://doi.org/10.1016/J.MARENVRES.2018.06.017

Corman, A. M., & Garthe, S. (2014). What flight heights tell us about foraging and potential conflicts with wind farms: a case study in Lesser Black-backed Gulls (Larus fuscus). Journal of Ornithology, 155, 1037–1043. https://doi.org/10.1007/s10336-014-1094-0

Evans, T. J., Young, R. C., Watson, H., Olsson, O., & Åkesson, S. (2020). Effects of back-mounted biologgers on condition, diving and flight performance in a breeding seabird. Journal of Avian Biology, 51(11). https://doi.org/10.1111/JAV.02509

Fijn, R. C., & Gyimesi, A. (2018). Behaviour related flight speeds of Sandwich Terns and their implications for wind farm collision rate modelling and impact assessment. Environmental Impact Assessment Review, 71, 12–16. https://doi.org/10.1016/J.EIAR.2018.03.007

Forsythe, W. C., Rykiel, E. J., Stahl, R. S., Wu, H., & Schoolfield, R. M. (1995). A model comparison for daylength as a function of latitude and day of year. Ecological Modelling, 80(1), 89–75.

Furness, R. W., Garthe, S., Trinder, M., Matthiopoulos, J., Wanless, S., & Jeglinski, J. (2018). Nocturnal flight activity of northern gannets Morus bassanus and implications for modelling collision risk at offshore wind farms. Environmental Impact Assessment Review, 73, 1–6. https://doi.org/10.1016/J.EIAR.2018.06.006

Furness, R. W., Wade, H. M., & Masden, E. A. (2013). Assessing vulnerability of marine bird populations to offshore wind farms. Journal of Environmental Management, 119, 56–66. https://doi.org/10.1016/j.jenvman.2013.01.025

Garriga, J., Palmer, J. R. B., Oltra, A., & Bartumeus, F. (2016). Expectation-Maximization Binary Clustering for Behavioural Annotation. PLOS ONE, 11(3), e0151984. https://doi.org/10.1371/JOURNAL.PONE.0151984

Garthe, S, & Hüppop, O. (2004). Scaling possible adverse effects of marine wind farms on seabirds: developing and applying a vulnerability index. Journal of Applied Ecology, 41(4), 724–734.

Garthe, Stefan, & Huppop, O. (2004). Scaling possible adverse effects of marine wind farms on seabirds: Developing and applying a vulnerability index. Journal of Applied Ecology, 41(4), 724–734. https://doi.org/10.1111/j.0021-8901.2004.00918.x

Hartig, F. (2022). DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R Package version 0.4.5. Retrieved from https://cran.r-project.org/package=DHARMa

Harwood, A. J. P., Perrow, M. R., & Berridge, R. J. (2018). Use of an optical rangefinder to assess the reliability of seabird flight heights from boat-based surveyors: implications for collision risk at offshore wind farms. Journal of Field Ornithology. https://doi.org/10.1111/jofo.12269

Johnson, D. S., London, J. M., Lea, M. A., & Durban, J. W. (2008). CONTINUOUS-TIME CORRELATED RANDOM WALK MODEL FOR ANIMAL TELEMETRY DATA. Ecology, 89(5), 1208–1215. https://doi.org/10.1890/07-1032.1

Johnston, A., & Cook, A. S. C. P. (2016). How high do birds fly? Development of methods and analysis of digital aerial data of seabird flight heights. BTO Research Report No. 676. Thetford. Retrieved from https://www.bto.org/research-data-services/publications/research-reports/2016/how-high-do-birds-fly-development-methods

Johnston, A., Cook, A. S. C. P., Wright, L. J., Humphreys, E. M., & 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(1), 31–41. https://doi.org/10.1111/1365-2664.12191

Johnston, D., Thaxter, C., Boersch-Supan, P., Humphreys, E., Bouten, W., Clewley, G., … Cook, A. (2021). Investigating avoidance and attraction responses in lesser black-backed gulls Larus fuscus to offshore wind farms. Marine Ecology Progress Series. https://doi.org/10.3354/MEPS13964

Klaassen, R. H. G., Hake, M., Strandberg, R., & Alerstam, T. (2011). Geographical and temporal flexibility in the response to crosswinds by migrating raptors. Proceedings of the Royal Society B: Biological Sciences, 278(1710), 1339–1346. https://doi.org/10.1098/RSPB.2010.2106

Largey, N., Cook, A. S. C. P., Thaxter, C. B., McCluskie, A., Stokke, Bå. G., Wilson, B., & Masden, E. A. (2021). Methods to quantify avian airspace use in relation to wind energy development. Ibis. Blackwell Publishing Ltd. https://doi.org/10.1111/ibi.12913

Lenth, R. V. (2022). emmeans: Estimated Marginal Means, aka Least-squares means. R package version 1.7.2.

Mallory, M. L., & Gilbert, C. D. (2008). Leg-loop harness design for attaching external transmitters to seabirds. Marine Ornithology, 36, 183–188. Retrieved from http://www.marineornithology.org/content/get.cgi?rn=788

Masden, E. A. (2015). Developing an avian collision risk model to incorporate variability and uncertainty. Edinburgh: Scottish Government.

Masden, E. A., & Cook, A. S. C. P. (2016). Avian collision risk models for wind energy impact assessments. Environmental Impact Assessment Review, 56, 43–49. https://doi.org/10.1016/j.eiar.2015.09.001

Masden, E. A., Cook, A. S. C. P., McCluskie, A., Bouten, W., Burton, N. H. K., & Thaxter, C. B. (2021). When speed matters: The importance of flight speed in an avian collision risk model. Environmental Impact Assessment Review, 90, 106622. https://doi.org/10.1016/j.eiar.2021.106622

Masden, E. A., McCluskie, A., Owen, E., & Langston, R. H. W. (2015). Renewable energy developments in an uncertain world: The case of offshore wind and birds in the UK. Marine Policy, 51, 169–172. https://doi.org/10.1016/j.marpol.2014.08.006

McClintock, B. T., & Michelot, T. (2018). momentuHMM: R package for generalized hidden Markov models of animal movement. Methods in Ecology and Evolution, 9(6), 1518–1530. https://doi.org/10.1111/2041-210X.12995

McGregor, R., King, S., Donovan, C. R., Caneco, B., Webb, A., Webb, A., & Wilson Marine Scotland Science JaredWilson, J. (2018). A Stochastic Collision Risk Model for Seabirds in Flight Authorisations Responsibility Name Signature Date Distribution List Name Organisation Email Address. Retrieved from https://www.gov.scot/Resource/0053/00536606.pdf

Pennycuick, C. (1997). Actual and “optimum” flight speeds: field data reassessed. Journal of Experimental Biology, 200(17).

Péron, G., Calabrese, J. M., Duriez, O., Fleming, C. H., García-Jiménez, R., Johnston, A., … Shepard, E. L. C. (2020). The challenges of estimating the distribution of flight heights from telemetry or altimetry data. Animal Biotelemetry, 8(1), 1–13. https://doi.org/10.1186/s40317-020-00194-z

Ross-Smith, V. H., Thaxter, C. B., Masden, E. A., Shamoun-Baranes, J., Burton, N. H. K., Wright, L. J., … Johnston, A. (2016). Modelling flight heights of lesser black-backed gulls and great skuas from GPS: a Bayesian approach. Journal of Applied Ecology, 53(6), 1676–1685. https://doi.org/10.1111/1365-2664.12760

Schaub, T., Klaassen, R. H. G., Bouten, W., Schlaich, A. E., & Koks, B. J. (2019). Collision risk of Montagu’s Harriers Circus pygargus with wind turbines derived from high‐resolution GPS tracking. Ibis, ibi.12788. https://doi.org/10.1111/ibi.12788

Searle, K., Butler, A., Mobbs, D., Trinder, M., McGregor, R., Cook, A., … Daunt, F. (n.d.). Study to examine how seabird collision risk, displacement and barrier effects could be integrated for assessment of offshore wind developments.

Seward, A., Taylor, R. C., Perrow, M. R., Berridge, R. J., Bowgen, K. M., Dodd, S., … Bolton, M. (2021). Effect of GPS tagging on behaviour and marine distribution of breeding Arctic Terns Sterna paradisaea. Ibis, 163(1), 197–212. https://doi.org/10.1111/IBI.12849

Skov, H., Heinänen, S., Norman, T., Ward, R. M., & Méndez-Roldán, S. Ellis, I. (2018). ORJIP Bird Collision and Avoidance Study. Final report. United Kingdom.

Thaxter, C. B., Johnston, D. T., Clewley, G. D., Humphreys, E. M., & Cook, A. S. C. . (2019). Improving our understanding of seabird behaviour at sea.

Thaxter, Chris B., Ross-Smith, V. H., Bouten, W., Clark, N. A., Conway, G. J., Rehfisch, M. M., & Burton, N. H. K. (2015). Seabird-wind farm interactions during the breeding season vary within and between years: A case study of lesser black-backed gull Larus fuscus in the UK. Biological Conservation, 186, 347–358. https://doi.org/10.1016/j.biocon.2015.03.027

Thaxter, Chris B., Ross-Smith, V. H., Clark, J. A., Clark, N. A., Conway, G. J., Marsh, M., … Burton, N. H. K. (2014). A trial of three harness attachment methods and their suitability for long-term use on Lesser Black-backed Gulls and Great Skuas. Ringing and Migration, 29(2), 65–76. https://doi.org/10.1080/03078698.2014.995546

Thaxter, Chris B., Ross-Smith, V. H., Clark, J. A., Clark, N. A., Conway, G. J., Masden, E. A., … Burton, N. H. K. (2016). Contrasting effects of GPS device and harness attachment on adult survival of Lesser Black-backed Gulls Larus fuscus and Great Skuas Stercorarius skua. Ibis, 158(2), 279–290. https://doi.org/10.1111/ibi.12340

Tjomlov, R. S., Skov, H., Armitage, M., Barker, M., Cuttat, F., & Thomas, K. (2021). Resolving key uncertainties of seabird flight and avoidance behaviours at offshore wind farms. Annual Report for April 2020 to October 2020. Report to AOWFL.

Toke, D. (2011). The UK offshore wind power programme: A sea-change in UK energy policy? Energy Policy, 39(2), 526–534. https://doi.org/10.1016/j.enpol.2010.08.043

Wakefield, E. D., Owen, E., Baer, J., Carroll, M. J., Daunt, F., Dodd, S. G., … Bolton, M. (2017). Breeding density, fine-scale tracking, and large-scale modeling reveal the regional distribution of four seabird species. Ecological Applications, 27(7), 2074–2091. https://doi.org/10.1002/eap.1591

Wischnewski, S., McCluskie, A., Bouten, W., Adlard, S., Babcock, M., & Wright, L. J. (n.d.). Plasticity in offshore space-use and behaviour across time governs the susceptibility of seabirds to offshore wind farms.

Wischnewski, S., Sansom, A., McCluskie, A., Bouten, W., Adlard, S., Aitken, D., … Wright, L. J. (n.d.). Temporal and spatial patterns in the offshore behaviour and distribution of kittiwakes: Lessons for Marine Spatial Planning.

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