Advertisement

Predicting the Weather-Dependent Collision Risk for Birds at Wind Farms

  • Henrik SkovEmail author
  • Stefan Heinänen
Conference paper

Abstract

Collision risk for birds remains a potential conservation issue and environmental barrier to the development of wind farms on land as well as at sea. Baseline and post-construction studies in Denmark carried out at coastal and marine wind farms during 2010–2012 have aimed at developing prediction tools which could pave the way for improved planning and siting of wind farms in relation to movements of birds. Detection of flight trajectories by means of visual observations is severely constrained, and thus field campaigns were undertaken using a combination of visual observations and radar- and rangefinder-based tracking. The collection of two- and three-dimensional track data was necessary to obtain useful information on the responses of migrating bird species to the wind farms, and on flight altitudes of the birds during different weather conditions and in relation to landscape components. To be able to assess general patterns in the migration behaviour of birds, we developed statistical models capable of explaining the differences in altitude based on relationships with wind and weather conditions and distance to coast. As these relationships in many cases were non-linear, the error structure of the data non-normally distributed, and the track data spatially and temporally auto-correlated we chose to use a generalized additive mixed modelling (GAMM) framework. The resulting models of the migration altitude of raptors and other groups of landbirds made it possible to assess the weather-dependent flight altitude at the wind farm sites. The studies provided strong indications that wind speed and direction as well as humidity, air clarity and air pressure are important predictors in general for all species in addition to distance to land and wind farm, and the birds favour tail winds and decreasing wind speed. Collision models display a variety of specific trends with rates of collisions of landbirds increasing during periods of head winds and reduced visibility, while the collision rates of seabirds typically increase during periods of tail winds and increased visibility. Our studies have shown that birds across a wide range of species show clear weather-dependent movements which can be predicted for specific spatial settings using statistical models. These findings stress the potential for intensifying the strategic planning processes related to wind farms.

Keywords

Avian collision Offshore wind farms Modelling Wind impacts Flight patterns 

Notes

Acknowledgments

The observers who carried out the recordings in the two study areas are thanked for their great efforts and their great photos. The collaboration from the wind farm developers DONG and E.ON is acknowledged.

References

  1. Alerstam, T., M. Rosén, J. Bäckman, P.G.P. Ericson, and O. Hellgren. 2007. Flight speeds among bird species: Allometric and phylogenetic effects. PLoS Biology 5(8): e197. doi: 10.1371/journal.pbio.0050197 (open source).CrossRefGoogle Scholar
  2. Band, W. 2000. Windfarms and birds: Calculating a theoretical collision risk assuming no avoidance. SNH Guidance. Excel spreadsheet available: http://www.snh.gov.uk/docs/C234672.xls.
  3. Band, W. 2012. Using a collision risk model to assess bird collision risks for offshore windfarms. London: The Crown Estate, SOSS Secretariat.Google Scholar
  4. Band, W., M. Madders, and D.P. Whitfield. 2007. Developing field and analytical methods to assess avian collision risk at wind farms. In Birds and wind farms, ed. M. DeLucas, G.F.E. Janss, and M. Ferrer, 259–275. Madrid: Quercus.Google Scholar
  5. Barrios, L., and A. Rodriguez. 2004. Behavioural and environmental correlates of soaring-bird mortality at on-shore wind turbines. Journal of Applied Ecology 41: 72–81.CrossRefGoogle Scholar
  6. Bellebaum, J., C. Grieger, R. Klein, U. Köppen, J. Kube, R. Neumann, A. Schulz, H. Sordyl,, and H. Wendeln. 2010. Ermittlung artbezogener Erheblichkeits¬schwellen von Zugvögeln für das Seegebiet der südwestlichen Ostsee bezüglich der Gefährdung des Vogelzuges im Zusammenhang mit dem Kollisionsrisiko an Windenergieanlagen. Abschlussbericht. Forschungs¬vorhaben des Bundesministeriums für Umwelt, Naturschutz und Reaktorsicherheit (FKZ 0329948). Neu Broderstorf.Google Scholar
  7. Bivand, R. 2009. Spatial dependencies: Weighting schemes, statistics and models. R package version 0.4–34.Google Scholar
  8. Blew, J., M. Hoffmann, G. Nehls, and V. Hennig. 2008. Investigations of the bird collision risk and the responses of harbour porpoises in the offshore wind farms Horns Rev, North Sea, and Nysted, Baltic Sea, in Denmark. Part I: Birds. Final Report FKZ 0329963 + FKZ 0329963A, 145pp. German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety.Google Scholar
  9. BTO. 2012. Birdsfacts. Retrieved from Bto.org: http://www.bto.org/about-birds/birdfacts.
  10. Collier, M., S. Dirksen,, and K. Krijgsveld. 2011. A review of methods to monitor collisions or micro-avoidance of birds with offshore wind turbines. s.l.: Commissioned by The Crown Estate, SOSS, through the British Trust for Ornithology.Google Scholar
  11. Cook, A., L. Wright, and N. Burton. 2012. A Review of flight heights and avoidance rates of birds in relation to offshore windfarms. London: Crown Estate, SOSS Secretariat.Google Scholar
  12. de Lucas M, G.F.E. Janss, and M. Ferrer. 2007. Birds and wind farms. Risk assessment and mitigation. 978-84-87610-18-9. Quercus, Spain.Google Scholar
  13. de Lucas, M., G.F.E. Janss, D.P. Whitfield, and M. Ferrer. 2008. Collision fatality of raptors in wind farms does not depend on raptor abundance. Journal of Applied Ecology 45: 1695–1703.CrossRefGoogle Scholar
  14. Desholm, M., and J. Kahlert. 2005. Avian collision risk at an offshore wind farm. Biology Letters 1: 296–298.CrossRefGoogle Scholar
  15. DOF. 2012. [Online] Available at: www.DOF.dk.
  16. Drewitt, A.L., and R.H.W. Langston. 2008. Collision effects of wind-power generators and other obstacles to birds. Annals of the New York Academy of Sciences 1134: 233–266.CrossRefGoogle Scholar
  17. Furness, B., and W. Wade. 2012. Vulnerability of Scottish seabirds to offshore wind turbines. Glasgow: MacArthur Green Ltd.Google Scholar
  18. Garthe, S., and O. Hüppop. 2004. Scaling possible adverse effects of marine wind farms on seabirds: Developing and applying a vulnerability index. Journal of Applied Ecology 41: 724–734.CrossRefGoogle Scholar
  19. Hull, C.L., and S. Muir. 2013. Behaviour and turbine avoidance rates of eagles at two wind farms in Tasmania, Australia. Wildlife Society Bulletin 37(1): 41–58.CrossRefGoogle Scholar
  20. Jenkins, A.R., J.J. Smallie, and M. Diamond. 2010. Avian collisions with power lines: A global review of causes and mitigation with a South African perspective. Bird Conservation International 20: 263–278.CrossRefGoogle Scholar
  21. Krijgsveld K.L., R.C. Fijn, C. Heunks, P.W. van Horssen, J. de Fouw, M. Collier, M.J.M. Poot, D. Beuker, and S. Dirksen. 2010. Effect studies offshore wind farm Egmond aan Zee. Progress report on fluxes and behaviour of flying birds covering 2007 & 2008. Bureau Waardenburg, Noordzeewind, Ijmuiden, 103p.Google Scholar
  22. Martin, G.R. 2011. Understanding bird collisions with man-made objects: A sensory ecological approach. Ibis 153: 239–254.CrossRefGoogle Scholar
  23. May, R., and K. Bevanger (Eds.). 2011. Proceedings. Conference on wind energy and wildlife impacts, 2–5 May 2011. Trondheim, Norway – NINA Report 693, 140p.Google Scholar
  24. Potts, J.M., and J. Elith. 2006. Comparing species abundance models. Ecological Modelling 199: 153–163.CrossRefGoogle Scholar
  25. Prinsen, H.A.M., G.C. Boere, N. Pires, and J.J. Smallie (Compilers). 2011. Review of the conflict between migratory birds and electricity power grids in the African-Eurasian region. CMS technical series no. XX, AEWA technical series no. XX, Bonn, Germany, 117p.Google Scholar
  26. R Core Team. 2004. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. http://www.R-project.org
  27. Smallwood, K.S., L. Neher, D. Bell, J. DiDonato, B. Karas, S. Snyder, and S. Lopez. 2009. Range management practices to reduce wind turbine impacts on burrowing owls and other raptors in the Altamont Pass Wind Resource Area, California. Report No. CEC-500-2008-080 to the California Energy Commission, Public Interest Energy Research – Environmental Area, Sacramento, USA.Google Scholar
  28. Winkelman, J.E. 1992. De invloed van de Sep-proefwindcentrale te Oosterbierum (Friesland). op vogels, 1: Aanvaringsslachtoffers, Arnhem, The Netherlands.: RIN-rapport 92/2, IBN-DLO.Google Scholar
  29. Wood, S.N. 2003. Thin plate regression splines. Journal of the Royal Statistical Society: Series B 65(1): 95–114.CrossRefGoogle Scholar
  30. Wood, S.N. 2006. Generalized additive models: An introduction with R. London: Chapman and Hall.Google Scholar
  31. Zuur, A.F., E.N. Ieno, N.J. Walker, A.A. Saveliev, and G.M. Smith. 2009. Mixed effects models and extensions in ecology with R. New York: Springer Science and Business Media.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.DHIHørsholmDenmark

Personalised recommendations