Skip to main content

Assessing seabird displacement at offshore wind farms: power ranges of a monitoring and data handling protocol


Prior to the construction of an offshore wind farm at the Belgian Thorntonbank, local seabird abundance was studied by means of ship-based surveys. ‘Seabirds at sea’ count data, however, exhibit extreme spatial and temporal variation, impeding the detection of human impacts on seabird abundance and distribution. This paper proposes a transparent impact assessment method, following a before–after control–impact design and accounting for the statistical challenges inherent to ‘seabirds at sea’ data. By simulating a broad range of targeted scenarios based on empirical model coefficients, we tested its efficacy in terms of power and investigated how the chance of statistically detecting a change in numbers is affected by data characteristics, monitoring period and survey intensity. Because of high over-dispersion and/or zero inflation, the power to detect a 50% decrease in numbers was generally low, but did reach 90% within less than 10 years of post-impact monitoring for northern gannet (Morus bassanus) and common guillemot (Uria aalge).

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. Cox, S. L., B. E. Scott & C. J. Camphuysen, 2013. Combined spatial and tidal processes identify links between pelagic prey species and seabirds. Marine Ecology Progress Series 479: 203–221.

    Article  Google Scholar 

  2. Embling, C. B., J. Illian, E. Armstrong, J. van der Kooij, J. Sharples, C. J. Camphuysen & B. E. Scott, 2012. Investigating fine-scale spatio-temporal predator–prey patterns in dynamic marine ecosystems: a functional data analysis approach. Journal of Applied Ecology 49: 481–492.

    Article  Google Scholar 

  3. Huettmann, F. & A. W. Diamond, 2006. Large-scale effects on the spatial distribution of seabirds in the Northwest Atlantic. Landscape Ecology 21: 1089–1108.

    Article  Google Scholar 

  4. Jackman, S., 2011. pscl: A Package of Classes and Methods for R Developed in the Political Science Computational Laboratory. Stanford University, Stanford.

    Google Scholar 

  5. Karnovsky, N. J., L. B. Spear, H. R. Carter, D. G. Ainley, K. D. Amey, L. T. Ballance, K. T. Briggs, R. G. Ford, G. L. Hunt Jr, C. Keiper, J. W. Mason, K. H. Morgan, R. L. Pitman & C. T. Tynan, 2006. At-sea distribution, abundance and habitat affinities of Xantus’s Murrelets. Marine Ornithology 38: 89–104.

    Google Scholar 

  6. Leopold, M. F., R. S. A. van Bemmelen & A. Zuur, 2013. Responses of local birds to the offshore wind farms PAWP and OWEZ off the Dutch mainland coast. Report C151/12, Imares, Texel.

  7. McCullagh, P. & J. A. Nelder, 1989. Generalized Linear Models, 2nd ed. Chapman and Hall, London.

    Book  Google Scholar 

  8. Mackenzie, M. L, L. A. Scott-Hayward, C. S. Oedekoven, H. Skov, E. Humphreys, & E. Rexstad, 2013. Statistical Modelling of Seabird and Cetacean data: Guidance Document. Report SB9 (CR/2012/05), Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews.

  9. Maclean, I. M. D., M. M. Rehfisch, H. Skov & C. B. Thaxter, 2013. Evaluating the statistical power of detecting changes in the abundance of seabirds at sea. Ibis 155: 113–126.

    Article  Google Scholar 

  10. Mapstone, B. D., 2005. Scalable decision rules for environmental impact studies: effect size, type I, and type II errors. Ecological Applications 5: 401–410.

    Article  Google Scholar 

  11. Marques, F. F. C. & S. T. Buckland, 2003. Incorporating covariates into standard line transect analyses. Biometrics 53: 924–935.

    Article  Google Scholar 

  12. Onkelinx, T., G. Van Ryckegem, D. Bauwens, P. Quataert & E. Van den Bergh, 2008. Potentie van ruimtelijke modellen als beleidsondersteunend instrument met betrekking tot het voorkomen van watervogels in de Zeeschelde. Report INBO.R.2008.34, Research Institute for Nature and Forest, Brussels.

  13. Pebesma, E. J., A. F. Bio & R. N. M. Duin, 2000. Mapping seabird densities on the North Sea: combining geostatistics and generalized linear models. In Kleingeld, W. J. & D. G. Krige (eds), Proceedings of the Sixth International Geostatistics Congress, Cape Town.

  14. Pérez-Lapeña, B., K. M. Wijnberg, S. J. M. H. Hulscher & A. Stein, 2010. Environmental impact assessment of offshore wind farms: a simulation-based approach. Journal of Applied Ecology 47: 1110–1118.

    Article  Google Scholar 

  15. Pérez-Lapeña, B., K. M. Wijnberg, A. Stein & S. J. M. H. Hulscher, 2011. Spatial factors affecting statistical power in testing marine fauna displacement. Ecological Applications 21: 2756–2769.

    PubMed  Article  Google Scholar 

  16. Petersen, I. K., T. K. Christensen, J. Kahlert, M. Desholm & A. D. Fox, 2006. Final results of bird studies at the offshore wind farms at Nysted and Horns Rev, Denmark. NERI report, National Environmental Research Institute.

  17. Petersen, I. K., R. D. Nielsen & M. L. MacKenzie, 2014. Post-construction evaluation of birds abundances and distributions in the Horns Rev 2 offshore wind farm area, 2011 & 2012. Technical Report, Aarhus University, Danish Centre for Environment and Energy, Aarhus.

  18. Potts, J. M. & J. Elith, 2006. Comparing species abundance models. Ecological Modelling 199: 153–163.

    Article  Google Scholar 

  19. Ripley, B. & M. Lapsley, 2013. RODBC: ODBC Database Access. R package version 1.3-7. Available on internet at

  20. R Core Team, 2013a. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. Available on internet at

  21. R Core Team, 2013b. Foreign: Read Data Stored by Minitab, S, SAS, SPSS, Stata, Systat, dBase, etc. R package version 0.8-55. Available on internet at

  22. Schneider, D. C., 1990. Spatial autocorrelation in marine birds. Polar Research 8: 89–97.

    Article  Google Scholar 

  23. Schneider, D. C. & D. C. Duffy, 1985. Scale-dependent variability in seabird abundance. Marine Ecology Progress Series 25: 211–218.

    Article  Google Scholar 

  24. Scott, B. E., A. Webb, M. R. Palmer, C. B. Embling & J. Sharples, 2013. Fine scale bio-physical oceanographic characteristics predict the foraging occurrence of contrasting seabird species; Gannet (Morus bassanus) and storm petrel (Hydrobates pelagicus). Progress in Oceanography 117: 118–129.

    Article  Google Scholar 

  25. Schwemmer, P., S. Adler, N. Guse, N. Markones & S. Garthe, 2009. Influence of water flow velocity, water depth and colony distance on distribution and foraging patterns of terns in the Wadden Sea. Fisheries Oceanography 18: 161–172.

    Article  Google Scholar 

  26. Stewart-Oaten, A. & J. R. Bence, 2001. Temporal and spatial variation in environmental impact assessment. Ecological Monographs 71: 305–339.

    Article  Google Scholar 

  27. Tasker, M. L., P. H. Jones, T. J. Dixon & B. F. Blake, 1984. Counting seabirds at sea from ships: a review of methods employed and a suggestion for a standardised approach. Auk 101: 567–577.

    Google Scholar 

  28. Underwood, A. J. & M. G. Chapman, 2003. Power, precaution, type II error and sampling design in assessment of environmental impacts. Journal of Experimental Biology and Ecology 296: 49–70.

    Article  Google Scholar 

  29. Vanermen, N., E. W. M. Stienen, W. Courtens & M. Van de walle, 2006. Referentiestudie van de avifauna van de Thorntonbank. Report INBO.A.2006.22, Research Institute for Nature and Forest, Brussels.

  30. Vanermen, N., E. W. M. Stienen, W. Courtens, T. Onkelinx, M. Van de walle & H. Verstraete, 2010. Monitoring seabird displacement: a modelling approach. Report INBO.R.2010.12, Research Institute for Nature and Forest, Brussels.

  31. Vanermen, N., E. W. M. Stienen, W. Courtens, T. Onkelinx, M. Van de walle & H. Verstraete, 2013. Bird monitoring at offshore wind farms in the Belgian Part of the North Sea—assessing seabird displacement effects. Report INBO.R.2013.755887, Research Institute for Nature and Forest, Brussels.

  32. Venables, W. N. & B. D. Ripley, 2002. Modern Applied Statistics with S, 4th ed. Springer, New York.

    Book  Google Scholar 

  33. Ver Hoef, J. M. & P. L. Boveng, 2007. Quasi-poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology 88: 2766–2772.

    PubMed  Article  Google Scholar 

  34. Walls, R., S. Canning, G. Lye, L. Givens, C. Garrett & J. Lancaster, 2013. Analysis of Marine Environmental Monitoring Plan Data from the Robin Rigg Offshore Wind Farm, Scotland (Operational Year 1). Technical Report 1022038, Natural Power Consultants.

  35. Wickham, H., 2007. Reshaping data with the reshape package. Journal of Statistical Software 21. Available on internet at

  36. Zeileis, A. & T. Hothorn, 2002. Diagnostic checking in regression relationships. R News 2: 7–10. Available on internet at

  37. Zeileis, A., C. Keibler & S. Jackman, 2008. Regression models for count data in R. Journal of Statistical Software 27. Available on internet at

  38. Zuur, A. F., E. N. Ieno, N. J. Walker, A. A. Saveliev & G. M. Smith, 2009. Mixed Effects Models and Extensions in Ecology with R. Springer, New York.

    Book  Google Scholar 

Download references


First of all, we want to thank the wind farm concession holders for financing this research in fulfilment of the monitoring requirements of their environmental permits, and the Royal Belgian Institute of Natural Sciences (RBINS) for assigning it to us. A special word of gratitude goes out to DAB Vloot and the Flanders Marine Institute (VLIZ) for providing monthly ship time on RV’s Zeeleeuw and Simon Stevin, and the same goes out to RBINS and the Belgian Science Policy (BELSPO) for the ship time on RV Belgica. In this respect, we also wish to thank all crew members of aforementioned RV’s for their cooperation. We kindly thank Robin Brabant, Steven Degraer & Lieven Naudts from RBINS and André Cattrijsse from VLIZ for their invaluable logistical support and cooperation throughout the monitoring programme. During the early stages of the statistical processing, my colleagues Dirk Bauwens and Paul Quataert provided helpful advice. We are very grateful to all volunteers (especially Walter Wackenier who joined us every month) who assisted during the seabird count surveys. Finally, we wish to thank the three anonymous reviewers for their highly valuable comments, as under their impulse the manuscript has improved considerably.

Author information



Corresponding author

Correspondence to Nicolas Vanermen.

Additional information

Guest editors: Steven Degraer, Jennifer Dannheim, Andrew B. Gill, Han Lindeboom & Dan Wilhelmsson / Environmental impacts of offshore wind farms

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vanermen, N., Onkelinx, T., Verschelde, P. et al. Assessing seabird displacement at offshore wind farms: power ranges of a monitoring and data handling protocol. Hydrobiologia 756, 155–167 (2015).

Download citation


  • Offshore wind farm
  • Belgian North Sea
  • Seabirds at sea
  • Impact assessment
  • BACI monitoring
  • Power analysis
  • Zero inflated negative binomial modelling