Avian vulnerability to wind farm collision through the year: Insights from lesser black-backed gulls (Larus fuscus) tracked from multiple breeding colonies
- Wind energy generation has become an important means to reduce reliance on fossil fuels and mitigate against human‐induced climate change, but could also represent a significant human–wildlife conflict. Airborne taxa such as birds may be particularly sensitive to collision mortality with wind turbines, yet the relative vulnerability of species’ populations across their annual life cycles has not been evaluated.
- Using GPS telemetry, we studied the movements of lesser black‐backed gulls Larus fuscus from three UK breeding colonies through their annual cycle. We modelled the distance travelled by birds at altitudes between the minimum and maximum rotor sweep zone of turbines, combined with the probability of collision, to estimate sensitivity to collision. Sensitivity was then combined with turbine density (exposure) to evaluate spatio‐temporal vulnerability.
- Sensitivity was highest near to colonies during the breeding season, where a greater distance travelled by birds was in concentrated areas where they were exposed to turbines.
- Consequently, vulnerability was high near to colonies but was also high at some migration bottlenecks and wintering sites where, despite a reduced sensitivity, exposure to turbines was greatest.
- Synthesis and applications. Our framework combines bird‐borne telemetry and spatial data on the location of wind turbines to identify potential areas of conflict for migratory populations throughout their annual cycle. This approach can aid the wind farm planning process by: (a) providing sensitivity maps to inform wind farm placement, helping minimize impacts; (b) identifying areas of high vulnerability where mitigation warrants exploration; (c) highlighting potential cumulative impacts of developments over international boundaries and (d) informing the conservation status of species at protected sites. Our methods can identify pressures and linkages for populations using effect‐specific metrics that are transferable and could help resolve other human–wildlife conflicts.
Chris B. Thaxter
Viola H. Ross‐Smith
Nigel A. Clark
Greg J. Conway
Gary D. Clewley
Lee J. Barber
Niall H. K. Burton
British Trust for Ornithology, Norfolk
Computational Geo‐Ecology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, The Netherlands
Elizabeth A. Masden
Environmental Research Institute, North Highland College, University of the Highlands and Islands, Thurso, U.K.
Journal of Applied Ecology 2019; 00: 1–13
First published: 09 September 2019
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