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1. Wind turbines and power lines can cause bird mortality due to collision or electrocution. The biodiversity impacts of energy infrastructure (EI) can be minimised through effective landscape-scale planning and mitigation. The identification of high-vulnerability areas is urgently needed to assess potential cumulative impacts of EI while supporting the transition to zero carbon energy.2. We collected GPS location data from 1,454 birds from 27 species susceptible to collision within Europe and North Africa and identified areas where tracked birds are most at risk of colliding with existing EI. Sensitivity to EI development was estimated for wind turbines and power lines by calculating the proportion of GPS flight locations at heights where birds were at risk of collision and accounting for species' specific susceptibility to collision. We mapped the maximum collision sensitivity value obtained across all species, in each 5 × 5 km grid cell, across Europe and North Africa. Vulnerability to collision was obtained by overlaying the sensitivity surfaces with density of wind turbines and transmission power lines.3. Results: Exposure to risk varied across the 27 species, with some species flying consistently at heights where they risk collision. For areas with sufficient tracking data within Europe and North Africa, 13.6% of the area was classified as high sensitivity to wind turbines and 9.4% was classified as high sensitivity to transmission power lines. Sensitive areas were concentrated within important migratory corridors and along coastlines. Hotspots of vulnerability to collision with wind turbines and transmission power lines (2018 data) were scattered across the study region with highest concentrations occurring in central Europe, near the strait of Gibraltar and the Bosporus in Turkey.4. Synthesis and applications. We identify the areas of Europe and North Africa that are most sensitive for the specific populations of birds for which sufficient GPS tracking data at high spatial resolution were available. We also map vulnerability hotspots where mitigation at existing EI should be prioritised to reduce collision risks. As tracking data availability improves our method could be applied to more species and areas to help reduce bird-EI conflicts. published

1. 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.2. 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.3. 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.4. 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.5. 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.

* Wind energy generation is increasing globally, and associated environmental impacts must be considered. The risk of seabirds colliding with offshore wind turbines is influenced by flight height, and flight height data usually come from observers on boats, making estimates in daylight in fine weather. GPS tracking provides an alternative and generates flight height information in a range of conditions, but the raw data have associated error. * Here, we present a novel analytical solution for accommodating GPS error. We use Bayesian state-space models to describe the flight height distributions and the error in altitude measured by GPS for lesser black-backed gulls and great skuas, tracked throughout the breeding season. We also examine how location and light levels influence flight height. * Lesser black-backed gulls flew lower by night than by day, indicating that this species would be less likely to encounter turbine blades at night, when birds’ ability to detect and avoid them might be reduced. Gulls flew highest over land and lowest near the coast. For great skuas, no significant relationships were found between flight height, time of day and location. * We consider four ‘collision risk windows’, corresponding to the airspace swept by rotor blades for different offshore wind turbine designs. We found the highest proportion of birds at risk for a 22–250 m turbine (up to 9% for great skuas and 34% for lesser black-backed gulls) and the lowest for a 30–258 m turbine. Our results suggest lesser black-backed gulls are at greater risk of collision than great skuas, especially by day. * Synthesis and applications. Our novel modelling approach is an effective way of resolving the error associated with GPS tracking data. We demonstrate its use on GPS measurements of altitude, generating important information on how breeding seabirds use their environment. This approach and the associated data also provide information to improve avian collision risk assessments for offshore wind farms. Our modelling approach could be applied to other GPS data sets to help manage the ecological needs of seabirds and other species at a time when the pressures on the marine environment are growing.