• Energy Research

AbstractWind 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 gullsLarus fuscusfrom 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.

The full methodology to produce this data is described in Gauld et al. (2022) Hotspots in the grid: avian sensitivity and vulnerability to collision risk from energy infrastructure interactions in Europe and north Africa, Journal of Applied Ecology In brief: 65 Bird movement datasets containing high resolution GPS tracking data were downloaded from the www.movebank.org repository in April of 2019. These data were processed to remove locations associated with poor GPS accuracy and code locations in flight as present within a danger height band for wind turbines (15 - 135m above ground), Transmission Powerlines (10 - 60m above ground) or not. All datasets were combined into a single dataframe. This was overlaid onto a 5 x 5km fishnet grid covering Europe and North Africa, each grid cell had a unique NID value. For each species present within a given grid cell, the proportions of GPS locations in flight at danger height for the two danger height bands were calculated and weighted for uncertainty using the Wilson Confidence Interval, the resulting value for each grid cell was multiplied by the MBRCI (Morpho-Behavioural Conservation Status Risk Index) value to produce a sensitivity score for each species present in each grid cell where sufficient tracking data is available. To produce the family level sensitivity surface, the maximum sensitivity score of any species within a given family in a given grid cell is used. To produce the combined sensitivity surface, the maximum sensitivity score of any species within a given grid cell is used. The seasonal surfaces were produced in the same manner but calculated separately for Breeding and Non-Breeding periods. The vulnerability surface was produced by overlaying the sensitivity scores onto the density of either wind turbines or power lines in each grid cell. Grid cells were then categorised according to vulnerability by quantiles so Very Low: <0.025 percentile Low: 0.025 <0.25 percentile Moderate: 0.25 < 0.75 Percentile High: 0.75 < 0.975 Percentile Very High: >0.975 Percentile and No Data where GPS tracking data was not present. Wind turbine and power line densities were derived from the best available continental scale data at the time of the analysis. The accuracy of these datasets is discussed extensively in the supporting information of the paper. Raw data was processed in R, QGIS and ArcMap 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. 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 5x5 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. 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. 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. The results here are intended to provide a continental scale guide to where the collision risk hotspots are for the tracked birds used in the analysis and help guide further wind farms and power line development away from the higher risk areas for birds. It is important not to assume that areas where we do not have tracking data are free from risk, therefore this analysis does not remove the need for more local scale investigations into the ecological impact of a proposed development. 

Abstract 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. 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. 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. 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.