DOI: 10.5061/dryad.jm63xsjcw
title: Hotspots in the grid: Avian sensitivity and vulnerability to collision risk from energy infrastructure interactions in Europe and North Africa

description: 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.
description: The full methodology to produce this data is described in Gauld
 <em>et al.</em> (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: &lt;0.025 percentile Low: 0.025
 &lt;0.25 percentile Moderate: 0.25 &lt; 0.75
 Percentile High: 0.75 &lt; 0.975
 Percentile Very High: &gt;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
description: 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.