Hotspots in the grid: Avian sensitivity and vulnerability to collision risk from energy infrastructure interactions in Europe and North Africa

Abstract

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. 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.

Survival of a small reintroduced griffon vulture population in the Apennines: Insights from Global Positioning System tracking

“…Based on our findings, actions and measures should be taken to proactively prevent and tackle human-related impact. 58 For instance, prevention and mitigation measures should be adopted at existing wind farms to reduce vulture strikes, 59,60 which accounts for 20% of casualties in our study. Such measures should represent a mandatory prerequisite while planning and operating new wind farms, which, in turn, should be located in areas far from vultures’ flight paths as much as possible.…”

An ecological risk assessment for the impacts of offshore wind farms on birds in Australia

“…For example, we used a relatively simple morphology‐based index of flight manoeuvrability that was consistent across all taxa investigated. This approach could be refined with the inclusion of more taxon‐specific flight characteristics, such as the amount of time spent soaring or gliding, average flight speed and whether the taxon moves in flocks or as individuals, to better describe the susceptibility of high‐risk taxa (Gauld et al 2022). Similarly, the attribute scoring for habitat specialization could be improved with species‐specific data, especially at regional scales.…”

“…Therefore, we have used wing loading, which is the mass of a bird divided by the wing area, as a consistent metric of morphology that provides a proxy for flight manoeuvrability. The approach follows Warham (1990) and Gauld et al (2022), such that taxa with a low wing loading are light and manoeuvrable (i.e., low risk), in contrast to taxa with a high wing loading that have relatively short-winged rapid flight, and have lower manoeuvrability (i.e., high risk). Data from Tobias et al (2022) on the wing length, wing width and body mass of all bird taxa were used to determine a wing loading index (FM) where FM = body mass/(wing length * wing width).…”

“…It seems obvious that the greater the separation of the wind farm and areas of high numbers or importance for birds will minimize impacts, and for that reason, the availability of large-scale bird distribution data in areas of potential wind farm development is critical to underpin all assessments. Wildlife sensitivity maps use a sensitivity or risk score for each taxon, with the distribution of each taxon represented by whether it occurs in a grid cell, with the risk scores summed across all taxa that occur in each grid cell (Gauld et al, 2022). As such, sensitivity maps represent a natural progression from the very large-scale, regional ecological risk assessment approach used here to allow much finer scale environmental processes and risks to be mapped.…”

Departure, routing and landing decisions of long-distance migratory songbirds in relation to weather

“…This is particularly problematic offshore, where songbirds find hardly any options to land and get attracted by illuminated artificial structures, which can lead to mass mortality of disoriented migrants [75][76][77][78]. Thus, knowledge about timing and routing is crucial to understand interactions of migrating birds with human activities and their potential consequences for individual survival and reproductive fitness [78,79].…”