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Data from: Towards a better understanding of avian collisions in wind energy facilities using automatic detection systems

Gémard, Charlène1; Duriez, Olivier1; Chappe, Olivier2; Duclos, Gwénaël2; Besnard, Aurélien1

Published Mar 17, 2025 on Dryad. https://doi.org/10.5061/dryad.sj3tx96fx

Data files

Mar 17, 2025 version files 41.55 MB

Abstract

The rapid expansion of wind power energy has direct negative impacts on biodiversity, notably on avifauna through collisions with turbines. A better understanding of the collision causes is the key to improving mitigation efforts. Collisions are the result of a combination of environmental factors that increase bird sensitivity and exposure to collisions. To date, potential risk factors have mostly been assessed individually, in few species of interest and/or at a small spatio-temporal scales, despite the multifaceted nature of collision risk. To fill this gap, we used for the first time data from automatic detection systems (optic systems that automatically detect and monitor birds in the vicinity of wind turbines) to simultaneously assess the effects of behavioral and environmental factors on bird sensitivity (here, estimated as the bird presence in the risk zone) and exposure (here, estimated as the frequency with which birds use the zone). We analyzed 205,867 bird trajectories from 11 wind energy facilities in France, recorded between 2018 and 2023. We obtained results similar to previous studies relying on other methods (i.e., GPS, direct observations). Results suggest that bird sensitivity was higher during periods of high bird activity (first hours of daylight, migrations). They also suggest that sensitivity and exposure may increase in conditions that reduced the birds’ visual perception of turbines (high nebulosity, low visibility, low rotor speeds) and in conditions that may influence the flight height of birds (high temperatures, high wind speeds). We found a nonsynchronicity of exposure and sensitivity peaks, highlighting the importance of considering both drivers of risk when investigating the collision risk. However, our results show a high variability between species, flight behaviors, and sites that should be addressed in the future to clarify the relationships between collision risk, birds’ visual perception of the turbine, and behavior. Data from automatic detection systems can be a promising non-invasive approach that requires few human and logistic resources to better understand bird behavior in anthropogenic environments and collision causes. These new insights are valuable to biodiversity stakeholders in bridging the gap between the productivity of wind energy facilities and biodiversity conservation.