DOI: 10.5061/dryad.sj3tx96fx
title: Data from: Towards a better understanding of avian collisions in wind energy facilities using automatic detection systems

description: 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.
description: # Data from: Towards a better understanding of avian collisions in wind
 energy facilities using automatic detection systems
 [https://doi.org/10.5061/dryad.sj3tx96fx](https://doi.org/10.5061/dryad.sj3tx96fx) ## Description of the data and file structure Data were automatically recorded by automatic detection systems (ADS) on turbines in 11 wind energy facilities (WEF) in France between 2018 and 2023. Their acquisition, processing, and analysis are fully described in the associated publication "Towards a better understanding of avian collisions in wind energy facilities using automatic detection systems". ### Files and variables #### File: JAPPL-2024-01035\_data.csv **Description:**  ##### Variables * trackID: unique ID of the detected bird * WEF_ID: unique ID of the Wind Energy Facility * ADS_ID: unique ID of the Automatic Detection System * ADS_type: type of ADS, either optical with or without stereoscopy (3D or 2D) * datetime: date and time of the bird detection * julianDay: date of the bird detection in Julian days (e.g., 1 = January 1st) * year: year of the bird detection * nbHoursAfterSunrise: time between sunrise and the bird detection (in hours) * birdSizeClass: size class of the detected bird (either medium or large) * birdTrackDuration: duration of the detected bird's track (in s) * birdTrackLength: length of the detected bird's track (in m) * birdTrackDisplacement: distance between the start and the end of the detected bird's track (in m) * birdTrackMeanSpeed: mean speed of the detected bird's track (in m/s) * birdTrackStraightness: straightness index of the detected bird's track, ranging from 0 (very curvy track) to 1 (straight track) * birdFlightBehavior: flight behavior of the detected bird, either transit (1) or foraging flight (2) * birdTurbineDistance: distance between the closest turbine and the detected bird (in m) * riskZoneIntrusion: whether the bird has intruded in the risk zone around the rotor (1) or not (0) * riskZoneIntrusionDuration: if the detected bird has entered the risk zone, the intrusion duration (in s) * exposure: number of bird detections per hour * WEF_nbTurbine: number of turbines in the WEF * WEF_age: age (in years) of the WEF at the time of the bird detection (i.e., year of detection - year of WEF construction) * WEF_turbineHeight: turbine height from ground to blade tip (in m) * WEF_rotorDiameter: diameter of the turbine rotor (in m) * WEF_constructionYear: construction year of the WEF * WEF_altitude: altitude of the WEF (in m) * WEF_CLC: number of pixel with trees in a buffer of 600 m around the WEF center * WEF_TRI: mean Terrain Ruggedness Index calculated in a buffer of 600 m around the WEF center * rotorSpeed: speed of the turbine rotor at the time of the bird detection (in rpm) * temperature: hourly temperature (in °C) * windSpeed: hourly wind speed (in m/s) * humidity: hourly humidity (in %) * nebulosity: hourly cloud cover (in octas) * visibility: hourly visibility (in m) NA- Not applicable