Use of avian GPS tracking to mitigate human fatalities from bird strikes caused by large soaring birds
Arrondo, Eneko (2021), Use of avian GPS tracking to mitigate human fatalities from bird strikes caused by large soaring birds, Dryad, Dataset, https://doi.org/10.5061/dryad.fxpnvx0rj
1. Birds striking aircraft cause substantial economic loss worldwide and, more worryingly, human and wildlife fatalities. Designing effective measures to avoid fatal bird strikes requires in-depth knowledge of the characteristics of this incident type and the flight behaviors of the bird species involved.
2. The characteristics of bird strikes involving aircraft crashes or loss of human life in Spain were studied and compared to flight patterns of bird monitored by GPS. We tracked 210 individuals of the three species that cause the most crashes and human fatalities in Spain: griffon and cinereous vultures (Gyps fulvus and Aegypius monachus) and white storks (Ciconia ciconia).
3. All the crashes involved general aviation aircraft, while none were recorded in commercial aviation. Most occurred outside airport boundaries, at midday and in the warmest months, which all correspond with the maximum flight activity of the studied species.
4. Bird flight altitudes overlapped the legal flight altitude limit set for general aviation.
5. Policy implications: Mitigation of fatal bird strikes should especially address the conflict between general aviation and large soaring birds. Air transportation authorities should consider modifying the flight ceiling for general aviation flights above the studied species’ maximum flight altitude. Moreover, policymakers should issue pilots with recommendations regarding the dates and times of peak activity of large soaring bird species to improve flight safety.
We selected all the bird locations (i.e. GPS positions) recorded in Spain. We then classified locations as flying or non-flying according to ground speed and altitude above ground level (AGL). To do so, we first estimated altitude AGL by a digital elevation model (cell size of 30x30 m) (IGNE, 2019https://idee.es/web/guest/inicio) and calculated the difference between tracking altitude and elevation model. Second, we selected 0.1% of the upper and lower values of our dataset to identify anomalous values (e.g. -8,069.4 and 47,053m AGL in the white stork dataset), but by minimizing the risk of omitting real values. Given the known flight behavior of the studied species, we considered these locations to be outliers and eliminated them from our analyses (Fleming et al., 2020). Third, we separated flying and perching locations by first establishing 2.5 ms-1 as our ground speed threshold for flying locations, which is in accordance with Schlaich et al. (2016), and then defined a second threshold using altitude. For this purpose, we calculated the cumulative histogram of altitude AGL per species (Figure S.1). Using 1-meter breaks, we established the value after the maximum slope for which cumulative frequency stabilized as the threshold to classify a flying location. To define the stabilization of frequencies, we calculated the relative difference (%) between consecutive cumulative frequencies. A difference of ≤ 0.1% was considered stabilization. Therefore, locations with ground speed ≤ 2.5 m/s and altitude ≤ 35 m, 60 m and 90 m AGL were considered non-flight locations for griffon vultures, cinereous vultures and white storks, respectively, whereas other locations were allocated as flight locations. Finally, to gain an approximate measure of the area covered by the studied species, we calculated the 95% kernel utilization distribution (UD) with a smoothing parameter of 20 km and a grid cell size of 2.5 km using the R package adehabitatHR (Calenge, 2014) including all the available locations for each species.