DOI: 10.5061/dryad.fxpnvx0rj
title: Use of avian GPS tracking to mitigate human fatalities from bird strikes caused by large soaring birds

description: 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.
description: 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<sup>-1</sup> 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.