Identification of Griffon Vulture’s Flight Types Using High-Resolution Tracking Data

Abstract

Being one of the most frequently killed raptors by collision with wind turbines, little is known about the Griffon vulture's flight strategies and behaviour in a fine scale. In this study, we used high-resolution tracking data to differentiate between the most frequently observed flight types of the Griffon, and evaluated the performance of our proposed approach by an independent observation during a period of 4 weeks of fieldwork. Five passive flight types including three types of soaring and two types of gliding were discriminated using the patterns of measured GPS locations. Of all flight patterns, gliding was classified precisely (precision = 88%), followed by linear and thermal soaring with precision of 83 and 75%, respectively. The overall accuracy of our classification was 70%. Our study contributes a baseline technique using high-resolution tracking data for the classification of flight types, and is one step forward towards the collision management of this species.

The use of social information in vulture flight decisions

“…We applied a moving window of 30 s to calculate the absolute cumulative sum of the turning angles (hereafter cumulative turning angle) and a moving window of 5 s to calculate the average vertical speed. We then applied a k-means approach ( k = 2, ‘kmeans’ function, stats R package) on the smoothed vertical speed (positive speed when flying upwards, negative when flying downwards) to distinguish between soaring (ascending flight) and gliding (descending flight, [58,59]). We further classified soaring locations into circular soaring (indicating use of thermal updraughts) and linear soaring (also called slope soaring, expected to occur outside of thermals), with circular soaring being associated with a cumulative turning angle greater than or equal to 300°.…”

The use of social information in vulture flight decisions

“…Within each flight session, we created a dynamic map of thermals (Figure 3). First, we spatially clustered vulture circular soaring locations (reflecting the use of the same thermal updraft) independently of time by using a 3D density-based spatial clustering approach ('dbscan' function, dbscan R package, [53]). This algorithm relies on a spherical neighbourhood to perform density-based neighbour joining, i.e.…”

An Autonomous Soaring for Small Drones Using the Extended Kalman Filter Thermal Updraft Center Prediction Method Based on Ordinary Least Squares

“…Thermal updrafts are widely distributed across regions such as hilly areas, deserts, urban centers, farmlands, lakes, and swamps. Raptors like vultures [2][3][4], frigatebirds [5][6][7], steppe eagles [8], bats [9], desert eagles, and vultures [10] can engage in static soaring within thermal updrafts, soaring in the high altitudes for extended periods without flapping their wings. By emulating the soaring behavior of birds within thermal updrafts, it is possible to significantly enhance the endurance of drones at a relatively low cost, enabling small drones to undertake long-range missions with extended flight times.…”