Abstract
The flight performance of birds is strongly affected by the dynamic state of the atmosphere at the birds' locations. Studies of flight and its impact on the movement ecology of birds must consider the wind to help us understand aerodynamics and bird flight strategies. Here, we introduce a systematic approach to evaluate wind speed and direction from the high‐frequency GPS recordings from bird‐borne tags during thermalling flight. Our method assumes that a fixed horizontal mean wind speed during a short (18 seconds, 19 GPS fixes) flight segment with a constant turn angle along a closed loop, characteristic of thermalling flight, will generate a fixed drift for each consequent location. We use a maximum‐likelihood approach to estimate that drift and to determine the wind and airspeeds at the birds' flight locations. We also provide error estimates for these GPS‐derived wind speed estimates. We validate our approach by comparing its wind estimates with the mid‐resolution weather reanalysis data from ECMWF, and by examining independent wind estimates from pairs of birds in a large dataset of GPS‐tagged migrating storks that were flying in close proximity. Our approach provides accurate and unbiased observations of wind speed and additional detailed information on vertical winds and uplift structure. These precise measurements are otherwise rare and hard to obtain and will broaden our understanding of atmospheric conditions, flight aerodynamics, and bird flight strategies. With an increasing number of GPS‐tracked animals, we may soon be able to use birds to inform us about the atmosphere they are flying through and thus improve future ecological and environmental studies.
Highlights
The world that animals traverse is constantly changing
To estimate the potential sensitivity of our method to such behaviorally driven bias, we considered a bird that changed its heading at a constant rate ω and varies its airspeed a according to its orientation relative to the wind direction δ, with a constant speed component a0 and a speed amplitude of ac: a(δ) = a0 −
By examining a large set of pairs of independent wind estimates from two white storks circling in close proximity in the same thermal, we determined that our approach provided wind estimates with an accuracy of about half a meter per second (Figure 2)
Summary
Flack et al, 2016; Gill et al, 2009; Kranstauber, Weinzierl, Wikelski, & Safi, 2015; Schmaljohann, Liechti, & Bruderer, 2009). In a hypothetical wind-free environment, high-resolution records of thermalling behavior will describe short track segments in the shape of closed loops representing the bird’s movement relative to the air. In real-world environments, wind adds a component to the movement vectors and distorts these closed loops in the resulting GPS track that depicts the movement relative to the ground (Figure 1). We further develop this approach in a systematic fashion: We use maximum-likelihood optimization to minimize the error in wind estimate over short segments representing individual thermal loops; we derive and validate error estimates; follow explicit assumptions about the bird’s flight patterns and identify their potential effects on the error or bias of the resulting wind estimate; and point out how our method can be generalized to other behavioral patterns and tracking technologies. We demonstrate that the resulting observations can be useful both for studying the structure of the atmosphere and the flight behavior of storks
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