DOI: 10.5061/dryad.4tmpg4f7s
title: Young frigatebirds learn how to compensate for wind-drift

description: Compensating for wind drift can improve goalward flight efficiency in
 animal taxa, especially amongst those that rely on thermal soaring to
 travel large distances. Little is known, however, about how animals
 acquire this ability. The great frigatebird (Fregata
 minor) exemplifies the challenges of wind drift compensation
 because it lives a highly pelagic lifestyle, traveling very long distances
 over the open ocean but without the ability to land on water. Using GPS
 tracks from fledgling frigatebirds, we followed young frigatebirds from
 the moment of fledging to investigate whether wind drift compensation was
 learnt and, if so, what sensory inputs underpinned it. We found
 that the effect of wind drift reduced significantly with both experience
 and access to visual landmark cues. Further, we found that the
 effect of experience on wind drift compensation was more
 pronounced when birds were out-of-sight of
 land. Our results suggest that improvement in wind drift
 compensation is not solely the product of either physical maturation or
 general improvements in flight control. Instead, we believe it is
 likely that they reflect how frigatebirds learn to process sensory
 information so as to reduce wind drift and maintain a constant course
 during goalward movement. 
description: GPS Tracking Tracking was carried out on Europa Island in the
 Mozambique Channel (-22.36oN, 40.37oE) on 13 adult female and 10 juvenile
 frigatebirds (see figure 1). Adult males were not used in this analysis as
 their role in chick provisioning is limited and, hence, they show little
 homing motivation. Devices measured 130×30×12mm and weighed 30g (PS-RF,
 e-obs GmbH, Munich, Germany), representing between 1.88%–3.55% of the
 frigatebirds' mass. Devices were deployed dorsally using Tesa tape
 and were set to record location every 2 or 5 minutes. 3-dimensional
 accelerometer data were also gathered but is not presented here (27).
 Environmental and landmark cue data Wind data were derived from the NOAA
 Global Forecast System at a temporal resolution of 3-hours and a spatial
 resolution of 0.5o longitude and 0.5o latitude. Whether or not birds could
 see any piece of land was ascertained using their altitude, measured using
 GPS and smoothed using a rolling median over a window of 4 consecutive
 fixes; the elevation of local topography, derived from USGS Global
 Multi-resolution Terrain Elevation Data; and the curvature of Earth.
 Smoothed altitude was used in analyses as the GPS-derived altitudinal
 error is substantially higher than that observed in both the longitudinal
 and latitudinal dimensions (28, 29). Birds were assumed to be able to see
 land if a line of-sight could be drawn between their position and the
 maximum elevation of any piece of land without the Earth’s surface
 intervening. GPS points taken at night were removed from the analysis
 because it was not known whether reduced visual salience might affect
 access to landmark cues, and there were insufficient night-time GPS points
 to statistically test for an effect of daytime/night-time (980 individual
 fixes, representing only 24 trips from 6 unique individuals). Analysis
 including GPS fixes taken at night is included in the supplementary
 materials. Track processing, statistics and analysis All statistics and
 processing were conducted in R (30). Tracks were interpolated using a
 cubic spline function (31) so that fixes were positioned at precise
 5-minute intervals. Tracks were also divided into trips out from the
 colony, with a trip defined as a continuous set of points recorded
 &gt; 500m from the island’s coastline with a maximum distance from the
 colony of &gt; 3km. Since juveniles were tracked from their very first
 trips to sea, for a given trip we attempted to quantify the experience of
 the bird at that point in its development, measuring experience as the
 number of trips the focal bird had been on prior to the trip in question.
 In total, 19,732 interpolated GPS fixes were used in the analysis of
 fledgling frigatebirds, representing 1001 trips from 10 individual birds
 (with a mean of 100 and a median of 122 trips per individual), whilst
 35,430 interpolated GPS fixes were used in the analysis of adult
 frigatebirds, representing 345 trips from 13 individuals (with a mean of
 26 and a median of 12 trips per individual). We chose to analyse only the
 homing sections of trajectories as we had an a priori expectation of where
 birds were aiming for. Because we had no expectations about the form of
 homing behaviour in frigatebirds, we conservatively defined homing as any
 points that occurred after the maximum distance to the colony was recorded
 on the trajectory. Due to the mechanisms by which frigatebirds generate
 lift (principally thermalling) and search for prey items (area restricted
 search behaviour) we expected individuals of all ages to engage in
 tortuous, non-navigational behaviours (21) . Track tortuosity was measured
 using the rolling standard deviation of track bearing over a window of 5
 consecutive fixes, and non-navigational behaviours were parsed out using a
 mixture model to separate GPS fixes into 2 groups based on tortuosity (32,
 33). Only points with tortuosity lower than the mixture model-defined
 cut-off (of 52o) were retained for use in navigational analyses. We
 repeated all analyses with multiple tortuosity cut-off points so as to
 ensure the significance of any findings were robust and unbiased by the
 threshold at which points were removed based on tortuosity (see
 supplementary materials). For each point along a homing track, a beeline
 direction to the colony was assigned along the Great Circle route, from
 which instantaneous deflection was calculated (34). Orientation behaviour
 was modelled using this instantaneous deflection from the beeline
 (measured in degrees on a -180 o to 180o scale) as a response variable.
 From the calculated beeline direction home, the cross-beeline and
 along-beeline wind components were calculated per interpolated GPS
 position. Using these wind components, we modelled the effect of wind
 drift in a 3-way interaction between the cross-beeline wind component,
 fledgling experience and whether or not the bird could see land as a
 binary factor (see figure 2). The along-beeline wind component was also
 included in all models. This was because we expected that an increased
 headwind component might reduce groundspeed, thus halting a bird’s forward
 progress and increasing instantaneous deflection per unit crosswind. By
 including the along-beeline component we, therefore, sought to standardise
 model output coefficients with respect to the along-beeline wind component
 so as the results presented were not the result of a confound between any
 variables of interest and the along-beeline wind component. The effect of
 wind drift was modelled using linear mixed-effects models with trip ID,
 nested within bird ID, used as random effects (34, 35). P-values were
 calculated using likelihood ratio tests between the hypothesised
 (alternative) model and a null model that did not contain the variable or
 interaction of interest.