Windscapes shape seabird instantaneous energy costs but adult behavior buffers impact on offspring

Abstract

Background: Windscapes affect energy costs for flying animals, but animals can adjust their behavior to accommodate wind-induced energy costs. Theory predicts that flying animals should decrease air speed to compensate for increased tailwind speed and increase air speed to compensate for increased crosswind speed. In addition, animals are expected to vary their foraging effort in time and space to maximize energy efficiency across variable windscapes.

Results: We examined the influence of wind on seabird (thick-billed murre Uria lomvia and black-legged kittiwake Rissa tridactyla) foraging behavior. Airspeed and mechanical flight costs (dynamic body acceleration and wing beat frequency) increased with headwind speed during commuting flights. As predicted, birds adjusted their airspeed to compensate for crosswinds and to reduce the effect of a headwind, but they could not completely compensate for the latter. As we were able to account for the effect of sampling frequency and wind speed, we accurately estimated commuting flight speed with no wind as 16.6 ms(?1) (murres) and 10.6 ms(?1) (kittiwakes). High winds decreased delivery rates of schooling fish (murres), energy (murres) and food (kittiwakes) but did not impact daily energy expenditure or chick growth rates. During high winds, murres switched from feeding their offspring with schooling fish, which required substantial above-water searching, to amphipods, which required less above-water searching.

Conclusions: Adults buffered the adverse effect of high winds on chick growth rates by switching to other food sources during windy days or increasing food delivery rates when weather improved.

Figure 1

Average measured ground speed as a proportion of the minimum sampling frequency declines with sub-sampling frequency for (a) murres and (b) kittiwakes, after accounting for random error associated with GPS signal. Each symbol represents one of six birds chosen randomly from the dataset. Least-squares exponential functions are shown.

Figure 2

Typical (a) murre and (b) kittiwake traces from accelerometers. Both show two flights with the typical increase in acceleration at the start of each flight. For murres, depth is also shown. (c) Dynamic body acceleration as a function of time since the start of the flight (N = 10 individuals for each species).

Figure 3

Representative acceleration traces (longitudinal axis, used to calculate Fourier transforms, in bold) of flying (a) murres and (b) kittiwakes. Fourier spectra from the same flights in (c) murres and (d) kittiwakes.

Figure 4

Ground speed increased with the magnitude of the component of wind speed in the direction of travel (¿tailwind¿) for both (a) murres (N = 35) and (b) kittiwakes (N = 10).

Figure 5

Air speed as a function of wind speed and angle between flight track and wind for kittiwakes and murres.

Figure 6

Wing beat frequency and dynamic body acceleration were correlated for both (a) murres (N = 10) and (b) kittiwakes (N = 10). Wing beat frequency increased with wind speed in the direction of travel for both (c) murres (N = 10) and (d) kittiwakes (N = 10). For murres, inbound and outbound wing beat frequency differed significantly, and so we show both groups. In (c) and (d), dynamic acceleration is calculated based on the regressions in (a) and (b) and airspeed is calculated based on regressions in Figure 4.

Figure 7

Energy delivery rate for (a) murres and (b) kittiwakes; residual chick growth rate on age for (c) murres and (d) kittiwkes; and residual proportion of (e) amphipods and (f) schooling fish, after accounting for time of day and date, relative to the difference between wind speed and average wind speed for a particular date.