Biology subjects

Hedenstrom, A.

Publications and source records attributed to Hedenstrom, A..

3 recordsLinked to original sources

bioRxiv2024

Flight performance, activity and behaviour of breeding pied flycatchers in the wild, revealed with accelerometers and machine learning

Flight behaviours have been extensively studied from different angles such as their kinematics, aerodynamics and more general their migration pattern. Nevertheless, much is still unknown about the daily flight activity of birds, in terms of their performance, behaviour and the potential differences between males and females. The recent development of miniaturized accelerometers allows us a glimpse into the daily life of a songbird. Here, we tagged 26 pied flycatchers (Ficedula hypoleuca) with accelerometers and analysed using machine learning approaches their flight performance, activity and behaviour during their chick rearing period. We found that during two hours of foraging chick-rearing pied flycatchers were flying 13.7% of the time. Almost all flights (>99%) were short flights lasting less than 10s. Flight activity changed throughout the day and was highest in the morning and lowest in the early afternoon. Male pied flycatcher had lower wing loading than females, and peak flight accelerations were inversely correlated with wing loading. Despite this, we found no significant differences in flight activity and performance between sexes. This suggests that males possess a higher potential flight performance, which they not fully utilized during foraging flights. Our results thus suggest that male and female pied flycatcher invest equally in parental care, but that this comes at a reduced cost by the male, due to their higher flight performance potential.

animal behavior and cognition↗

bioRxiv2021

Fine-scale changes in speed and altitude suggest protean movements in homing pigeon flights

The flight speeds that animals should adopt to minimise energy expenditure in different scenarios can be predicted by the curve of power against speed. However, relatively few studies have examined how animals respond to the landscape below them, which could affect speed through modifications in climb rate and perceived predation risk. We equipped homing pigeons (Columba livia) with high frequency loggers to examine how flight speed varies in relation to topography and land cover, predicting that these parameters may have a substantial impact on flight speed and hence cost. Pigeons showed mixed evidence for an energy saving strategy, as they minimised climb rates by starting their ascent ahead of hills, but selected rapid speeds in their ascents. Birds did not modify their speed substantially in relation to land cover, but higher speeds were observed during descending flight, highlighting the importance to consider the rate of change in altitude before estimating power use from speed. Finally, we document an unexpected variability in speed and altitude over fine scales; a source of substantial inefficiency. We suggest this may a form of protean behaviour adopted to reduce predation risk when flocking is not an option, and that such a strategy could be widespread.

animal behavior and cognition↗

bioRxiv2019

Optic flow helps explain gulls’ altitude control over seas

For studies of how flying animals control their flight, seabirds are of particular interest to track with a biologger because they forage offshore where the visual environment can be simply modeled by a flat world textured by waves. This study suggests that optic flow can explain gulls altitude control over seas. In particular, a new flight model that includes both energy and optical invariants (called the ventral optic flow regulation) explain the dynamics of gulls altitude control during offshore takeoff and cruising flight. A linear statistical model applied to 352 flights from 16 individual lesser black backed gulls (Larus fuscus) gave a strong correlation between wind assistance and gulls altitude. Thereafter, an optic flow-based flight model was applied to 18 offshore takeoff flights from 1. individual gulls. By introducing an upper limit in climb rate in a non-linear first order parametric model on the gulls elevation dynamics, coupled with an optic-flow set-point, the predicted altitude gives an optimized fit factor value of 63% on average (min value: 30%, max value: 83%) with respect to GPS data. We conclude that the optic-flow regulation principle (here running close to 25{degrees}/s) allows gulls to adjust their altitude over sea without having to directly measure their current altitude.

neuroscience↗