Data from: State-space modelling of the flight behaviour of a soaring bird provides new insights to migratory strategies

1. Characterizing the spatiotemporal variation of animal behaviour can
elucidate the way individuals interact with their environment and allocate
energy. Increasing sophistication of tracking technologies paired with
novel analytical approaches allows the characterisation of movement
dynamics even when an individual is not directly observable. 2. In this
study, high-resolution movement data collected via global positioning
system (GPS) tracking in three dimensions were paired with topographical
information and used in a Bayesian state-space model to describe the
flight modes of migrating golden eagles (Aquila chrysaetos) in eastern
North America. 3. Our model identified five functional behavioural states,
two of which were previously undescribed variations on thermal soaring.
The other states comprised gliding, perching and orographic soaring.
States were discriminated by movement features in the horizontal (step
length and turning angle) and vertical (change in altitude) planes, and by
the association with ridgelines promoting wind deflection. Tracked eagles
spent 2%, 31%, 38%, 9% and 20% of their day time in directed thermal
soaring, gliding, convoluted thermal soaring, perching and orographic
soaring, respectively. The analysis of the relative occurrence of these
flight modes highlighted yearly, seasonal, age, individual and sex
differences in flight strategy and performance. Particularly, less
energy-efficient orographic soaring was more frequent in autumn, when
thermals were less available. Adult birds were also better at optimising
energy efficiency than sub-adults. 4. Our approach represents the first
example of a state-space model for bird flight mode using altitude data in
conjunction with horizontal locations, and is applicable to other flying
organisms where similar data are available. The ability to describe animal
movements in a three-dimensional habitat is critical to advance our
understanding of the functional processes driving animals’ decisions.