Abstract:
Understanding the environmental factors influencing animal movements is
fundamental to theoretical and applied research in the field of movement ecology. Studies
relating fine-scale movement paths to spatiotemporally structured landscape data, such as
vegetation productivity or human activity, are particularly lacking despite the obvious
importance of such information to understanding drivers of animal movement. In part, this
may be because few approaches provide the sophistication to characterize the complexity of
movement behavior and relate it to diverse, varying environmental stimuli. We overcame this
hurdle by applying, for the first time to an ecological question, a finite impulse–response
signal-filtering approach to identify human and natural environmental drivers of movements
of 13 free-ranging African elephants (Loxodonta africana) from distinct social groups collected
over seven years. A minimum mean-square error (MMSE) estimation criterion allowed
comparison of the predictive power of landscape and ecological model inputs. We showed that
a filter combining vegetation dynamics, human and physical landscape features, and previous
movement outperformed simpler filter structures, indicating the importance of both dynamic
and static landscape features, as well as habit, on movement decisions taken by elephants.
Elephant responses to vegetation productivity indices were not uniform in time or space,
indicating that elephant foraging strategies are more complex than simply gravitation toward
areas of high productivity. Predictions were most frequently inaccurate outside protected area
boundaries near human settlements, suggesting that human activity disrupts typical elephant
movement behavior. Successful management strategies at the human–elephant interface,
therefore, are likely to be context specific and dynamic. Signal processing provides a promising
approach for elucidating environmental factors that drive animal movements over large time
and spatial scales.
