Abstract:
High-resolution animal location data are increasingly available, requiring
analytical approaches and statistical tools that can accommodate the temporal structure and
transient dynamics (non-stationarity) inherent in natural systems. Traditional analyses often
assume uncorrelated or weakly correlated temporal structure in the velocity (net displacement)
time series constructed using sequential location data. We propose that frequency and time–frequency domain methods, embodied by Fourier and wavelet transforms, can serve as useful
probes in early investigations of animal movement data, stimulating new ecological insight
and questions. We introduce a novel movement model with time-varying parameters to study
these methods in an animal movement context. Simulation studies show that the spectral
signature given by these methods provides a useful approach for statistically detecting and
characterizing temporal dependency in animal movement data. In addition, our simulations
provide a connection between the spectral signatures observed in empirical data with null
hypotheses about expected animal activity. Our analyses also show that there is not a specific
one-to-one relationship between the spectral signatures and behavior type and that departures
from the anticipated signatures are also informative. Box plots of net displacement arranged
by time of day and conditioned on common spectral properties can help interpret the spectral
signatures of empirical data. The first case study is based on the movement trajectory of a lion
(Panthera leo) that shows several characteristic daily activity sequences, including an active–rest cycle that is correlated with moonlight brightness. A second example based on six pairs of
African buffalo (Syncerus caffer) illustrates the use of wavelet coherency to show that their
movements synchronize when they are within ;1 km of each other, even when individual
movement was best described as an uncorrelated random walk, providing an important spatial
baseline of movement synchrony and suggesting that local behavioral cues play a strong role
in driving movement patterns. We conclude with a discussion about the role these methods
may have in guiding appropriately flexible probabilistic models connecting movement with
biotic and abiotic covariates.
