Data from: From fine-scale foraging to home ranges: a semi-variance approach to identifying movement modes across spatiotemporal scales

Understanding animal movement is a key challenge in ecology and
conservation biology. Relocation data often represent a complex mixture of
different movement behaviors, and reliably decomposing this mix into its
component parts is an unresolved problem in movement ecology. Traditional
approaches, such as composite random walk models, require that the
timescales characterizing the movement are all similar to the usually
arbitrary data-sampling rate. Movement behaviors such as long-distance
searching and fine-scale foraging, however, are often intermixed but
operate on vastly different spatial and temporal scales. An approach that
integrates the full sweep of movement behaviors across scales is currently
lacking. Here we show how the semivariance function (SVF) of a stochastic
movement process can both identify multiple movement modes and solve the
sampling rate problem. We express a broad range of continuous-space,
continuous-time stochastic movement models in terms of their SVFs, connect
them to relocation data via variogram regression, and compare them using
standard model selection techniques. We illustrate our approach using
Mongolian gazelle relocation data and show that gazelle movement is
characterized by ballistic foraging movements on a 6-h timescale, fast
diffusive searching with a 10-week timescale, and asymptotic diffusion
over longer timescales.