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Recent theory suggests that animals should switch facultatively among canonical movement modes as a complex function of internal state, landscape characteristics, motion capacity, and navigational capacity. We tested the generality of this paradigm for free-ranging elk (Cervus elaphus) over 5 orders of magnitude in time (minutes to years) and space (meters to 100 km). At the coarsest spatiotemporal scale, elk shifted from a dispersive to a home-ranging phase over the course of 1-3 years after introduction into a novel environment. At intermediate spatiotemporal scales, elk continued to alternate between movement modes. During the dispersive phase, elk alternated between encamped and exploratory modes, possibly linked to changes in motivational goals from foraging to social bonding. During the home-ranging phase, elk movements were characterized by a complex interplay between attraction to preferred habitat types and memory of previous movements across the home-range. At the finest temporal and spatial scale, elk used area-restricted search while browsing, interspersed with less sinuous paths when not browsing. Encountering a patch of high-quality food plants triggered the switch from one mode to the next, creating biphasic movement dynamics that were reinforced by local resource heterogeneity. These patterns suggest that multiphasic structure is fundamental to the movement patterns of elk at all temporal and spatial scales tested.

Animal movement paths are often thought of as a confluence of behavioral processes and landscape patterns. Yet it has proven difficult to develop frameworks for analyzing animal movement that can test these interactions. Here we describe a novel method for fitting movement models to data that can incorporate diverse aspects of landscapes and behavior. Using data from five elk (Cervus canadensis) reintroduced to central Ontario, we employed artificial neural networks to estimate movement probability kernels as functions of three landscape-behavioral processes. These consisted of measures of the animals' response to the physical spatial structure of the landscape, the spatial variability in resources, and memory of previously visited locations. The results support the view that animal movement results from interactions among elements of landscape structure and behavior, motivating context-dependent movement probabilities, rather than from successive realizations of static distributions, as some traditional models of movement and resource selection assume. Flexible, nonlinear models may thus prove useful in understanding the mechanisms controlling animal movement patterns.