Restrictions on roaming Until the past century or so, the movement of wild animals was relatively unrestricted, and their travels contributed substantially to ecological processes. As humans have increasingly altered natural habitats, natural animal movements have been restricted. Tucker et al. examined GPS locations for more than 50 species. In general, animal movements were shorter in areas with high human impact, likely owing to changed behaviors and physical limitations. Besides affecting the species themselves, such changes could have wider effects by limiting the movement of nutrients and altering ecological interactions. Science , this issue p. 466
Movement data provide a window - often our only window - into the cognitive, social and biological processes that underlie the behavioural ecology of animals in the wild. Robust methods for identifying and interpreting distinct modes of movement behaviour are of great importance, but complicated by the fact that movement data are complex, multivariate and dependent. Many different approaches to exploratory analysis of movement have been developed to answer similar questions, and practitioners are often at a loss for how to choose an appropriate tool for a specific question. We apply and compare four methodological approaches: first passage time (FPT), Bayesian partitioning of Markov models (BPMM), behavioural change point analysis (BCPA) and a fitted multistate random walk (MRW) to three simulated tracks and two animal trajectories - a sea lamprey (Petromyzon marinus) tracked for 12 h and a wolf (Canis lupus) tracked for 1 year. The simulations - in which, respectively, velocity, tortuosity and spatial bias change - highlight the sensitivity of all methods to model misspecification. Methods that do not account for autocorrelation in the movement variables lead to spurious change points, while methods that do not account for spatial bias completely miss changes in orientation. When applied to the animal data, the methods broadly agree on the structure of the movement behaviours. Important discrepancies, however, reflect differences in the assumptions and nature of the outputs. Important trade-offs are between the strength of the a priori assumptions (low in BCPA, high in MRW), complexity of output (high in the BCPA, low in the BPMM and MRW) and explanatory potential (highest in the MRW). The animal track analysis suggests some general principles for the exploratory analysis of movement data, including ways to exploit the strengths of the various methods. We argue for close and detailed exploratory analysis of movement before fitting complex movement models.
Animals regularly return to locations such as foraging patches, nests, dens, watering holes, or movement corridors, and these revisited locations are often sites of ecological significance. Analyzing the temporal and spatial pattern of revisitation can lead to important insights into the life history and ecology of populations. We introduce the R package 'recurse' to calculate revisitations to locations in the movement trajectory or other locations for one or multiple individuals. The package also calculates metrics such as residence time and time between visits. It can be used to quantitatively identify frequently used sites (e.g. dens, nests, foraging locations), to examine patterns of revisitation and link them with covariates such as habitat features or climatic data, and to address conservation questions of interest about specific locations. We present an example application with movement trajectory data from a turkey vulture Cathartes aura during the breeding season and demonstrate analyzing recursions, specific locations, seasonal and daily temporal patterns, and visit timing. The 'recurse' package should be of interest both to ecologists looking to analyze their movement data and to conservationists needing site-specific information for management and conservation actions.
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