Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges

TL;DR

This paper reviews key statistical models for analyzing individual animal movement data, emphasizing the need for balanced, accessible, and statistically sound approaches amidst increasing data complexity.

Contribution

It provides an overview of popular models like hidden Markov, state-space, and diffusion processes, advocating for their core use in animal movement analysis.

Findings

Core models include hidden Markov, state-space, and diffusion processes.

Current trends favor overly complex or simplistic models, risking misinterpretation.

Balanced, intermediate complexity models are recommended for practical use.

Abstract

With the influx of complex and detailed tracking data gathered from electronic tracking devices, the analysis of animal movement data has recently emerged as a cottage industry amongst biostatisticians. New approaches of ever greater complexity are continue to be added to the literature. In this paper, we review what we believe to be some of the most popular and most useful classes of statistical models used to analyze individual animal movement data. Specifically we consider discrete-time hidden Markov models, more general state-space models and diffusion processes. We argue that these models should be core components in the toolbox for quantitative researchers working on stochastic modelling of individual animal movement. The paper concludes by offering some general observations on the direction of statistical analysis of animal movement. There is a trend in movement ecology toward what are arguably overly-complex modelling approaches which are inaccessible to ecologists, unwieldy with large data sets or not based in mainstream statistical practice. Additionally, some analysis methods developed within the ecological community ignore fundamental properties of movement data, potentially leading to misleading conclusions about animal movement. Corresponding approaches, e.g. based on L\'evy walk-type models, continue to be popular despite having been largely discredited. We contend that there is a need for an appropriate balance between the extremes of either being overly complex or being overly simplistic, whereby the discipline relies on models of intermediate complexity that are usable by general ecologists, but grounded in well-developed statistical practice and efficient to fit to large data sets.