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

Toby A. Patterson*, Alison Parton, Roland Langrock, Paul G. Blackwell, Len Thomas, Ruth King

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

120 Citations (Scopus)
9 Downloads (Pure)

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 among 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 analyse 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 towards what are arguably overly complex modelling approaches which are inaccessible to ecologists, unwieldy with large data sets or not based on 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 Lévy 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.

Original languageEnglish
Pages (from-to)399-438
Number of pages40
JournalAdvances in Statistical Analysis
Volume101
Issue number4
Early online date4 Jul 2017
DOIs
Publication statusPublished - Oct 2017

Keywords

  • Hidden Markov model
  • Measurement error
  • Ornstein–Uhlenbeck process
  • State-space model
  • Stochastic differential equation
  • Time series

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