AStA Advances in Statistical Analysis

, Volume 101, Issue 4, pp 399–438 | Cite as

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

  • Toby A. PattersonEmail author
  • Alison Parton
  • Roland Langrock
  • Paul G. Blackwell
  • Len Thomas
  • Ruth King
Original Paper


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.


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



We thank Geoff Hosack and two anonymous referees for their useful feedback on this manuscript. We also gratefully acknowledge the editorial input of David Borchers which greatly improved and clarified the points made in this paper.


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Copyright information

© Crown Copyright 2017

Authors and Affiliations

  • Toby A. Patterson
    • 1
    Email author
  • Alison Parton
    • 2
  • Roland Langrock
    • 3
  • Paul G. Blackwell
    • 2
  • Len Thomas
    • 4
  • Ruth King
    • 5
  1. 1.CSIRO Oceans and AtmosphereHobartAustralia
  2. 2.School of Mathematics and StatisticsUniversity of SheffieldSheffieldUK
  3. 3.Department of Business Administration and EconomicsBielefeld UniversityBielefeldGermany
  4. 4.School of Mathematics and StatisticsUniversity of St AndrewsSt AndrewsUK
  5. 5.School of MathematicsUniversity of EdinburghEdinburghUK

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