Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions

Authors

  • Roland Langrock,

    1. Centre for Research into Ecological and Environmental Modelling, School of Mathematics and Statistics, The Observatory, Buchanan Gardens, University of St Andrews, St Andrews, Fife KY16 PLZ Scotland, United Kingdom
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  • Ruth King,

    1. Centre for Research into Ecological and Environmental Modelling, School of Mathematics and Statistics, The Observatory, Buchanan Gardens, University of St Andrews, St Andrews, Fife KY16 PLZ Scotland, United Kingdom
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  • Jason Matthiopoulos,

    1. Scottish Oceans Institute, School of Biology, University of St Andrews, St Andrews, Fife KY16 8LB Scotland, United Kingdom
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  • Len Thomas,

    1. Centre for Research into Ecological and Environmental Modelling, School of Mathematics and Statistics, The Observatory, Buchanan Gardens, University of St Andrews, St Andrews, Fife KY16 PLZ Scotland, United Kingdom
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  • Daniel Fortin,

    1. Centre d'Étude de la Forêt and Département de Biologie, Université Laval, Québec City, Quebec G1V 0A6 Canada
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  • Juan M. Morales

    1. Ecotono, INIBIOMA-CONICET, Universidad Nacional del Comahue, Quintral 1250, 8400 Bariloche, Argentina
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  • Corresponding Editor: B. D. Inouye.

Abstract

We discuss hidden Markov-type models for fitting a variety of multistate random walks to wildlife movement data. Discrete-time hidden Markov models (HMMs) achieve considerable computational gains by focusing on observations that are regularly spaced in time, and for which the measurement error is negligible. These conditions are often met, in particular for data related to terrestrial animals, so that a likelihood-based HMM approach is feasible. We describe a number of extensions of HMMs for animal movement modeling, including more flexible state transition models and individual random effects (fitted in a non-Bayesian framework). In particular we consider so-called hidden semi-Markov models, which may substantially improve the goodness of fit and provide important insights into the behavioral state switching dynamics. To showcase the expediency of these methods, we consider an application of a hierarchical hidden semi-Markov model to multiple bison movement paths.

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