Modelling group dynamic animal movement

Authors

  • Roland Langrock,

    Corresponding author
    1. Center for Research into Ecological and Environmental Modelling, School of Mathematics and Statistics, University of St Andrews, St Andrews, UK
    • Correspondence author. Roland Langrock, CREEM, The Observatory, Buchanan Gardens, University of St Andrews, St Andrews, KY16 9LZ, UK. E-mail: roland@mcs.st-and.ac.uk

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  • J. Grant C. Hopcraft,

    1. Center for Research into Ecological and Environmental Modelling, School of Mathematics and Statistics, University of St Andrews, St Andrews, UK
    2. Boyd Orr Centre for Population and Ecosystem Health, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
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  • Paul G. Blackwell,

    1. School of Mathematics and Statistics, University of Sheffield, Sheffield, UK
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  • Victoria Goodall,

    1. South African Environmental Observation Network, Fynbos Node, South Africa
    2. School of Statistics and Actuarial Science, University of the Witwatersrand, Witwatersrand, South Africa
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  • Ruth King,

    1. Center for Research into Ecological and Environmental Modelling, School of Mathematics and Statistics, University of St Andrews, St Andrews, UK
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  • Mu Niu,

    1. School of Mathematics and Statistics, University of Sheffield, Sheffield, UK
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  • Toby A. Patterson,

    1. Commonwealth Scientific and Industrial Research Organisation, Wealth from Oceans Research Flagship, Hobart, Tas., Australia
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  • Martin W. Pedersen,

    1. National Institute of Aquatic Resources, Technical University of Denmark, Charlottenlund, Denmark
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  • Anna Skarin,

    1. Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, Uppsala, Sweden
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  • Robert S. Schick

    1. Center for Research into Ecological and Environmental Modelling, School of Mathematics and Statistics, University of St Andrews, St Andrews, UK
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Summary

  1. Group dynamics are a fundamental aspect of many species' movements. The need to adequately model individuals' interactions with other group members has been recognized, particularly in order to differentiate the role of social forces in individual movement from environmental factors. However, to date, practical statistical methods, which can include group dynamics in animal movement models, have been lacking.
  2. We consider a flexible modelling framework that distinguishes a group-level model, describing the movement of the group's centre, and an individual-level model, such that each individual makes its movement decisions relative to the group centroid. The basic idea is framed within the flexible class of hidden Markov models, extending previous work on modelling animal movement by means of multistate random walks.
  3. While in simulation experiments parameter estimators exhibit some bias in non-ideal scenarios, we show that generally the estimation of models of this type is both feasible and ecologically informative.
  4. We illustrate the approach using real movement data from 11 reindeer (Rangifer tarandus). Results indicate a directional bias towards a group centroid for reindeer in an encamped state. Though the attraction to the group centroid is relatively weak, our model successfully captures group-influenced movement dynamics. Specifically, as compared to a regular mixture of correlated random walks, the group dynamic model more accurately predicts the non-diffusive behaviour of a cohesive mobile group.
  5. As technology continues to develop, it will become easier and less expensive to tag multiple individuals within a group in order to follow their movements. Our work provides a first inferential framework for understanding the relative influences of individual versus group-level movement decisions. This framework can be extended to include covariates corresponding to environmental influences or body condition. As such, this framework allows for a broader understanding of the many internal and external factors that can influence an individual's movement.

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