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A general hidden state random walk model for animal movement

Author

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  • Nicosia, Aurélien
  • Duchesne, Thierry
  • Rivest, Louis-Paul
  • Fortin, Daniel

Abstract

A general hidden state random walk model is proposed to describe the movement of an animal that takes into account movement taxis with respect to features of the environment. A circular–linear process models the direction and distance between two consecutive localizations of the animal. A hidden process structure accounts for the animal’s change in movement behavior. The originality of the proposed approach is that several environmental targets can be included in the directional model. An EM algorithm that enables prediction of the hidden states of the process is devised to fit this model. An application to the analysis of the movement of caribou in Canada’s boreal forest is presented.

Suggested Citation

  • Nicosia, Aurélien & Duchesne, Thierry & Rivest, Louis-Paul & Fortin, Daniel, 2017. "A general hidden state random walk model for animal movement," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 76-95.
    • Handle: RePEc:eee:csdana:v:105:y:2017:i:c:p:76-95
    DOI: 10.1016/j.csda.2016.07.009
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    References listed on IDEAS

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    1. Louis-Paul Rivest & Thierry Duchesne & Aurélien Nicosia & Daniel Fortin, 2016. "A general angular regression model for the analysis of data on animal movement in ecology," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(3), pages 445-463, April.
    2. Langrock, R. & Zucchini, W., 2011. "Hidden Markov models with arbitrary state dwell-time distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 715-724, January.
    3. K. V. Mardia, 1999. "Directional statistics and shape analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 949-957.
    4. Thierry Duchesne & Daniel Fortin & Louis-Paul Rivest, 2015. "Equivalence between Step Selection Functions and Biased Correlated Random Walks for Statistical Inference on Animal Movement," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
    5. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    6. Ichiro Ken Shimatani & Ken Yoda & Nobuhiro Katsumata & Katsufumi Sato, 2012. "Toward the Quantification of a Conceptual Framework for Movement Ecology Using Circular Statistical Modeling," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-13, November.
    7. Ingrassia, Salvatore & Rocci, Roberto, 2011. "Degeneracy of the EM algorithm for the MLE of multivariate Gaussian mixtures and dynamic constraints," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1715-1725, April.
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    Cited by:

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    2. Jonathan U Harrison & Ruth E Baker, 2018. "The impact of temporal sampling resolution on parameter inference for biological transport models," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-30, June.
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