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Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time

Author

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  • A. Parton

    (University of Sheffield)

  • P. G. Blackwell

    (University of Sheffield)

Abstract

Mechanistic modelling of animal movement is often formulated in discrete time despite problems with scale invariance, such as handling irregularly timed observations. A natural solution is to formulate in continuous time, yet uptake of this has been slow. This lack of implementation is often excused by a difficulty in interpretation. Here we aim to bolster usage by developing a continuous-time model with interpretable parameters, similar to those of popular discrete-time models that use turning angles and step lengths. Movement is defined by a joint bearing and speed process, with parameters dependent on a continuous-time behavioural switching process, creating a flexible class of movement models. Methodology is presented for Markov chain Monte Carlo inference given irregular observations, involving augmenting observed locations with a reconstruction of the underlying movement process. This is applied to well-known GPS data from elk (Cervus elaphus), which have previously been modelled in discrete time. We demonstrate the interpretable nature of the continuous-time model, finding clear differences in behaviour over time and insights into short-term behaviour that could not have been obtained in discrete time.

Suggested Citation

  • A. Parton & P. G. Blackwell, 2017. "Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 373-392, September.
  • Handle: RePEc:spr:jagbes:v:22:y:2017:i:3:d:10.1007_s13253-017-0286-5
    DOI: 10.1007/s13253-017-0286-5
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    References listed on IDEAS

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    1. Amy Hurford, 2009. "GPS Measurement Error Gives Rise to Spurious 180° Turning Angles and Strong Directional Biases in Animal Movement Data," PLOS ONE, Public Library of Science, vol. 4(5), pages 1-12, May.
    2. Breed, Greg A. & Costa, Daniel P. & Jonsen, Ian D. & Robinson, Patrick W. & Mills-Flemming, Joanna, 2012. "State-space methods for more completely capturing behavioral dynamics from animal tracks," Ecological Modelling, Elsevier, vol. 235, pages 49-58.
    3. Harris, Keith J. & Blackwell, Paul G., 2013. "Flexible continuous-time modelling for heterogeneous animal movement," Ecological Modelling, Elsevier, vol. 255(C), pages 29-37.
    4. P. G. Blackwell, 2003. "Bayesian inference for Markov processes with diffusion and discrete components," Biometrika, Biometrika Trust, vol. 90(3), pages 613-627, September.
    5. Ann E. McKellar & Roland Langrock & Jeffrey R. Walters & Dylan C. Kesler, 2015. "Using mixed hidden Markov models to examine behavioral states in a cooperatively breeding bird," Behavioral Ecology, International Society for Behavioral Ecology, vol. 26(1), pages 148-157.
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