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

A. Parton () and P. G. Blackwell ()
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A. Parton: University of Sheffield
P. G. Blackwell: University of Sheffield

Journal of Agricultural, Biological and Environmental Statistics, 2017, vol. 22, issue 3, No 10, 373-392

Abstract: 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.

Keywords: Movement modelling; Switching behaviour; Random walk; GPS data; Markov chain Monte Carlo; Elk (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s13253-017-0286-5

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