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June 2021 A continuous-time semi-Markov model for animal movement in a dynamic environment
Devin Johnson, Noel Pelland, Jeremy Sterling
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Ann. Appl. Stat. 15(2): 797-812 (June 2021). DOI: 10.1214/20-AOAS1408


We consider an extension to discrete-space, continuous-time models for animal movement that have previously been presented in the literature. The extension from a continuous-time Markov formulation to a continuous-time semi-Markov formulation allows for the inclusion of temporally dynamic habitat conditions as well as temporally changing movement responses by animals to that environment. We show that, with only a little additional consideration, the Poisson likelihood calculation for the Markov version can still be used within the multiple imputation framework commonly employed for analysis of telemetry data. In addition, we consider a Bayesian model selection methodology within the imputation framework. The model selection method uses a Laplace approximation to the posterior model probability to provide a computationally feasible approach. The full methodology is then used to analyze movements of 15 weaned northern fur seal (Callorhinus ursinus) pups with respect to surface winds, geostrophic currents and sea surface temperature. The highest posterior model probabilities belonged to those models containing only winds and current; SST was not a significant factor for modeling their movement.


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Devin Johnson. Noel Pelland. Jeremy Sterling. "A continuous-time semi-Markov model for animal movement in a dynamic environment." Ann. Appl. Stat. 15 (2) 797 - 812, June 2021.


Received: 1 May 2019; Revised: 1 October 2020; Published: June 2021
First available in Project Euclid: 16 July 2021

Digital Object Identifier: 10.1214/20-AOAS1408

Keywords: Animal telemetry , movement model , multiple imputation , Northern fur seal , semi-Markov model

Rights: Copyright © 2021 Institute of Mathematical Statistics


Vol.15 • No. 2 • June 2021
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