Bayesian Inference for Continuous Time Animal Movement Based on Steps and Turns

Abstract

Although animal locations gained via GPS, etc. are typically observed on a discrete time scale, movement models formulated in continuous time are preferable in order to avoid the struggles experienced in discrete time when faced with irregular observations or the prospect of comparing analyses on different time scales. A class of models able to emulate a range of movement ideas are defined by representing movement as a combination of stochastic processes describing both speed and bearing. A method for Bayesian inference for such models is described through the use of a Markov chain Monte Carlo approach. Such inference relies on an augmentation of the animal's locations in discrete time that have been observed with error, with a more detailed movement path gained via simulation techniques. Analysis of real data on an individual reindeer Rangifer tarandus illustrates the presented methods.

Keywords

Movement modelling; Random walk; Rangifer tarandus; Data augmentation; GPS data

Published in

Springer Proceedings in Mathematics & Statistics
2017, Volume: 194, pages: 223-230 Title: Bayesian statistics in action : BAYSM 2016, Florence, Italy, June 19-21
ISBN: 978-3-319-54083-2, eISBN: 978-3-319-54084-9Publisher: Springer

Conference

3rd Bayesian Young Statisticians Meeting (BAYSM)

SLU Authors

• Skarin, Anna

  • Department of Applied Animal Science and Welfare, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics


Publication identifier

DOI: https://doi.org/10.1007/978-3-319-54084-9_21

Permanent link to this page (URI)

https://res.slu.se/id/publ/93405