Exact Bayesian inference for animal movement in continuous time

Blackwell, P. G., Niu, M., Lambert, M. S., LaPoint, S. D. and O'Hara, R. B. (2016) Exact Bayesian inference for animal movement in continuous time. Methods in Ecology and Evolution, 7(2), pp. 184-195. (doi:10.1111/2041-210X.12460)

[img]
Preview
Text
128109.pdf - Published Version
Available under License Creative Commons Attribution.

682kB

Abstract

1. It is natural to regard most animal movement as a continuous-time process, generally observed at discrete times. Most existing statistical methods for movement data ignore this; the remainder mostly use discrete-time approximations, the statistical properties of which have not been widely studied, or are limited to special cases. We aim to facilitate wider use of continuous-time modelling for realistic problems. 2. We develop novel methodology which allows exact Bayesian statistical analysis for a rich class of movement models with behavioural switching in continuous time, without any need for time discretization error. We represent the times of changes in behaviour as forming a thinned Poisson process, allowing exact simulation and Markov chain Monte Carlo inference. The methodology applies to data that are regular or irregular in time, with or without missing values. 3. We apply these methods to GPS data from two animals, a fisher (Pekania [Martes] pennanti) and a wild boar (Sus scrofa), using models with both spatial and temporal heterogeneity. We are able to identify and describe differences in movement behaviour across habitats and over time. 4. Our methods allow exact fitting of realistically complex movement models, incorporating environmental information. They also provide an essential point of reference for evaluating other existing and future approximate methods for continuous-time inference.

Item Type:Articles
Additional Information:M. Niu was funded by EPSRC/NERC grant EP/10009171/1 (National Centrefor Statistical Ecology). S. D. LaPoint was funded by the Max-Planck-PolandBiodiversity Fund.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Niu, Dr Mu
Authors: Blackwell, P. G., Niu, M., Lambert, M. S., LaPoint, S. D., and O'Hara, R. B.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Methods in Ecology and Evolution
Publisher:Wiley
ISSN:2041-210X
ISSN (Online):2041-210X
Published Online:29 September 2015
Copyright Holders:Copyright © 2015 The Authors
First Published:First published in Methods in Ecology and Evolution 7(2): 184-195
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record