Abstract

This article presents a new approach to modeling group animal movement in continuous time. The movement of a group of animals is modeled as a multivariate Ornstein Uhlenbeck diffusion process in a high-dimensional space. Each individual of the group is attracted to a leading point which is generally unobserved, and the movement of the leading point is also an Ornstein Uhlenbeck process attracted to an unknown attractor. The Ornstein Uhlenbeck bridge is applied to reconstruct the location of the leading point. All movement parameters are estimated using Markov chain Monte Carlo sampling, specifically a Metropolis Hastings algorithm. We apply the method to a small group of simultaneously tracked reindeer, Rangifer tarandus tarandus, showing that the method detects dependency in movement between individuals.

Keywords

Animal movement; Bayesian inference; Multivariate Ornstein Uhlenbeck process; Ornstein Uhlenbeck bridge; Stochastic differential equation

Published in

Biometrics
2016, Volume: 72, number: 2, pages: 315-324
Publisher: WILEY-BLACKWELL

SLU Authors

• Skarin, Anna

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

UKÄ Subject classification

Computational Mathematics
Behavioral Sciences Biology
Probability Theory and Statistics


Publication identifier

DOI: https://doi.org/10.1111/biom.12454

Permanent link to this page (URI)

https://res.slu.se/id/publ/77053