title: Modeling interdependent animal movement in continuous time.
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.
authors.affiliation: School of Mathematics & Statistics, University of Sheffield, Sheffield S3 7RH, UK.
authors.forename: Mu
authors.initials: M
authors.lastname: Niu
authors.affiliation: School of Mathematics & Statistics, University of Sheffield, Sheffield S3 7RH, UK.
authors.forename: Paul G
authors.initials: PG
authors.lastname: Blackwell
authors.affiliation: Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, Uppsala, Sweden.
authors.forename: Anna
authors.initials: A
authors.lastname: Skarin
mesh_terms: D000465:Algorithms; D000818:Animals; D000066949:Biobehavioral Sciences; D003198:Computer Simulation; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D009068:Movement; D012053:Reindeer
keywords: Animal movement; Bayesian inference; Multivariate Ornstein Uhlenbeck process; Ornstein Uhlenbeck bridge; Stochastic differential equation