Modeling interdependent animal movement in continuous time

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.

• Publication year

  2016

• Venue

  Biometrics

• Publication date

  2016-06-01

• Fields of study

  Mathematics, Computer Science, Medicine

• Identifiers
  DOI 10.1111/biom.12454PMID 26812666
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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