Agent-Based Models for Collective Animal Movement: Proximity-Induced State Switching

Andrew Hoegh (), Frank T. Manen and Mark Haroldson
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Andrew Hoegh: Montana State University
Frank T. Manen: U.S. Geological Survey Interagency Grizzly Bear Study Team Northern Rocky Mountain Science Center
Mark Haroldson: U.S. Geological Survey Interagency Grizzly Bear Study Team Northern Rocky Mountain Science Center

Journal of Agricultural, Biological and Environmental Statistics, 2021, vol. 26, issue 4, No 3, 560-579

Abstract: Abstract Animal movement is a complex phenomenon where individual movement patterns can be influenced by a variety of factors including the animal’s current activity, available terrain and habitat, and locations of other animals. Motivated by modeling grizzly bear movement in the Greater Yellowstone Ecosystem, this article presents an agent-based model represented in a state-space framework for collective animal movement. The novel contribution of this work is a collective animal movement model that captures interactions between animals that can trigger changes in movement patterns, such as when a dominant grizzly bear may cause another subordinate bear to temporarily leave an area. The modeling framework enables learning different movement patterns through a state-space representation with particle-MCMC methods for fully Bayesian model fitting and the prediction of future animal movement behaviors.Supplementary materials accompanying this paper appear online.

Keywords: Bayesian statistics; MCMC; Particle-MCMC (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13253-021-00456-0

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