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.
Citation Information
| Publication Year | 2021 |
|---|---|
| Title | Agent-based models for collective animal movement: Proximity-induced state switching |
| DOI | 10.1007/s13253-021-00456-0 |
| Authors | Andrew B. Hoegh, Frank T. van Manen, Mark A. Haroldson |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Journal of Agricultural, Biological and Environmental Statistics |
| Index ID | 70224288 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Northern Rocky Mountain Science Center |
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Frank T van Manen, Ph.D.
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Frank T van Manen, Ph.D.
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