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.

动物运动是一种复杂的现象，个体运动模式会受到多种因素的影响，包括动物当前的活动、可用的地形和栖息地以及其他动物的位置。受大黄石生态系统中灰熊运动建模的启发，本文提出了一个基于代理的模型，以状态空间框架表示，用于集体动物运动。这项工作的新贡献是一个集体动物运动模型，它捕捉动物之间的相互作用，这些相互作用可以触发运动模式的变化，例如当一只占主导地位的灰熊可能导致另一只从属的熊暂时离开一个区域时。