Statistical models for animal movement provide tools that help ecologists and biologists learn how animals interact with their environment and each other. Efforts to develop increasingly realistic, implementable, and scientifically valuable methods for analyzing remotely observed trajectories have provided practitioners with a wide selection of models to help them understand animal behavior. Increasingly, researchers are interested in studying multiple animals jointly, which requires methods that can account for dependence across individuals. Dependence can arise for many reasons, including shared behavioral tendencies, familial relationships, and direct interactions on the landscape. We provide a synopsis of recent statistical methods for animal movement data applicable to settings in which inference is desired across multiple individuals. Highlights of these approaches include the ability to infer shared behavioral traits across a group of individuals and the ability to infer unobserved social networks summarizing dynamic relationships that manifest themselves in movement decisions. This article is categorized under: Statistical Models > Bayesian Models Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Data: Types and Structure > Social Networks.
|Original language||English (US)|
|Journal||Wiley Interdisciplinary Reviews: Computational Statistics|
|State||Accepted/In press - Jan 1 2020|
All Science Journal Classification (ASJC) codes
- Statistics and Probability