Animal movement models for multiple individuals. (9th March 2020)

• Record Type: Journal Article
• Authors: Scharf, Henry R.; Buderman, Frances E.
• Is Part Of: Wiley interdisciplinary reviews. Volume 12:Number 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-09
• Subjects: animal movement -- behavior -- hierarchical -- population inference -- social network; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1506
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract: 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 Abstract : Simulated trajectories from a process convolution model for movement conditioned on a latent social network. The inset in the bottom right shows the dynamic pairwise relationships among the three individuals. A time interval during which individuals 1 and 3 are strongly connected is highlighted with a gray box. On the trajectories, black arrows oriented in the direction of movement, with arrowhead length proportional to the connection strength, highlight the impact of this particular connection on movement. At the onset of the strong connection period, the indicated paths orient toward each other as individuals demonstrate attractive behavior. As the connection strength weakens, the individuals resume independent movement.