MODELING ANIMAL MOVEMENT WITH DIRECTIONAL PERSISTENCE AND ATTRACTIVE POINTS

• 成果类型: Article
• 署名作者: Mastrantonio, Gianluca
• 署名单位: Polytechnic University of Turin
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1584
• 发表日期: 2022
• 页码: 2030-2053
• 关键词: hidden markov-models; livestock guardian dogs; resource selection; space models; discrete; mixtures; formulation; predators; inference; BEHAVIOR

摘要

GPS technology is currently easily accessible to researchers, and many animal movement data sets are available. Two of the main features that a model which describes an animal's path can possess are directional persistence and attraction to a point in space. In this work, we propose a new approach that can have both characteristics. Our proposal is a hidden Markov model with a new emission distribution. The emission distribution models the two aforementioned characteristics, while the latent state of the hidden Markov model is needed to account for the behavioral modes. We show that the model is easy to implement in a Bayesian framework. We estimate our proposal on the motivating data that represent GPS locations of a Maremma Sheepdog recorded in Australia. The obtained results are easily interpretable and we show that our proposal outperforms the main competitive model.