Modeling animal movement with directional persistence and attractive points
share
GPS technology is more accessible to researchers and, nowadays, animal movement data are widely available. To analyze such data, different approaches have been proposed, and among them, hidden Markov models with the Ornstein-Uhlenbeck or the step-and-turn emission distribution are the most commonly used. The former characterizes movement with the use of a center of attraction, while the latter has directional persistence. In this work we propose a new emission distribution that posses the defining characteristics of the two aforementioned approaches, and at any given time, an animal can exhibit a different degree of directional persistence and attraction to a point in space. Hidden Markov models based on our proposal, the Ornstein-Uhlenbeck, and the step-and-turn, are estimated on a real data example, where GPS locations of a Maremma Sheepdog are recorded.We show that our proposal has the richest output and better describes the data.
READ FULL TEXT