Hidden Markov movement models reveal diverse seasonal movement patterns in two North American ungulates.

ABSTRACT: Animal movement is the mechanism connecting landscapes to fitness, and understanding variation in seasonal animal movements has benefited from the analysis and categorization of animal displacement. However, seasonal movement patterns can defy classification when movements are highly variable. Hidden Markov movement models (HMMs) are a class of latent-state models well-suited to modeling movement data. Here, we used HMMs to assess seasonal patterns of variation in the movement of pronghorn (Antilocapra americana), a species known for variable seasonal movements that challenge analytical approaches, while using a population of mule deer (Odocoileus hemionus), for whom seasonal movements are well-documented, as a comparison. We used population-level HMMs in a Bayesian framework

SUBMITTER: Paterson JT 

PROVIDER: S-EPMC10361361 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature