A multi-scale modeling framework for the feedback between movement and body condition of Merino sheep

VIANEY, LEOS BARAJAS; MORALES, JUAN MANUEL; MARK, KAISER; DI VIRGILIO, AGUSTINA

St. Andrews

Conferencia; International Statistical Ecology Conference; 2018

University of St. Andrews

A long-sought goal is to connect movement with population dynamics. For many species, and especially for ungulates, there is a known link between condition (e.g. fat reserves) and the probability of survival and reproduction. Assuming a particular genetic makeup and physiology, condition reflects the history of behavioral decisions, including movement and habitat use. However, the condition of an animal can also have a direct implication on the types of movements that it performs and the habitats that it visits. An animal in good condition can move in a manner that is biologically optimal while an animal in poor condition may not be able to afford migration or may have to venture into areas of higher predation risk in order to forage. We aim to explicitly model the connections between condition, movement and space use. We used GPS as a measure of behavioral-based movement data, with behaviors inferred via the use of a hidden Markov model. For the condition process, we used the unobserved levels of body fat (as an indicator of body condition) that were inferred via a state-space model. In order to account for the interaction between the two latent processes, we present a single modeling framework to account for the latent feedback structure between movement and condition using Merino sheep as a case study. The model also accounts for the difference in temporal resolution between movement and condition data, as the former is typically collected at a much finer temporal scale than the latter. We perform all inference using a Bayesian framework.