Name: Vianey Leos
Institution: Iowa State University, USA
E-mail: vianey@iastate.edu
Co-authors: Agustina di Virgilio, Mark Steven Kaiser, Juan Manuel Morales
Abstract:
A long-sought goal in ecology 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. Movement data for ungulates are typically collected at a fine temporal scale, e.g. a position recorded by a GPS device every five or ten minutes. However, fat reserves cannot be measured remotely and must be done manually. This in turn creates a mismatch in the temporal scale at which the two data streams are observed, i.e. every five minutes for movement vs approximately once a month for condition. Further, the temporal mismatch leads to various challenges when jointly modeling the two processes.
For the movement model, we use discrete-time, finite-state hidden Markov models (HMMs) with the positional data of the sheep serving as the observation process and the underlying state process serving as a proxy for behaviors of interest. To incorporate condition as a potential covariate affecting the movement, and thus behavioral, process, we make use of the deterministic functions that describe the evolution of body fat in Merino sheep in order to predict daily values of the condition process. This deterministic process is formulated as a function of the states inferred by HMM, as well as the distance that the sheep travels. We aim to provide a general modeling framework the describes the interaction between condition and movement, using Merino sheep as a case study, that further accounts for the mismatch between temporal scales of the two processes of interest.