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Activity Number: 384 - Advances in Animal Movement Modeling
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: JABES-Journal of Agricultural, Biological, and Environmental Statistics
Abstract #326495 Presentation
Title: Multi-Scale Modeling of Animal Movement and General Behavior Data Using Hidden Markov Models with Hierarchical Structures
Author(s): Vianey Leos Barajas* and Eric Gangloff and Timo Adam and Roland Langrock and Juan Morales
Companies: Iowa State University and Station d'Ecologie Théorique et Expérimentale du CNRS and Bielefeld University and Bielefeld University and INIBIOMA-CRUB CONICET
Keywords: animal behavior; bio-logging; state-switching model; temporal resolution

Hidden Markov models (HMMs) are commonly used to model animal movement data and infer aspects of animal behavior. An HMM assumes that each data point from a time series of observations stems from one of N possible states. The states are loosely connected to behavioral modes that manifest themselves at the temporal resolution at which observations are made. Due to advances in tag technology and tracking with digital video recordings, data can be collected at increasingly fine temporal resolutions. Yet, inferences at time scales cruder than those at which data are collected and, which correspond to larger-scale behavioral processes, are not yet answered via HMMs. We include additional hierarchical structures to the basic HMM framework, incorporating multiple Markov chains at various time scales. The hierarchically structured HMMs allow for behavioral inferences at multiple time scales and can also serve as a means to avoid coarsening data. Our proposed framework is one of the first that models animal behavior simultaneously at multiple time scales, opening new possibilities in the area of animal movement and behavior modeling.

Authors who are presenting talks have a * after their name.

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