Multi-scale Modeling of Animal Movement and General Behavior Data Using Hidden Markov Models with Hierarchical Structures

Vianey Leos-Barajas (), Eric J. Gangloff, Timo Adam, Roland Langrock, Floris M. Beest, Jacob Nabe-Nielsen and Juan M. Morales
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Vianey Leos-Barajas: Iowa State University
Eric J. Gangloff: Iowa State University
Timo Adam: Bielefeld University
Roland Langrock: Bielefeld University
Floris M. Beest: Aarhus University
Jacob Nabe-Nielsen: Aarhus University
Juan M. Morales: INIBIOMA-CONICET

Journal of Agricultural, Biological and Environmental Statistics, 2017, vol. 22, issue 3, No 3, 232-248

Abstract: Abstract 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. We illustrate the application of hierarchically structured HMMs in two real-data examples: (i) vertical movements of harbor porpoises observed in the field, and (ii) garter snake movement data collected as part of an experimental design. Supplementary materials accompanying this paper appear online.

Keywords: Animal behavior; Bio-logging; Experimental design; Latent process; State-switching model; Temporal resolution (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s13253-017-0282-9

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