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. van Beest
  • Jacob Nabe-Nielsen
  • Juan M. Morales

DOI: 10.1007/s13253-017-0282-9

Cite this article as:
Leos-Barajas, V., Gangloff, E.J., Adam, T. et al. JABES (2017). doi:10.1007/s13253-017-0282-9


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.


Animal behavior Bio-logging Experimental design Latent process State-switching model Temporal resolution 

Supplementary material

13253_2017_282_MOESM1_ESM.rdata (144 kb)
Supplementary material 1 (RData 144 KB)
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Supplementary material 2 (RData 8308 KB)
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Supplementary material 3 (csv 253 KB)
13253_2017_282_MOESM4_ESM.cpp (1 kb)
Supplementary material 4 (cpp 0 KB)
13253_2017_282_MOESM5_ESM.r (16 kb)
Supplementary material 5 (R 16 KB)
13253_2017_282_MOESM6_ESM.csv (306 kb)
Supplementary material 6 (csv 305 KB)

Funding information

Funder NameGrant NumberFunding Note
DONG Energy
Iowa Science Foundation
    East Anglia Offshore Wind
      ENECO Luctherduinen
          SMart Wind
            American Society of Icthyologists and Herpetologists
              Office of Biotechnology, Iowa State University
              National Science Foundation
              • IOS 0922528

              Copyright information

              © International Biometric Society 2017

              Authors and Affiliations

              1. 1.Iowa State UniversityAmesUSA
              2. 2.Bielefeld UniversityBielefeldGermany
              3. 3.Aarhus UniversityRoskildeDenmark
              4. 4.INIBIOMA-CONICETBarilocheArgentina

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