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- Title
A hidden Markov movement model for rapidly identifying behavioral states from animal tracks.
- Authors
Whoriskey, Kim; Auger ‐ Méthé, Marie; Albertsen, Christoffer M.; Whoriskey, Frederick G.; Binder, Thomas R.; Krueger, Charles C.; Mills Flemming, Joanna
- Abstract
Electronic telemetry is frequently used to document animal movement through time. Methods that can identify underlying behaviors driving specific movement patterns can help us understand how and why animals use available space, thereby aiding conservation and management efforts. For aquatic animal tracking data with significant measurement error, a Bayesian state-space model called the first-Difference Correlated Random Walk with Switching (DCRWS) has often been used for this purpose. However, for aquatic animals, highly accurate tracking data are now becoming more common. We developed a new hidden Markov model (HMM) for identifying behavioral states from animal tracks with negligible error, called the hidden Markov movement model (HMMM). We implemented as the basis for the HMMM the process equation of the DCRWS, but we used the method of maximum likelihood and the R package TMB for rapid model fitting. The HMMM was compared to a modified version of the DCRWS for highly accurate tracks, the DCRWS
- Subjects
ANIMAL mechanics; ANIMAL behavior; ANIMAL radio tracking; ANIMAL ecology; HIDDEN Markov models; STATE-space methods
- Publication
Ecology & Evolution (20457758), 2017, Vol 7, Issue 7, p2112
- ISSN
2045-7758
- Publication type
Article
- DOI
10.1002/ece3.2795