title: A hidden Markov movement model for rapidly identifying behavioral states from animal tracks.
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 DCRWSNOME, and to a common HMM for animal tracks fitted with the R package moveHMM. We show that the HMMM is both accurate and suitable for multiple species by fitting it to real tracks from a grey seal, lake trout, and blue shark, as well as to simulated data. The HMMM is a fast and reliable tool for making meaningful inference from animal movement data that is ideally suited for ecologists who want to use the popular DCRWS implementation and have highly accurate tracking data. It additionally provides a groundwork for development of more complex modeling of animal movement with TMB. To facilitate its uptake, we make it available through the R package swim.
authors.affiliation: Department of Mathematics and Statistics Dalhousie University Halifax NS Canada.
authors.forename: Kim
authors.identifier: 0000-0001-7520-1016
authors.initials: K
authors.lastname: Whoriskey
authors.affiliation: Department of Mathematics and Statistics Dalhousie University Halifax NS Canada.
authors.forename: Marie
authors.identifier: 0000-0003-3550-4930
authors.initials: M
authors.lastname: Auger-Méthé
authors.affiliation: National Institute of Aquatic Resources Technical University of Denmark Charlottenlund Denmark.
authors.forename: Christoffer M
authors.initials: CM
authors.lastname: Albertsen
authors.affiliation: Ocean Tracking Network Dalhousie University Halifax NS Canada.
authors.forename: Frederick G
authors.initials: FG
authors.lastname: Whoriskey
authors.affiliation: Hammond Bay Biological Station Department of Fisheries and Wildlife Michigan State University Millersburg MI USA.
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Binder
authors.affiliation: Center for Systems Integration and Sustainability Michigan State University East Lansing MI USA.
authors.forename: Charles C
authors.initials: CC
authors.lastname: Krueger
authors.affiliation: Department of Mathematics and Statistics Dalhousie University Halifax NS Canada.
authors.forename: Joanna
authors.initials: J
authors.lastname: Mills Flemming
keywords: Great Lakes Acoustic Telemetry Observation System; Ocean Tracking Network; TMB; behavioral states; movement ecology; swim