A hidden Markov movement model for rapidly identifying behavioral states from animal tracks. Issue 7 (28th February 2017)

• Record Type: Journal Article
• Authors: Whoriskey, Kim; Auger‐Méthé, Marie; Albertsen, Christoffer M.; Whoriskey, Frederick G.; Binder, Thomas R.; Krueger, Charles C.; Mills Flemming, Joanna
• Is Part Of: Ecology and evolution. Volume 7:Issue 7(2017:Apr.)
• Page Start: 2112
• Page End: 2121
• Publication Date: 2017-02-28
• Subjects: behavioral states -- Great Lakes Acoustic Telemetry Observation System -- movement ecology -- Ocean Tracking Network -- TMB -- swim; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.2795
• Languages: English
• ISSNs: 2045-7758

Abstract

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 packageTMB for rapid model fitting. The HMMM was compared to a modified version of the DCRWS for highly accurate tracks, the DCRWS NOME, and to a common HMM for animal tracks fitted with the R packagemoveHMM . 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 withTMB . To facilitate its uptake, we make it available through the R packageswim . Abstract : We used the R package TMB to develop a new hidden Markov model (HMM) for identifying behavioral states from animal tracks with negligible error, which we call the hidden Markov movement model (HMMM). We show that the HMMM can make meaningful inference from animal movement data collected on multiple species. It additionally provides a groundwork for development of more complex movement modelling with TMB. To facilitate its uptake, we make the HMMM available through the R package swim.