title: Flexible characterization of animal movement pattern using net squared displacement and a latent state model.
abstract: BACKGROUND
  Characterizing the movement patterns of animals is an important step in understanding their ecology. Various methods have been developed for classifying animal movement at both coarse (e.g., migratory vs. sedentary behavior) and fine (e.g., resting vs. foraging) scales. A popular approach for classifying movements at coarse resolutions involves fitting time series of net-squared displacement (NSD) to models representing different conceptualizations of coarse movement strategies (i.e., migration, nomadism, sedentarism, etc.). However, the performance of this method in classifying actual (as opposed to simulated) animal movements has been mixed. Here, we develop a more flexible method that uses the same NSD input, but relies on an underlying discrete latent state model. Using simulated data, we first assess how well patterns in the number of transitions between modes of movement and the duration of time spent in a mode classify movement strategies. We then apply our approach to elucidate variability in the movement strategies of eight giant tortoises (Chelonoidis sp.) using a multi-year (2009-2014) GPS dataset from three different Galapagos Islands.
  
  
  RESULTS
  With respect to patterns of time spent and the number of transitions between modes, our approach out-performed previous efforts to distinguish among migration, dispersal, and sedentary behavior. We documented marked inter-individual variation in giant tortoise movement strategies, with behaviors indicating migration, dispersal, nomadism and sedentarism, as well as hybrid behaviors such as "exploratory residence".
  
  
  CONCLUSIONS
  Distilling complex animal movement into discrete modes remains a fundamental challenge in movement ecology, a problem made more complex by the ever-longer duration, ever-finer resolution, and gap-ridden trajectories recorded by GPS devices. By clustering into modes, we derived information on the time spent within one mode and the number of transitions between modes which enabled finer differentiation of movement strategies over previous methods. Ultimately, the techniques developed here address limitations of previous approaches and provide greater insights with respect to characterization of movement strategies across scales by more fully utilizing long-term GPS telemetry datasets.
authors.affiliation: Department of Environmental and Forest Biology, State University of New York, College of Environmental Science and Forestry, Syracuse, 13210 NY USA ; Roosevelt Wild Life Station, State University of New York, College of Environmental Science and Forestry, Syracuse, 13210 NY USA.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Bastille-Rousseau
authors.affiliation: School of Mathematics and Statistics, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield, S3 7RH UK.
authors.forename: Jonathan R
authors.initials: JR
authors.lastname: Potts
authors.affiliation: U.S. Geological Survey, Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, Flagstaff, 86001 AZ USA.
authors.forename: Charles B
authors.initials: CB
authors.lastname: Yackulic
authors.affiliation: Department of Environmental and Forest Biology, State University of New York, College of Environmental Science and Forestry, Syracuse, 13210 NY USA ; Roosevelt Wild Life Station, State University of New York, College of Environmental Science and Forestry, Syracuse, 13210 NY USA.
authors.forename: Jacqueline L
authors.initials: JL
authors.lastname: Frair
authors.affiliation: School of Environment and Natural Resources, Ohio State University, Columbus, 43210 OH USA.
authors.forename: E Hance
authors.initials: EH
authors.lastname: Ellington
authors.affiliation: Department of Environmental and Forest Biology, State University of New York, College of Environmental Science and Forestry, Syracuse, 13210 NY USA ; Saint Louis Zoo, WildCare Institute, 1 Government Drive, St. Louis, 63110 MO USA ; Max Planck Institute for Ornithology, Radolfzell, Germany ; Whitney Harris World Ecology Center, University of Missouri-St. Louis, St. Louis, 63121 MO USA ; Department of Biology, Washington University in St. Louis, St. Louis, 63130 MO USA ; Charles Darwin Foundation, Puerto Ayora, Galapagos Ecuador.
authors.forename: Stephen
authors.initials: S
authors.lastname: Blake
keywords: Bayesian clustering; Discrete latent state; Giant Galapagos tortoises (Chelonoidis sp.); Migration; Mixture model; Resident