title: What is the animal doing? Tools for exploring behavioural structure in animal movements.
abstract: Movement data provide a window - often our only window - into the cognitive, social and biological processes that underlie the behavioural ecology of animals in the wild. Robust methods for identifying and interpreting distinct modes of movement behaviour are of great importance, but complicated by the fact that movement data are complex, multivariate and dependent. Many different approaches to exploratory analysis of movement have been developed to answer similar questions, and practitioners are often at a loss for how to choose an appropriate tool for a specific question. We apply and compare four methodological approaches: first passage time (FPT), Bayesian partitioning of Markov models (BPMM), behavioural change point analysis (BCPA) and a fitted multistate random walk (MRW) to three simulated tracks and two animal trajectories - a sea lamprey (Petromyzon marinus) tracked for 12 h and a wolf (Canis lupus) tracked for 1 year. The simulations - in which, respectively, velocity, tortuosity and spatial bias change - highlight the sensitivity of all methods to model misspecification. Methods that do not account for autocorrelation in the movement variables lead to spurious change points, while methods that do not account for spatial bias completely miss changes in orientation. When applied to the animal data, the methods broadly agree on the structure of the movement behaviours. Important discrepancies, however, reflect differences in the assumptions and nature of the outputs. Important trade-offs are between the strength of the a priori assumptions (low in BCPA, high in MRW), complexity of output (high in the BCPA, low in the BPMM and MRW) and explanatory potential (highest in the MRW). The animal track analysis suggests some general principles for the exploratory analysis of movement data, including ways to exploit the strengths of the various methods. We argue for close and detailed exploratory analysis of movement before fitting complex movement models.
authors.affiliation: Department of Biology, University of Maryland, College Park, MD, 20742, USA.|School of Environmental and Forest Sciences, University of Washington, Seattle, WA, 98195, USA.
authors.forename: Eliezer
authors.initials: E
authors.lastname: Gurarie
authors.affiliation: Quantitative Ecology and Resource Management, University of Washington, Seattle, WA, 98195, USA.
authors.forename: Chloe
authors.initials: C
authors.lastname: Bracis
authors.affiliation: Department of Biosciences, University of Helsinki, 00014, Helsinki, Finland.|Research Unit of Biodiversity (UMIB, UO-CSIC-PA), Oviedo University - Campus Mieres, 33600, Mieres, Spain.
authors.forename: Maria
authors.initials: M
authors.lastname: Delgado
authors.affiliation: Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, 48824, USA.
authors.forename: Trevor D
authors.initials: TD
authors.lastname: Meckley
authors.affiliation: Natural Resources Institute, Box 16, FI-96301, Rovaniemi, Finland.
authors.forename: Ilpo
authors.initials: I
authors.lastname: Kojola
authors.affiliation: Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, 48824, USA.
authors.forename: C Michael
authors.initials: CM
authors.lastname: Wagner
mesh_terms: D063147:Animal Distribution; D000818:Animals; D004463:Ecology / Q000379:methods*; D005010:Ethology / Q000379:methods*; D006702:Homing Behavior; D008954:Models, Biological; D009068:Movement; D048428:Petromyzon / Q000502:physiology*; D019596:Wolves / Q000502:physiology*
keywords: behavioural change points; hidden Markov models; partitioning; segmentation; state space models; telemetry