title: Classification of Animal Movement Behavior through Residence in Space and Time.
abstract: Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are time-intensive (e.g., rest), time & distance-intensive (e.g., area restricted search), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST's ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST's ability to discriminate between behavior states relative to other classical movement metrics. We then temporally sub-sample albatross track data to illustrate RST's response to less resolved data. Finally, we evaluate RST's performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.
authors.affiliation: Department of Fisheries and Wildlife, Marine Mammal Institute, Oregon State University, Hatfield Marine Science Center, Newport, Oregon, United States of America.
authors.forename: Leigh G
authors.identifier: http://orcid.org/0000-0002-2643-3950
authors.initials: LG
authors.lastname: Torres
authors.affiliation: Department of Fisheries and Wildlife, Marine Mammal Institute, Oregon State University, Hatfield Marine Science Center, Newport, Oregon, United States of America.
authors.forename: Rachael A
authors.initials: RA
authors.lastname: Orben
authors.affiliation: Applied Math and Computer Science Departments, University of Washington, Seattle, Washington, United States of America.
authors.forename: Irina
authors.initials: I
authors.lastname: Tolkova
authors.affiliation: National Institute of Water and Atmospheric Research Ltd., Hataitai, Wellington, New Zealand.
authors.forename: David R
authors.initials: DR
authors.lastname: Thompson
mesh_terms: D000818:Animals; D001522:Behavior, Animal; D001717:Birds / Q000502:physiology*; D017753:Ecosystem; D040362:Geographic Information Systems; D008124:Locomotion; D009520:New Zealand; D062211:Spatio-Temporal Analysis; D013997:Time Factors