Animal movement analysis through residence in space and time
- Published
- Accepted
- Subject Areas
- Animal Behavior, Biogeography, Ecology
- Keywords
- albatross, area restricted search, behavior, residence time, distance, tracking, tags, scale, behavioral change points, movement data
- Copyright
- © 2016 Torres et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Animal movement analysis through residence in space and time. PeerJ Preprints 4:e2480v1 https://doi.org/10.7287/peerj.preprints.2480v1
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 distance-intensive (e.g., area restricted search), time-intensive (e.g., rest), 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 sub-sample albatross track data to illustrate RST’s response to less temporally 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.
Author Comment
This manuscript is currently in review with Plos One. The R code is being developed into a package that will be available soon, but for now the code (and data and annotation) is available in the supplementary zip file supplied here.