Investigators: Dr. Leigh Torres, Dr. Rachael Orben, Irina Tolkova, Dr. David Thompson

Large animal movement datasets are increasingly common (see https://www.movebank.org/ or http://www.seabirdtracking.org/) and there is a need for efficient methods of data exploration that adjust to the individual variability of each track.

Identification and classification of behavior states in animal movement data can be complex, yet there are simple behavior states that are shared between individuals and taxa.

In collaboration with Irina Tolkova (NSF REU Intern) we developed a simple method of behavior classification based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. This Residence in Space and Time, or RST method 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).

More information about this project:
For a more detailed explanation of RST see this blog post.

Or read the paper:

Torres, L. G., Orben, R. A., Tolkova, I., & Thompson, D. R. (2017). Classification of Animal Movement Behavior through Residence in Space and Time, 12(1), e0168513–18. http://doi.org/10.1371/journal.pone.0168513

#WSTC3 Classification of seabird movement behavior through residence in space and time.

Related publications:

M. B. Ogburn, Harrison, A. - L., Whoriskey, F. G., Cooke, S. J., Flemming, J. E., and Torres, L. G., “Addressing Challenges in the Application of Animal Movement Ecology to Aquatic Conservation and Management”, Frontiers in Marine Science, vol. 4, 2017.

B. G. Lascelles, Taylor, P. R., Miller, M. G. R., Dias, M. P., Oppel, S., Torres, L. G., Hedd, A., Le Corre, M., Phillips, R. A., Shaffer, S. A., Weimerskirch, H., and Small, C., “Applying global criteria to tracking data to define important areas for marine conservation”, Diversity and Distributions, vol. 22, no. 4, pp. 422 - 431, 2016.

https://storify.com/RachaelOrben/residence-in-space-and-time-rst

Large animal movement datasets are increasingly common (see https://www.movebank.org/ or http://www.seabirdtracking.org/) and there is a need for efficient methods of data exploration that adjust to the individual variability of each track.