Data from: Classification of animal movement behavior through residence in space and time

Citation
Thompson DR, Torres LG, Sagar PM, Kroeger CE, Orben RA. 2017. Data from: Classification of animal movement behavior through residence in space and time. Movebank Data Repository. https://doi.org/10.5441/001/1.694p666h
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.
Keywords
Thalassarche chrysostoma,animal movement,animal tracking,area restricted search,behavior classification,GPS logger,grey-headed albatross,Movebank,movement ecology,residence time,Thalassarche chrysostoma,track segmentation
Taxa
Taxon
Thalassarche chrysostoma
Grey-headed Albatross
Sensors
Sensor
GPS
Related Workflows
BibTex
@misc{001/1_694p666h,
  title = {Data from: Classification of animal movement behavior through residence in space and time},
  author = {Thompson, DR and Torres, LG and Sagar, PM and Kroeger, CE and Orben, RA},
  year = {2017},
  URL = {http://dx.doi.org/10.5441/001/1.694p666h},
  doi = {doi:10.5441/001/1.694p666h},
  publisher = {Movebank data repository}
}
RIS
TY  - DATA
ID  - doi:10.5441/001/1.694p666h
T1  - Data from: Classification of animal movement behavior through residence in space and time
AU  - Thompson, David R.
AU  - Torres, Leigh G.
AU  - Sagar, Paul M.
AU  - Kroeger, Caitlin E.
AU  - Orben, Rachael A.
Y1  - 2017/01/27
KW  - animal behavior
KW  - animal movement
KW  - animal tracking
KW  - area restricted search
KW  - behavior classification
KW  - GPS logger
KW  - grey-headed albatross
KW  - Movebank
KW  - movement ecology
KW  - residence time
KW  - Thalassarche chrysostoma
KW  - track segmentation
KW  - Thalassarche chrysostoma
KW  - Thalassarche chrysostoma
PB  - Movebank data repository
UR  - http://dx.doi.org/10.5441/001/1.694p666h
DO  - doi:10.5441/001/1.694p666h
ER  - 
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