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

When using this dataset, please cite the original article.

Torres LG, Orben RA, Tolkova I, Thompson DR (2017) Classification of animal movement behavior through residence in space and time. PLoS ONE 12(1): e0168513. doi:10.1371/journal.pone.0168513

Additionally, please cite the Movebank data package:

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. doi:10.5441/001/1.694p666h
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Package Identifier doi: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 animal behavior, animal movement, animal tracking, area restricted search, behavior classification, GPS logger, grey-headed albatross, Movebank, movement ecology, residence time, Thalassarche chrysostoma, track segmentation,

Grey-headed albatross, New Zealand (data from Torres et al. 2017) View File Details
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Download: Grey-headed albatross, New Zealand (data from Torres et al. 2017).csv ( 16.78Mb )
To the extent possible under law, the authors have waived all copyright and related or neighboring rights to this data.  



Grey-headed albatross, New Zealand (data from Torres et al. 2017)-reference-data View File Details
Download: README.txt ( 9.503Kb )
Download: Grey-headed albatross, New Zealand (data from Torres et al. 2017)-reference-data.csv ( 4.183Kb )
To the extent possible under law, the authors have waived all copyright and related or neighboring rights to this data.  


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