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Classification of Animal Movement Behavior through Residence in Space and Time

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 t...

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Autores principales: Torres, Leigh G., Orben, Rachael A., Tolkova, Irina, Thompson, David R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5207689/
https://www.ncbi.nlm.nih.gov/pubmed/28045906
http://dx.doi.org/10.1371/journal.pone.0168513
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author Torres, Leigh G.
Orben, Rachael A.
Tolkova, Irina
Thompson, David R.
author_facet Torres, Leigh G.
Orben, Rachael A.
Tolkova, Irina
Thompson, David R.
author_sort Torres, Leigh G.
collection PubMed
description 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.
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spelling pubmed-52076892017-01-19 Classification of Animal Movement Behavior through Residence in Space and Time Torres, Leigh G. Orben, Rachael A. Tolkova, Irina Thompson, David R. PLoS One Research Article 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. Public Library of Science 2017-01-03 /pmc/articles/PMC5207689/ /pubmed/28045906 http://dx.doi.org/10.1371/journal.pone.0168513 Text en © 2017 Torres et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Torres, Leigh G.
Orben, Rachael A.
Tolkova, Irina
Thompson, David R.
Classification of Animal Movement Behavior through Residence in Space and Time
title Classification of Animal Movement Behavior through Residence in Space and Time
title_full Classification of Animal Movement Behavior through Residence in Space and Time
title_fullStr Classification of Animal Movement Behavior through Residence in Space and Time
title_full_unstemmed Classification of Animal Movement Behavior through Residence in Space and Time
title_short Classification of Animal Movement Behavior through Residence in Space and Time
title_sort classification of animal movement behavior through residence in space and time
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5207689/
https://www.ncbi.nlm.nih.gov/pubmed/28045906
http://dx.doi.org/10.1371/journal.pone.0168513
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