A comparison of techniques for classifying behavior from accelerometers for two species of seabird
The behavior of many wild animals remains a mystery, as it is difficult to quantify behavior of species that cannot be easily followed throughout their daily or seasonal movements. Accelerometers can solve some of these mysteries, as they collect activity data at a high temporal resolution (<1 s)...
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ftpubmed:oai:pubmedcentral.nih.gov:6434605 2023-05-15T18:07:11+02:00 A comparison of techniques for classifying behavior from accelerometers for two species of seabird Patterson, Allison Gilchrist, Hugh Grant Chivers, Lorraine Hatch, Scott Elliott, Kyle 2019-02-21 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6434605/ http://www.ncbi.nlm.nih.gov/pubmed/30962879 https://doi.org/10.1002/ece3.4740 en eng John Wiley and Sons Inc. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6434605/ http://www.ncbi.nlm.nih.gov/pubmed/30962879 http://dx.doi.org/10.1002/ece3.4740 © 2018 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. CC-BY Original Research Text 2019 ftpubmed https://doi.org/10.1002/ece3.4740 2019-04-14T00:14:59Z The behavior of many wild animals remains a mystery, as it is difficult to quantify behavior of species that cannot be easily followed throughout their daily or seasonal movements. Accelerometers can solve some of these mysteries, as they collect activity data at a high temporal resolution (<1 s), can be relatively small (<1 g) so they minimally disrupt behavior, and are increasingly capable of recording data for long periods. Nonetheless, there is a need for increased validation of methods to classify animal behavior from accelerometers to promote widespread adoption of this technology in ecology. We assessed the accuracy of six different behavioral assignment methods for two species of seabird, thick‐billed murres (Uria lomvia) and black‐legged kittiwakes (Rissa tridactyla). We identified three behaviors using tri‐axial accelerometers: standing, swimming, and flying, after classifying diving using a pressure sensor for murres. We evaluated six classification methods relative to independent classifications from concurrent GPS tracking data. We used four variables for classification: depth, wing beat frequency, pitch, and dynamic acceleration. Average accuracy for all methods was >98% for murres, and 89% and 93% for kittiwakes during incubation and chick rearing, respectively. Variable selection showed that classification accuracy did not improve with more than two (kittiwakes) or three (murres) variables. We conclude that simple methods of behavioral classification can be as accurate for classifying basic behaviors as more complex approaches, and that identifying suitable accelerometer metrics is more important than using a particular classification method when the objective is to develop a daily activity or energy budget. Highly accurate daily activity budgets can be generated from accelerometer data using multiple methods and a small number of accelerometer metrics; therefore, identifying a suitable behavioral classification method should not be a barrier to using accelerometers in studies of seabird ... Text rissa tridactyla Uria lomvia uria PubMed Central (PMC) Ecology and Evolution 9 6 3030 3045 |
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Original Research Patterson, Allison Gilchrist, Hugh Grant Chivers, Lorraine Hatch, Scott Elliott, Kyle A comparison of techniques for classifying behavior from accelerometers for two species of seabird |
topic_facet |
Original Research |
description |
The behavior of many wild animals remains a mystery, as it is difficult to quantify behavior of species that cannot be easily followed throughout their daily or seasonal movements. Accelerometers can solve some of these mysteries, as they collect activity data at a high temporal resolution (<1 s), can be relatively small (<1 g) so they minimally disrupt behavior, and are increasingly capable of recording data for long periods. Nonetheless, there is a need for increased validation of methods to classify animal behavior from accelerometers to promote widespread adoption of this technology in ecology. We assessed the accuracy of six different behavioral assignment methods for two species of seabird, thick‐billed murres (Uria lomvia) and black‐legged kittiwakes (Rissa tridactyla). We identified three behaviors using tri‐axial accelerometers: standing, swimming, and flying, after classifying diving using a pressure sensor for murres. We evaluated six classification methods relative to independent classifications from concurrent GPS tracking data. We used four variables for classification: depth, wing beat frequency, pitch, and dynamic acceleration. Average accuracy for all methods was >98% for murres, and 89% and 93% for kittiwakes during incubation and chick rearing, respectively. Variable selection showed that classification accuracy did not improve with more than two (kittiwakes) or three (murres) variables. We conclude that simple methods of behavioral classification can be as accurate for classifying basic behaviors as more complex approaches, and that identifying suitable accelerometer metrics is more important than using a particular classification method when the objective is to develop a daily activity or energy budget. Highly accurate daily activity budgets can be generated from accelerometer data using multiple methods and a small number of accelerometer metrics; therefore, identifying a suitable behavioral classification method should not be a barrier to using accelerometers in studies of seabird ... |
format |
Text |
author |
Patterson, Allison Gilchrist, Hugh Grant Chivers, Lorraine Hatch, Scott Elliott, Kyle |
author_facet |
Patterson, Allison Gilchrist, Hugh Grant Chivers, Lorraine Hatch, Scott Elliott, Kyle |
author_sort |
Patterson, Allison |
title |
A comparison of techniques for classifying behavior from accelerometers for two species of seabird |
title_short |
A comparison of techniques for classifying behavior from accelerometers for two species of seabird |
title_full |
A comparison of techniques for classifying behavior from accelerometers for two species of seabird |
title_fullStr |
A comparison of techniques for classifying behavior from accelerometers for two species of seabird |
title_full_unstemmed |
A comparison of techniques for classifying behavior from accelerometers for two species of seabird |
title_sort |
comparison of techniques for classifying behavior from accelerometers for two species of seabird |
publisher |
John Wiley and Sons Inc. |
publishDate |
2019 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6434605/ http://www.ncbi.nlm.nih.gov/pubmed/30962879 https://doi.org/10.1002/ece3.4740 |
genre |
rissa tridactyla Uria lomvia uria |
genre_facet |
rissa tridactyla Uria lomvia uria |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6434605/ http://www.ncbi.nlm.nih.gov/pubmed/30962879 http://dx.doi.org/10.1002/ece3.4740 |
op_rights |
© 2018 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1002/ece3.4740 |
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Ecology and Evolution |
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9 |
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6 |
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3030 |
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3045 |
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