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Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal
The miniaturization and affordability of new technology is driving a biologging revolution in wildlife ecology with use of animal‐borne data logging devices. Among many new biologging technologies, accelerometers are emerging as key tools for continuously recording animal behavior. Yet a critical, b...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6342100/ https://www.ncbi.nlm.nih.gov/pubmed/30680142 http://dx.doi.org/10.1002/ece3.4786 |
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author | Studd, Emily K. Landry‐Cuerrier, Manuelle Menzies, Allyson K. Boutin, Stan McAdam, Andrew G. Lane, Jeffrey E. Humphries, Murray M. |
author_facet | Studd, Emily K. Landry‐Cuerrier, Manuelle Menzies, Allyson K. Boutin, Stan McAdam, Andrew G. Lane, Jeffrey E. Humphries, Murray M. |
author_sort | Studd, Emily K. |
collection | PubMed |
description | The miniaturization and affordability of new technology is driving a biologging revolution in wildlife ecology with use of animal‐borne data logging devices. Among many new biologging technologies, accelerometers are emerging as key tools for continuously recording animal behavior. Yet a critical, but under‐acknowledged consideration in biologging is the trade‐off between sampling rate and sampling duration, created by battery‐ (or memory‐) related sampling constraints. This is especially acute among small animals, causing most researchers to sample at high rates for very limited durations. Here, we show that high accuracy in behavioral classification is achievable when pairing low‐frequency acceleration recordings with temperature. We conducted 84 hr of direct behavioral observations on 67 free‐ranging red squirrels (200–300 g) that were fitted with accelerometers (2 g) recording tri‐axial acceleration and temperature at 1 Hz. We then used a random forest algorithm and a manually created decision tree, with variable sampling window lengths, to associate observed behavior with logger recorded acceleration and temperature. Finally, we assessed the accuracy of these different classifications using an additional 60 hr of behavioral observations, not used in the initial classification. The accuracy of the manually created decision tree classification using observational data varied from 70.6% to 91.6% depending on the complexity of the tree, with increasing accuracy as complexity decreased. Short duration behavior like running had lower accuracy than long‐duration behavior like feeding. The random forest algorithm offered similarly high overall accuracy, but the manual decision tree afforded the flexibility to create a hierarchical tree, and to adjust sampling window length for behavioral states with varying durations. Low frequency biologging of acceleration and temperature allows accurate behavioral classification of small animals over multi‐month sampling durations. Nevertheless, low sampling rates impose several important limitations, especially related to assessing the classification accuracy of short duration behavior. |
format | Online Article Text |
id | pubmed-6342100 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63421002019-01-24 Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal Studd, Emily K. Landry‐Cuerrier, Manuelle Menzies, Allyson K. Boutin, Stan McAdam, Andrew G. Lane, Jeffrey E. Humphries, Murray M. Ecol Evol Original Research The miniaturization and affordability of new technology is driving a biologging revolution in wildlife ecology with use of animal‐borne data logging devices. Among many new biologging technologies, accelerometers are emerging as key tools for continuously recording animal behavior. Yet a critical, but under‐acknowledged consideration in biologging is the trade‐off between sampling rate and sampling duration, created by battery‐ (or memory‐) related sampling constraints. This is especially acute among small animals, causing most researchers to sample at high rates for very limited durations. Here, we show that high accuracy in behavioral classification is achievable when pairing low‐frequency acceleration recordings with temperature. We conducted 84 hr of direct behavioral observations on 67 free‐ranging red squirrels (200–300 g) that were fitted with accelerometers (2 g) recording tri‐axial acceleration and temperature at 1 Hz. We then used a random forest algorithm and a manually created decision tree, with variable sampling window lengths, to associate observed behavior with logger recorded acceleration and temperature. Finally, we assessed the accuracy of these different classifications using an additional 60 hr of behavioral observations, not used in the initial classification. The accuracy of the manually created decision tree classification using observational data varied from 70.6% to 91.6% depending on the complexity of the tree, with increasing accuracy as complexity decreased. Short duration behavior like running had lower accuracy than long‐duration behavior like feeding. The random forest algorithm offered similarly high overall accuracy, but the manual decision tree afforded the flexibility to create a hierarchical tree, and to adjust sampling window length for behavioral states with varying durations. Low frequency biologging of acceleration and temperature allows accurate behavioral classification of small animals over multi‐month sampling durations. Nevertheless, low sampling rates impose several important limitations, especially related to assessing the classification accuracy of short duration behavior. John Wiley and Sons Inc. 2018-12-27 /pmc/articles/PMC6342100/ /pubmed/30680142 http://dx.doi.org/10.1002/ece3.4786 Text en © 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. |
spellingShingle | Original Research Studd, Emily K. Landry‐Cuerrier, Manuelle Menzies, Allyson K. Boutin, Stan McAdam, Andrew G. Lane, Jeffrey E. Humphries, Murray M. Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal |
title | Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal |
title_full | Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal |
title_fullStr | Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal |
title_full_unstemmed | Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal |
title_short | Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal |
title_sort | behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6342100/ https://www.ncbi.nlm.nih.gov/pubmed/30680142 http://dx.doi.org/10.1002/ece3.4786 |
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