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Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids
BACKGROUND: Animal-attached devices can be used on cryptic species to measure their movement and behaviour, enabling unprecedented insights into fundamental aspects of animal ecology and behaviour. However, direct observations of subjects are often still necessary to translate biologging data accura...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8186069/ https://www.ncbi.nlm.nih.gov/pubmed/34099067 http://dx.doi.org/10.1186/s40462-021-00265-7 |
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author | Dickinson, Eleanor R. Twining, Joshua P. Wilson, Rory Stephens, Philip A. Westander, Jennie Marks, Nikki Scantlebury, David M. |
author_facet | Dickinson, Eleanor R. Twining, Joshua P. Wilson, Rory Stephens, Philip A. Westander, Jennie Marks, Nikki Scantlebury, David M. |
author_sort | Dickinson, Eleanor R. |
collection | PubMed |
description | BACKGROUND: Animal-attached devices can be used on cryptic species to measure their movement and behaviour, enabling unprecedented insights into fundamental aspects of animal ecology and behaviour. However, direct observations of subjects are often still necessary to translate biologging data accurately into meaningful behaviours. As many elusive species cannot easily be observed in the wild, captive or domestic surrogates are typically used to calibrate data from devices. However, the utility of this approach remains equivocal. METHODS: Here, we assess the validity of using captive conspecifics, and phylogenetically-similar domesticated counterparts (surrogate species) for calibrating behaviour classification. Tri-axial accelerometers and tri-axial magnetometers were used with behavioural observations to build random forest models to predict the behaviours. We applied these methods using captive Alpine ibex (Capra ibex) and a domestic counterpart, pygmy goats (Capra aegagrus hircus), to predict the behaviour including terrain slope for locomotion behaviours of captive Alpine ibex. RESULTS: Behavioural classification of captive Alpine ibex and domestic pygmy goats was highly accurate (> 98%). Model performance was reduced when using data split per individual, i.e., classifying behaviour of individuals not used to train models (mean ± sd = 56.1 ± 11%). Behavioural classifications using domestic counterparts, i.e., pygmy goat observations to predict ibex behaviour, however, were not sufficient to predict all behaviours of a phylogenetically similar species accurately (> 55%). CONCLUSIONS: We demonstrate methods to refine the use of random forest models to classify behaviours of both captive and free-living animal species. We suggest there are two main reasons for reduced accuracy when using a domestic counterpart to predict the behaviour of a wild species in captivity; domestication leading to morphological differences and the terrain of the environment in which the animals were observed. We also identify limitations when behaviour is predicted in individuals that are not used to train models. Our results demonstrate that biologging device calibration needs to be conducted using: (i) with similar conspecifics, and (ii) in an area where they can perform behaviours on terrain that reflects that of species in the wild. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40462-021-00265-7. |
format | Online Article Text |
id | pubmed-8186069 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81860692021-06-10 Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids Dickinson, Eleanor R. Twining, Joshua P. Wilson, Rory Stephens, Philip A. Westander, Jennie Marks, Nikki Scantlebury, David M. Mov Ecol Research BACKGROUND: Animal-attached devices can be used on cryptic species to measure their movement and behaviour, enabling unprecedented insights into fundamental aspects of animal ecology and behaviour. However, direct observations of subjects are often still necessary to translate biologging data accurately into meaningful behaviours. As many elusive species cannot easily be observed in the wild, captive or domestic surrogates are typically used to calibrate data from devices. However, the utility of this approach remains equivocal. METHODS: Here, we assess the validity of using captive conspecifics, and phylogenetically-similar domesticated counterparts (surrogate species) for calibrating behaviour classification. Tri-axial accelerometers and tri-axial magnetometers were used with behavioural observations to build random forest models to predict the behaviours. We applied these methods using captive Alpine ibex (Capra ibex) and a domestic counterpart, pygmy goats (Capra aegagrus hircus), to predict the behaviour including terrain slope for locomotion behaviours of captive Alpine ibex. RESULTS: Behavioural classification of captive Alpine ibex and domestic pygmy goats was highly accurate (> 98%). Model performance was reduced when using data split per individual, i.e., classifying behaviour of individuals not used to train models (mean ± sd = 56.1 ± 11%). Behavioural classifications using domestic counterparts, i.e., pygmy goat observations to predict ibex behaviour, however, were not sufficient to predict all behaviours of a phylogenetically similar species accurately (> 55%). CONCLUSIONS: We demonstrate methods to refine the use of random forest models to classify behaviours of both captive and free-living animal species. We suggest there are two main reasons for reduced accuracy when using a domestic counterpart to predict the behaviour of a wild species in captivity; domestication leading to morphological differences and the terrain of the environment in which the animals were observed. We also identify limitations when behaviour is predicted in individuals that are not used to train models. Our results demonstrate that biologging device calibration needs to be conducted using: (i) with similar conspecifics, and (ii) in an area where they can perform behaviours on terrain that reflects that of species in the wild. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40462-021-00265-7. BioMed Central 2021-06-07 /pmc/articles/PMC8186069/ /pubmed/34099067 http://dx.doi.org/10.1186/s40462-021-00265-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Dickinson, Eleanor R. Twining, Joshua P. Wilson, Rory Stephens, Philip A. Westander, Jennie Marks, Nikki Scantlebury, David M. Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids |
title | Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids |
title_full | Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids |
title_fullStr | Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids |
title_full_unstemmed | Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids |
title_short | Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids |
title_sort | limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in caprids |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8186069/ https://www.ncbi.nlm.nih.gov/pubmed/34099067 http://dx.doi.org/10.1186/s40462-021-00265-7 |
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