Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method
Previous studies have posited the use of acoustics-based surveys to monitor population size and estimate their density. However, decreasing the bias in population estimations, such as by using Capture–Mark–Recapture, requires the identification of individuals using supervised classification methods,...
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ftpubmed:oai:pubmedcentral.nih.gov:8012383 2023-05-15T15:50:46+02:00 Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method Sadhukhan, Sougata Root-Gutteridge, Holly Habib, Bilal 2021-03-31 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012383/ https://doi.org/10.1038/s41598-021-86718-w en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012383/ http://dx.doi.org/10.1038/s41598-021-86718-w © The Author(s) 2021 Open Access This 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/. CC-BY Sci Rep Article Text 2021 ftpubmed https://doi.org/10.1038/s41598-021-86718-w 2021-04-04T01:25:32Z Previous studies have posited the use of acoustics-based surveys to monitor population size and estimate their density. However, decreasing the bias in population estimations, such as by using Capture–Mark–Recapture, requires the identification of individuals using supervised classification methods, especially for sparsely populated species like the wolf which may otherwise be counted repeatedly. The cryptic behaviour of Indian wolf (Canis lupus pallipes) poses serious challenges to survey efforts, and thus, there is no reliable estimate of their population despite a prominent role in the ecosystem. Like other wolves, Indian wolves produce howls that can be detected over distances of more than 6 km, making them ideal candidates for acoustic surveys. Here, we explore the use of a supervised classifier to identify unknown individuals. We trained a supervised Agglomerative Nesting hierarchical clustering (AGNES) model using 49 howls from five Indian wolves and achieved 98% individual identification accuracy. We tested our model’s predictive power using 20 novel howls from a further four individuals (test dataset) and resulted in 75% accuracy in classifying howls to individuals. The model can reduce bias in population estimations using Capture-Mark-Recapture and track individual wolves non-invasively by their howls. This has potential for studies of wolves’ territory use, pack composition, and reproductive behaviour. Our method can potentially be adapted for other species with individually distinctive vocalisations, representing an advanced tool for individual-level monitoring. Text Canis lupus PubMed Central (PMC) Indian Scientific Reports 11 1 |
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Article Sadhukhan, Sougata Root-Gutteridge, Holly Habib, Bilal Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method |
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Previous studies have posited the use of acoustics-based surveys to monitor population size and estimate their density. However, decreasing the bias in population estimations, such as by using Capture–Mark–Recapture, requires the identification of individuals using supervised classification methods, especially for sparsely populated species like the wolf which may otherwise be counted repeatedly. The cryptic behaviour of Indian wolf (Canis lupus pallipes) poses serious challenges to survey efforts, and thus, there is no reliable estimate of their population despite a prominent role in the ecosystem. Like other wolves, Indian wolves produce howls that can be detected over distances of more than 6 km, making them ideal candidates for acoustic surveys. Here, we explore the use of a supervised classifier to identify unknown individuals. We trained a supervised Agglomerative Nesting hierarchical clustering (AGNES) model using 49 howls from five Indian wolves and achieved 98% individual identification accuracy. We tested our model’s predictive power using 20 novel howls from a further four individuals (test dataset) and resulted in 75% accuracy in classifying howls to individuals. The model can reduce bias in population estimations using Capture-Mark-Recapture and track individual wolves non-invasively by their howls. This has potential for studies of wolves’ territory use, pack composition, and reproductive behaviour. Our method can potentially be adapted for other species with individually distinctive vocalisations, representing an advanced tool for individual-level monitoring. |
format |
Text |
author |
Sadhukhan, Sougata Root-Gutteridge, Holly Habib, Bilal |
author_facet |
Sadhukhan, Sougata Root-Gutteridge, Holly Habib, Bilal |
author_sort |
Sadhukhan, Sougata |
title |
Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method |
title_short |
Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method |
title_full |
Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method |
title_fullStr |
Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method |
title_full_unstemmed |
Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method |
title_sort |
identifying unknown indian wolves by their distinctive howls: its potential as a non-invasive survey method |
publisher |
Nature Publishing Group UK |
publishDate |
2021 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012383/ https://doi.org/10.1038/s41598-021-86718-w |
geographic |
Indian |
geographic_facet |
Indian |
genre |
Canis lupus |
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Canis lupus |
op_source |
Sci Rep |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012383/ http://dx.doi.org/10.1038/s41598-021-86718-w |
op_rights |
© The Author(s) 2021 Open Access This 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/. |
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CC-BY |
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https://doi.org/10.1038/s41598-021-86718-w |
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Scientific Reports |
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11 |
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