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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Published in:Scientific Reports
Main Authors: Sadhukhan, Sougata, Root-Gutteridge, Holly, Habib, Bilal
Format: Text
Language:English
Published: Nature Publishing Group UK 2021
Subjects:
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012383/
https://doi.org/10.1038/s41598-021-86718-w
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spelling 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
institution Open Polar
collection PubMed Central (PMC)
op_collection_id ftpubmed
language English
topic Article
spellingShingle Article
Sadhukhan, Sougata
Root-Gutteridge, Holly
Habib, Bilal
Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method
topic_facet Article
description 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
genre_facet 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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