Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method

Abstract 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...

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Autores principales:	Sougata Sadhukhan, Holly Root-Gutteridge, Bilal Habib
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9de6f0aea3194b5197fb348d38402d92
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spelling	oai:doaj.org-article:9de6f0aea3194b5197fb348d38402d922021-12-02T14:23:18ZIdentifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method10.1038/s41598-021-86718-w2045-2322https://doaj.org/article/9de6f0aea3194b5197fb348d38402d922021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86718-whttps://doaj.org/toc/2045-2322Abstract 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.Sougata SadhukhanHolly Root-GutteridgeBilal HabibNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sougata Sadhukhan Holly Root-Gutteridge Bilal Habib Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method
description	Abstract 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	article
author	Sougata Sadhukhan Holly Root-Gutteridge Bilal Habib
author_facet	Sougata Sadhukhan Holly Root-Gutteridge Bilal Habib
author_sort	Sougata Sadhukhan
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 Portfolio
publishDate	2021
url	https://doaj.org/article/9de6f0aea3194b5197fb348d38402d92
work_keys_str_mv	AT sougatasadhukhan identifyingunknownindianwolvesbytheirdistinctivehowlsitspotentialasanoninvasivesurveymethod AT hollyrootgutteridge identifyingunknownindianwolvesbytheirdistinctivehowlsitspotentialasanoninvasivesurveymethod AT bilalhabib identifyingunknownindianwolvesbytheirdistinctivehowlsitspotentialasanoninvasivesurveymethod
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Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method

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