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...
Published in: | Scientific Reports |
---|---|
Main Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Nature Portfolio
2021
|
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-021-86718-w https://doaj.org/article/9de6f0aea3194b5197fb348d38402d92 |
id |
ftdoajarticles:oai:doaj.org/article:9de6f0aea3194b5197fb348d38402d92 |
---|---|
record_format |
openpolar |
spelling |
ftdoajarticles:oai:doaj.org/article:9de6f0aea3194b5197fb348d38402d92 2023-05-15T15:50:47+02:00 Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method Sougata Sadhukhan Holly Root-Gutteridge Bilal Habib 2021-03-01T00:00:00Z https://doi.org/10.1038/s41598-021-86718-w https://doaj.org/article/9de6f0aea3194b5197fb348d38402d92 EN eng Nature Portfolio https://doi.org/10.1038/s41598-021-86718-w https://doaj.org/toc/2045-2322 doi:10.1038/s41598-021-86718-w 2045-2322 https://doaj.org/article/9de6f0aea3194b5197fb348d38402d92 Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) Medicine R Science Q article 2021 ftdoajarticles https://doi.org/10.1038/s41598-021-86718-w 2022-12-31T07:30:59Z 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. Article in Journal/Newspaper Canis lupus Directory of Open Access Journals: DOAJ Articles Indian Scientific Reports 11 1 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
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 |
topic_facet |
Medicine R Science Q |
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 in Journal/Newspaper |
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://doi.org/10.1038/s41598-021-86718-w https://doaj.org/article/9de6f0aea3194b5197fb348d38402d92 |
geographic |
Indian |
geographic_facet |
Indian |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_source |
Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) |
op_relation |
https://doi.org/10.1038/s41598-021-86718-w https://doaj.org/toc/2045-2322 doi:10.1038/s41598-021-86718-w 2045-2322 https://doaj.org/article/9de6f0aea3194b5197fb348d38402d92 |
op_doi |
https://doi.org/10.1038/s41598-021-86718-w |
container_title |
Scientific Reports |
container_volume |
11 |
container_issue |
1 |
_version_ |
1766385794462777344 |