Using identity calls to detect structure in acoustic datasets

Acoustic analyses can be powerful tools for illuminating structure within and between populations, especially for cryptic or difficult to access taxa. Acoustic repertoires are often compared using aggregate similarity measures across all calls of a particular type, but specific group identity calls...

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Published in:Methods in Ecology and Evolution
Main Authors: Hersh, T., Gero, S., Rendell, L., Whitehead, H.
Format: Article in Journal/Newspaper
Language:English
Published: 2021
Subjects:
Online Access:http://hdl.handle.net/21.11116/0000-0009-2AA8-B
http://hdl.handle.net/21.11116/0000-0009-2AAA-9
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spelling ftpubman:oai:pure.mpg.de:item_3341325 2023-08-27T04:12:14+02:00 Using identity calls to detect structure in acoustic datasets Hersh, T. Gero, S. Rendell, L. Whitehead, H. 2021-05-20 application/pdf http://hdl.handle.net/21.11116/0000-0009-2AA8-B http://hdl.handle.net/21.11116/0000-0009-2AAA-9 eng eng info:eu-repo/semantics/altIdentifier/doi/10.1111/2041-210X.13644 http://hdl.handle.net/21.11116/0000-0009-2AA8-B http://hdl.handle.net/21.11116/0000-0009-2AAA-9 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Methods in Ecology and Evolution info:eu-repo/semantics/article 2021 ftpubman https://doi.org/10.1111/2041-210X.13644 2023-08-02T00:43:43Z Acoustic analyses can be powerful tools for illuminating structure within and between populations, especially for cryptic or difficult to access taxa. Acoustic repertoires are often compared using aggregate similarity measures across all calls of a particular type, but specific group identity calls may more clearly delineate structure in some taxa. 2. We present a new method—the identity call method—that estimates the number of acoustically distinct subdivisions in a set of repertoires and identifies call types that characterize those subdivisions. The method uses contaminated mixture models to identify call types, assigning each call a probability of belonging to each type. Repertoires are hierarchically clustered based on similarities in call type usage, producing a dendrogram with ‘identity clades’ of repertoires and the ‘identity calls’ that best characterize each clade. We validated this approach using acoustic data from sperm whales, grey-breasted wood-wrens and Australian field crickets, and ran a suite of tests to assess parameter sensitivity. 3. For all taxa, the method detected diagnostic signals (identity calls) and structure (identity clades; sperm whale subpopulations, wren subspecies and cricket species) that were consistent with past research. Some datasets were more sensitive to parameter variation than others, which may reflect real uncertainty or biological variability in the taxa examined. We recommend that users perform comparative analyses of different parameter combinations to determine which portions of the dendrogram warrant careful versus confident interpretation. 4. The presence of group-characteristic identity calls does not necessarily mean animals perceive them as such. Fine-scale experiments like playbacks are a key next step to understand call perception and function. This method can help inform such studies by identifying calls that may be salient to animals and are good candidates for investigation or playback stimuli. For cryptic or difficult to access taxa with group-specific ... Article in Journal/Newspaper Sperm whale Max Planck Society: MPG.PuRe Methods in Ecology and Evolution 12 9 1668 1678
institution Open Polar
collection Max Planck Society: MPG.PuRe
op_collection_id ftpubman
language English
description Acoustic analyses can be powerful tools for illuminating structure within and between populations, especially for cryptic or difficult to access taxa. Acoustic repertoires are often compared using aggregate similarity measures across all calls of a particular type, but specific group identity calls may more clearly delineate structure in some taxa. 2. We present a new method—the identity call method—that estimates the number of acoustically distinct subdivisions in a set of repertoires and identifies call types that characterize those subdivisions. The method uses contaminated mixture models to identify call types, assigning each call a probability of belonging to each type. Repertoires are hierarchically clustered based on similarities in call type usage, producing a dendrogram with ‘identity clades’ of repertoires and the ‘identity calls’ that best characterize each clade. We validated this approach using acoustic data from sperm whales, grey-breasted wood-wrens and Australian field crickets, and ran a suite of tests to assess parameter sensitivity. 3. For all taxa, the method detected diagnostic signals (identity calls) and structure (identity clades; sperm whale subpopulations, wren subspecies and cricket species) that were consistent with past research. Some datasets were more sensitive to parameter variation than others, which may reflect real uncertainty or biological variability in the taxa examined. We recommend that users perform comparative analyses of different parameter combinations to determine which portions of the dendrogram warrant careful versus confident interpretation. 4. The presence of group-characteristic identity calls does not necessarily mean animals perceive them as such. Fine-scale experiments like playbacks are a key next step to understand call perception and function. This method can help inform such studies by identifying calls that may be salient to animals and are good candidates for investigation or playback stimuli. For cryptic or difficult to access taxa with group-specific ...
format Article in Journal/Newspaper
author Hersh, T.
Gero, S.
Rendell, L.
Whitehead, H.
spellingShingle Hersh, T.
Gero, S.
Rendell, L.
Whitehead, H.
Using identity calls to detect structure in acoustic datasets
author_facet Hersh, T.
Gero, S.
Rendell, L.
Whitehead, H.
author_sort Hersh, T.
title Using identity calls to detect structure in acoustic datasets
title_short Using identity calls to detect structure in acoustic datasets
title_full Using identity calls to detect structure in acoustic datasets
title_fullStr Using identity calls to detect structure in acoustic datasets
title_full_unstemmed Using identity calls to detect structure in acoustic datasets
title_sort using identity calls to detect structure in acoustic datasets
publishDate 2021
url http://hdl.handle.net/21.11116/0000-0009-2AA8-B
http://hdl.handle.net/21.11116/0000-0009-2AAA-9
genre Sperm whale
genre_facet Sperm whale
op_source Methods in Ecology and Evolution
op_relation info:eu-repo/semantics/altIdentifier/doi/10.1111/2041-210X.13644
http://hdl.handle.net/21.11116/0000-0009-2AA8-B
http://hdl.handle.net/21.11116/0000-0009-2AAA-9
op_rights info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.1111/2041-210X.13644
container_title Methods in Ecology and Evolution
container_volume 12
container_issue 9
container_start_page 1668
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