Using identity calls to detect structure in acoustic datasets
Abstract 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 identi...
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crwiley:10.1111/2041-210x.13644 2024-09-15T18:37:35+00:00 Using identity calls to detect structure in acoustic datasets Hersh, Taylor A. Gero, Shane Rendell, Luke Whitehead, Hal Natural Sciences and Engineering Research Council of Canada Carlsbergfondet Faculty of Graduate Studies, Dalhousie University Mitacs Villum Fonden Oticon Fonden Natur og Univers, Det Frie Forskningsråd Nova Scotia Research Innovation Trust National Geographic Society Killam Trusts Explorers Club PADI Foundation 2021 http://dx.doi.org/10.1111/2041-210x.13644 https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13644 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/2041-210X.13644 https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13644 en eng Wiley http://creativecommons.org/licenses/by-nc-nd/4.0/ Methods in Ecology and Evolution volume 12, issue 9, page 1668-1678 ISSN 2041-210X 2041-210X journal-article 2021 crwiley https://doi.org/10.1111/2041-210x.13644 2024-08-01T04:21:12Z Abstract 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. 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. 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. 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 Wiley Online Library Methods in Ecology and Evolution 12 9 1668 1678 |
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English |
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Abstract 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. 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. 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. 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 ... |
author2 |
Natural Sciences and Engineering Research Council of Canada Carlsbergfondet Faculty of Graduate Studies, Dalhousie University Mitacs Villum Fonden Oticon Fonden Natur og Univers, Det Frie Forskningsråd Nova Scotia Research Innovation Trust National Geographic Society Killam Trusts Explorers Club PADI Foundation |
format |
Article in Journal/Newspaper |
author |
Hersh, Taylor A. Gero, Shane Rendell, Luke Whitehead, Hal |
spellingShingle |
Hersh, Taylor A. Gero, Shane Rendell, Luke Whitehead, Hal Using identity calls to detect structure in acoustic datasets |
author_facet |
Hersh, Taylor A. Gero, Shane Rendell, Luke Whitehead, Hal |
author_sort |
Hersh, Taylor A. |
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 |
publisher |
Wiley |
publishDate |
2021 |
url |
http://dx.doi.org/10.1111/2041-210x.13644 https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13644 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/2041-210X.13644 https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13644 |
genre |
Sperm whale |
genre_facet |
Sperm whale |
op_source |
Methods in Ecology and Evolution volume 12, issue 9, page 1668-1678 ISSN 2041-210X 2041-210X |
op_rights |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
op_doi |
https://doi.org/10.1111/2041-210x.13644 |
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Methods in Ecology and Evolution |
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12 |
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9 |
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1668 |
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1678 |
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1810481956331192320 |