Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song
Vocal communication systems have a set of rules that govern the arrangement of acoustic signals, broadly defined as 'syntax'. However, there is a limited understanding of potentially shared or analogous rules across vocal displays in different taxa. Recent work on songbirds has investigate...
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ftzenodo:oai:zenodo.org:5010875 2024-09-15T18:11:14+00:00 Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song Allen, Jennifer Garland, Ellen Dunlop, Rebecca Noad, Michael 2019-12-17 https://doi.org/10.5061/dryad.2bvq83bkv unknown Zenodo https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.2bvq83bkv oai:zenodo.org:5010875 info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode Syntax vocal learning song network modelling Megaptera novaeangliae info:eu-repo/semantics/other 2019 ftzenodo https://doi.org/10.5061/dryad.2bvq83bkv 2024-07-26T04:01:40Z Vocal communication systems have a set of rules that govern the arrangement of acoustic signals, broadly defined as 'syntax'. However, there is a limited understanding of potentially shared or analogous rules across vocal displays in different taxa. Recent work on songbirds has investigated syntax using network-based modelling. This technique quantifies features such as connectivity (adjacent signals in a sequence) and recurring patterns. Here, we apply network-based modelling to the complex, hierarchically structured songs of humpback whales (Megaptera novaeangliae) from east Australia. Given the song's annual evolving pattern and the cultural conformity of males within a population, network modelling captured the patterns of multiple song types over 13 consecutive years. Song arrangements in each year displayed clear "small-world" network structure, characterised by clusters of highly connected sounds. Transitions between these connected sounds further suggested a combination of both structural stability and variability. Small-world network structure within humpback songs may facilitate the characteristic and persistent vocal learning observed. Similar small-world structures and transition patterns are found in several birdsong displays, indicating common syntactic patterns among vocal learning in multiple taxa. Understanding the syntactic rules governing vocal displays in multiple, independently evolving lineages may indicate what rules or structural features are important to the evolution of complex communication, including human language. Other/Unknown Material Humpback Whale Megaptera novaeangliae Zenodo |
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Syntax vocal learning song network modelling Megaptera novaeangliae |
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Syntax vocal learning song network modelling Megaptera novaeangliae Allen, Jennifer Garland, Ellen Dunlop, Rebecca Noad, Michael Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song |
topic_facet |
Syntax vocal learning song network modelling Megaptera novaeangliae |
description |
Vocal communication systems have a set of rules that govern the arrangement of acoustic signals, broadly defined as 'syntax'. However, there is a limited understanding of potentially shared or analogous rules across vocal displays in different taxa. Recent work on songbirds has investigated syntax using network-based modelling. This technique quantifies features such as connectivity (adjacent signals in a sequence) and recurring patterns. Here, we apply network-based modelling to the complex, hierarchically structured songs of humpback whales (Megaptera novaeangliae) from east Australia. Given the song's annual evolving pattern and the cultural conformity of males within a population, network modelling captured the patterns of multiple song types over 13 consecutive years. Song arrangements in each year displayed clear "small-world" network structure, characterised by clusters of highly connected sounds. Transitions between these connected sounds further suggested a combination of both structural stability and variability. Small-world network structure within humpback songs may facilitate the characteristic and persistent vocal learning observed. Similar small-world structures and transition patterns are found in several birdsong displays, indicating common syntactic patterns among vocal learning in multiple taxa. Understanding the syntactic rules governing vocal displays in multiple, independently evolving lineages may indicate what rules or structural features are important to the evolution of complex communication, including human language. |
format |
Other/Unknown Material |
author |
Allen, Jennifer Garland, Ellen Dunlop, Rebecca Noad, Michael |
author_facet |
Allen, Jennifer Garland, Ellen Dunlop, Rebecca Noad, Michael |
author_sort |
Allen, Jennifer |
title |
Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song |
title_short |
Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song |
title_full |
Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song |
title_fullStr |
Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song |
title_full_unstemmed |
Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song |
title_sort |
network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song |
publisher |
Zenodo |
publishDate |
2019 |
url |
https://doi.org/10.5061/dryad.2bvq83bkv |
genre |
Humpback Whale Megaptera novaeangliae |
genre_facet |
Humpback Whale Megaptera novaeangliae |
op_relation |
https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.2bvq83bkv oai:zenodo.org:5010875 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode |
op_doi |
https://doi.org/10.5061/dryad.2bvq83bkv |
_version_ |
1810448825593102336 |