Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song
J.A.A. was funded by an Australian Government Research Training Program Scholarship and the Australian American Association University of Queensland Fellowship. E.C.G. was funded by a Royal Society Newton International Fellowship and a Royal Society University Research Fellowship. HARC was funded by...
Published in: | Proceedings of the Royal Society B: Biological Sciences |
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Main Authors: | , , , |
Other Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2020
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Subjects: | |
Online Access: | https://hdl.handle.net/10023/19288 https://doi.org/10.1098/rspb.2019.2014 |
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author | Allen, Jenny A. Garland, Ellen C. Dunlop, Rebecca A. Noad, Michael J. |
author2 | The Royal Society University of St Andrews.School of Biology University of St Andrews.Centre for Biological Diversity University of St Andrews.Sea Mammal Research Unit University of St Andrews.Centre for Social Learning & Cognitive Evolution |
author_facet | Allen, Jenny A. Garland, Ellen C. Dunlop, Rebecca A. Noad, Michael J. |
author_sort | Allen, Jenny A. |
collection | University of St Andrews: Digital Research Repository |
container_issue | 1917 |
container_start_page | 20192014 |
container_title | Proceedings of the Royal Society B: Biological Sciences |
container_volume | 286 |
description | J.A.A. was funded by an Australian Government Research Training Program Scholarship and the Australian American Association University of Queensland Fellowship. E.C.G. was funded by a Royal Society Newton International Fellowship and a Royal Society University Research Fellowship. HARC was funded by the US Office of Naval Research, the Australian Defence Science and Technology Organisation, and the Australian Marine Mammal Centre. BRAHSS was funded by the E&P Sound and Marine Life Joint Industry Programme and the US Bureau of Ocean Energy Management. 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, characterized 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 ... |
format | Article in Journal/Newspaper |
genre | Humpback Whale Megaptera novaeangliae |
genre_facet | Humpback Whale Megaptera novaeangliae |
geographic | Queensland |
geographic_facet | Queensland |
id | ftstandrewserep:oai:research-repository.st-andrews.ac.uk:10023/19288 |
institution | Open Polar |
language | English |
op_collection_id | ftstandrewserep |
op_doi | https://doi.org/10.1098/rspb.2019.2014 |
op_relation | Proceedings of the Royal Society B: Biological Sciences 265420428 85076825929 000504313100007 Allen , J A , Garland , E C , Dunlop , R A & Noad , M J 2019 , ' Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song ' , Proceedings of the Royal Society B: Biological Sciences , vol. 286 , no. 1917 , 20192014 . https://doi.org/10.1098/rspb.2019.2014 RIS: urn:7095BA96094B8BE87927405E5837D458 https://hdl.handle.net/10023/19288 doi:10.1098/rspb.2019.2014 NF140667 UF160081 |
op_rights | Copyright © 2019 The Author(s). Published by the Royal Society. All rights reserved. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1098/rspb.2019.2014 |
publishDate | 2020 |
record_format | openpolar |
spelling | ftstandrewserep:oai:research-repository.st-andrews.ac.uk:10023/19288 2025-04-13T14:20:23+00:00 Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song Allen, Jenny A. Garland, Ellen C. Dunlop, Rebecca A. Noad, Michael J. The Royal Society University of St Andrews.School of Biology University of St Andrews.Centre for Biological Diversity University of St Andrews.Sea Mammal Research Unit University of St Andrews.Centre for Social Learning & Cognitive Evolution 2020-01-15T11:30:04Z 680400 6119930 application/pdf https://hdl.handle.net/10023/19288 https://doi.org/10.1098/rspb.2019.2014 eng eng Proceedings of the Royal Society B: Biological Sciences 265420428 85076825929 000504313100007 Allen , J A , Garland , E C , Dunlop , R A & Noad , M J 2019 , ' Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song ' , Proceedings of the Royal Society B: Biological Sciences , vol. 286 , no. 1917 , 20192014 . https://doi.org/10.1098/rspb.2019.2014 RIS: urn:7095BA96094B8BE87927405E5837D458 https://hdl.handle.net/10023/19288 doi:10.1098/rspb.2019.2014 NF140667 UF160081 Copyright © 2019 The Author(s). Published by the Royal Society. All rights reserved. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1098/rspb.2019.2014 Vocal learning Network modelling Syntax Humpback whale Song QH301 Biology DAS BDC R2C QH301 Journal article 2020 ftstandrewserep https://doi.org/10.1098/rspb.2019.2014 2025-03-19T08:01:34Z J.A.A. was funded by an Australian Government Research Training Program Scholarship and the Australian American Association University of Queensland Fellowship. E.C.G. was funded by a Royal Society Newton International Fellowship and a Royal Society University Research Fellowship. HARC was funded by the US Office of Naval Research, the Australian Defence Science and Technology Organisation, and the Australian Marine Mammal Centre. BRAHSS was funded by the E&P Sound and Marine Life Joint Industry Programme and the US Bureau of Ocean Energy Management. 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, characterized 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 ... Article in Journal/Newspaper Humpback Whale Megaptera novaeangliae University of St Andrews: Digital Research Repository Queensland Proceedings of the Royal Society B: Biological Sciences 286 1917 20192014 |
spellingShingle | Vocal learning Network modelling Syntax Humpback whale Song QH301 Biology DAS BDC R2C QH301 Allen, Jenny A. Garland, Ellen C. Dunlop, Rebecca A. Noad, Michael J. Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song |
title | 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_short | 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 |
topic | Vocal learning Network modelling Syntax Humpback whale Song QH301 Biology DAS BDC R2C QH301 |
topic_facet | Vocal learning Network modelling Syntax Humpback whale Song QH301 Biology DAS BDC R2C QH301 |
url | https://hdl.handle.net/10023/19288 https://doi.org/10.1098/rspb.2019.2014 |