Using agent-based models to understand the role of individuals in the song evolution of humpback whales ( Megaptera novaeangliae )
Male humpback whales produce hierarchically structured songs, primarily during the breeding season. These songs gradually change over the course of the breeding season, and are generally population specific. However, instances have been recorded of more rapid song changes where the song of a populat...
Published in: | Music & Science |
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Format: | Article in Journal/Newspaper |
Language: | English |
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2018
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Online Access: | https://risweb.st-andrews.ac.uk/portal/en/researchoutput/using-agentbased-models-to-understand-the-role-of-individuals-in-the-song-evolution-of-humpback-whales-megaptera-novaeangliae(b5a079c9-0aaa-4b25-9d43-4d474bb2b6f7).html https://doi.org/10.1177/2059204318757021 https://research-repository.st-andrews.ac.uk/bitstream/10023/12929/1/McLoughlin_2018_MS_Agent_basedmodels_CC.pdf |
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ftunstandrewcris:oai:risweb.st-andrews.ac.uk:publications/b5a079c9-0aaa-4b25-9d43-4d474bb2b6f7 2023-05-15T16:36:06+02:00 Using agent-based models to understand the role of individuals in the song evolution of humpback whales ( Megaptera novaeangliae ) Mcloughlin, Michael Lamoni, Luca Garland, Ellen C. Ingram, Simon Kirke, Alexis Noad, Michael J. Rendell, Luke Miranda, Eduardo 2018 application/pdf https://risweb.st-andrews.ac.uk/portal/en/researchoutput/using-agentbased-models-to-understand-the-role-of-individuals-in-the-song-evolution-of-humpback-whales-megaptera-novaeangliae(b5a079c9-0aaa-4b25-9d43-4d474bb2b6f7).html https://doi.org/10.1177/2059204318757021 https://research-repository.st-andrews.ac.uk/bitstream/10023/12929/1/McLoughlin_2018_MS_Agent_basedmodels_CC.pdf eng eng info:eu-repo/semantics/openAccess Mcloughlin , M , Lamoni , L , Garland , E C , Ingram , S , Kirke , A , Noad , M J , Rendell , L & Miranda , E 2018 , ' Using agent-based models to understand the role of individuals in the song evolution of humpback whales ( Megaptera novaeangliae ) ' , Music & Science , vol. 1 . https://doi.org/10.1177/2059204318757021 Agent-based model Humpback whale Song Song evolution Vocal learning article 2018 ftunstandrewcris https://doi.org/10.1177/2059204318757021 2022-06-02T07:48:17Z Male humpback whales produce hierarchically structured songs, primarily during the breeding season. These songs gradually change over the course of the breeding season, and are generally population specific. However, instances have been recorded of more rapid song changes where the song of a population can be replaced by the song of an adjacent population. The mechanisms that drive these changes are not currently understood, and difficulties in tracking individual whales over long migratory routes mean field studies to understand these mechanisms are not feasible. In order to help understand the mechanisms that drive these song changes, we present here a spatially explicit agent-based model inspired by methods used in computer music research. We model the migratory patterns of humpback whales, a simple song learning and production method coupled with sound transmission loss, and how often singing occurs during these migratory cycles. This model is then extended to include learning biases that may be responsible for driving changes in the song, such as a bias towards novel song, production errors, and the coupling of novel song bias and production errors. While none of the methods showed population song replacement, our model shows that shared feeding grounds where conspecifics are able to mix provides key opportunities for cultural transmission, and production errors facilitated gradually changing songs. Our results point towards other learning biases being necessary in order for population song replacement to occur. Article in Journal/Newspaper Humpback Whale Megaptera novaeangliae University of St Andrews: Research Portal Music & Science 1 205920431875702 |
institution |
Open Polar |
collection |
University of St Andrews: Research Portal |
op_collection_id |
ftunstandrewcris |
language |
English |
topic |
Agent-based model Humpback whale Song Song evolution Vocal learning |
spellingShingle |
Agent-based model Humpback whale Song Song evolution Vocal learning Mcloughlin, Michael Lamoni, Luca Garland, Ellen C. Ingram, Simon Kirke, Alexis Noad, Michael J. Rendell, Luke Miranda, Eduardo Using agent-based models to understand the role of individuals in the song evolution of humpback whales ( Megaptera novaeangliae ) |
topic_facet |
Agent-based model Humpback whale Song Song evolution Vocal learning |
description |
Male humpback whales produce hierarchically structured songs, primarily during the breeding season. These songs gradually change over the course of the breeding season, and are generally population specific. However, instances have been recorded of more rapid song changes where the song of a population can be replaced by the song of an adjacent population. The mechanisms that drive these changes are not currently understood, and difficulties in tracking individual whales over long migratory routes mean field studies to understand these mechanisms are not feasible. In order to help understand the mechanisms that drive these song changes, we present here a spatially explicit agent-based model inspired by methods used in computer music research. We model the migratory patterns of humpback whales, a simple song learning and production method coupled with sound transmission loss, and how often singing occurs during these migratory cycles. This model is then extended to include learning biases that may be responsible for driving changes in the song, such as a bias towards novel song, production errors, and the coupling of novel song bias and production errors. While none of the methods showed population song replacement, our model shows that shared feeding grounds where conspecifics are able to mix provides key opportunities for cultural transmission, and production errors facilitated gradually changing songs. Our results point towards other learning biases being necessary in order for population song replacement to occur. |
format |
Article in Journal/Newspaper |
author |
Mcloughlin, Michael Lamoni, Luca Garland, Ellen C. Ingram, Simon Kirke, Alexis Noad, Michael J. Rendell, Luke Miranda, Eduardo |
author_facet |
Mcloughlin, Michael Lamoni, Luca Garland, Ellen C. Ingram, Simon Kirke, Alexis Noad, Michael J. Rendell, Luke Miranda, Eduardo |
author_sort |
Mcloughlin, Michael |
title |
Using agent-based models to understand the role of individuals in the song evolution of humpback whales ( Megaptera novaeangliae ) |
title_short |
Using agent-based models to understand the role of individuals in the song evolution of humpback whales ( Megaptera novaeangliae ) |
title_full |
Using agent-based models to understand the role of individuals in the song evolution of humpback whales ( Megaptera novaeangliae ) |
title_fullStr |
Using agent-based models to understand the role of individuals in the song evolution of humpback whales ( Megaptera novaeangliae ) |
title_full_unstemmed |
Using agent-based models to understand the role of individuals in the song evolution of humpback whales ( Megaptera novaeangliae ) |
title_sort |
using agent-based models to understand the role of individuals in the song evolution of humpback whales ( megaptera novaeangliae ) |
publishDate |
2018 |
url |
https://risweb.st-andrews.ac.uk/portal/en/researchoutput/using-agentbased-models-to-understand-the-role-of-individuals-in-the-song-evolution-of-humpback-whales-megaptera-novaeangliae(b5a079c9-0aaa-4b25-9d43-4d474bb2b6f7).html https://doi.org/10.1177/2059204318757021 https://research-repository.st-andrews.ac.uk/bitstream/10023/12929/1/McLoughlin_2018_MS_Agent_basedmodels_CC.pdf |
genre |
Humpback Whale Megaptera novaeangliae |
genre_facet |
Humpback Whale Megaptera novaeangliae |
op_source |
Mcloughlin , M , Lamoni , L , Garland , E C , Ingram , S , Kirke , A , Noad , M J , Rendell , L & Miranda , E 2018 , ' Using agent-based models to understand the role of individuals in the song evolution of humpback whales ( Megaptera novaeangliae ) ' , Music & Science , vol. 1 . https://doi.org/10.1177/2059204318757021 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1177/2059204318757021 |
container_title |
Music & Science |
container_volume |
1 |
container_start_page |
205920431875702 |
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1766026401478082560 |