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Song Diversity Predicts the Viability of Fragmented Bird Populations

In the global scenario of increasing habitat fragmentation, finding appropriate indicators of population viability is a priority for conservation. We explored the potential of learned behaviours, specifically acoustic signals, to predict the persistence over time of fragmented bird populations. We f...

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Detalles Bibliográficos
Autores principales: Laiolo, Paola, Vögeli, Matthias, Serrano, David, Tella, José L.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2266806/
https://www.ncbi.nlm.nih.gov/pubmed/18350158
http://dx.doi.org/10.1371/journal.pone.0001822
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author Laiolo, Paola
Vögeli, Matthias
Serrano, David
Tella, José L.
author_facet Laiolo, Paola
Vögeli, Matthias
Serrano, David
Tella, José L.
author_sort Laiolo, Paola
collection PubMed
description In the global scenario of increasing habitat fragmentation, finding appropriate indicators of population viability is a priority for conservation. We explored the potential of learned behaviours, specifically acoustic signals, to predict the persistence over time of fragmented bird populations. We found an association between male song diversity and the annual rate of population change, population productivity and population size, resulting in birds singing poor repertoires in populations more prone to extinction. This is the first demonstration that population viability can be predicted by a cultural trait (acquired via social learning). Our results emphasise that cultural attributes can reflect not only individual-level characteristics, but also the emergent population-level properties. This opens the way to the study of animal cultural diversity in the increasingly common human-altered landscapes.
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spelling pubmed-22668062008-03-19 Song Diversity Predicts the Viability of Fragmented Bird Populations Laiolo, Paola Vögeli, Matthias Serrano, David Tella, José L. PLoS One Research Article In the global scenario of increasing habitat fragmentation, finding appropriate indicators of population viability is a priority for conservation. We explored the potential of learned behaviours, specifically acoustic signals, to predict the persistence over time of fragmented bird populations. We found an association between male song diversity and the annual rate of population change, population productivity and population size, resulting in birds singing poor repertoires in populations more prone to extinction. This is the first demonstration that population viability can be predicted by a cultural trait (acquired via social learning). Our results emphasise that cultural attributes can reflect not only individual-level characteristics, but also the emergent population-level properties. This opens the way to the study of animal cultural diversity in the increasingly common human-altered landscapes. Public Library of Science 2008-03-19 /pmc/articles/PMC2266806/ /pubmed/18350158 http://dx.doi.org/10.1371/journal.pone.0001822 Text en Laiolo et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Laiolo, Paola
Vögeli, Matthias
Serrano, David
Tella, José L.
Song Diversity Predicts the Viability of Fragmented Bird Populations
title Song Diversity Predicts the Viability of Fragmented Bird Populations
title_full Song Diversity Predicts the Viability of Fragmented Bird Populations
title_fullStr Song Diversity Predicts the Viability of Fragmented Bird Populations
title_full_unstemmed Song Diversity Predicts the Viability of Fragmented Bird Populations
title_short Song Diversity Predicts the Viability of Fragmented Bird Populations
title_sort song diversity predicts the viability of fragmented bird populations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2266806/
https://www.ncbi.nlm.nih.gov/pubmed/18350158
http://dx.doi.org/10.1371/journal.pone.0001822
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