A comparison of metrics for estimating phylogenetic signal under alternative evolutionary models
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Genetics and Molecular Biology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572012000400019 |
Resumo: | Several metrics have been developed for estimating phylogenetic signal in comparative data. These may be important both in guiding future studies on correlated evolution and for inferring broad-scale evolutionary and ecological processes (e.g., phylogenetic niche conservatism). Notwithstanding, the validity of some of these metrics is under debate, especially after the development of more sophisticated model-based approaches that estimate departure from particular evolutionary models (i.e., Brownian motion). Here, two of these model-based metrics (Blomberg's K-statistics and Pagel's λ) are compared with three statistical approaches [Moran's I autocorrelation coefficient, coefficients of determination from the autoregressive method (ARM), and phylogenetic eigenvector regression (PVR)]. Based on simulations of a trait evolving under Brownian motion for a phylogeny with 209 species, we showed that all metrics are strongly, although non-linearly, correlated to each other. Our analyses revealed that statistical approaches provide valid results and may be still particularly useful when detailed phylogenies are unavailable or when trait variation among species is difficult to describe by more standard Brownian or O-U evolutionary models. |
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Genetics and Molecular Biology |
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A comparison of metrics for estimating phylogenetic signal under alternative evolutionary modelsautocorrelationBlomberg's KPagel's lambdaautoregressive methodMoran's Iphylogenetic eigenvector regressionSeveral metrics have been developed for estimating phylogenetic signal in comparative data. These may be important both in guiding future studies on correlated evolution and for inferring broad-scale evolutionary and ecological processes (e.g., phylogenetic niche conservatism). Notwithstanding, the validity of some of these metrics is under debate, especially after the development of more sophisticated model-based approaches that estimate departure from particular evolutionary models (i.e., Brownian motion). Here, two of these model-based metrics (Blomberg's K-statistics and Pagel's λ) are compared with three statistical approaches [Moran's I autocorrelation coefficient, coefficients of determination from the autoregressive method (ARM), and phylogenetic eigenvector regression (PVR)]. Based on simulations of a trait evolving under Brownian motion for a phylogeny with 209 species, we showed that all metrics are strongly, although non-linearly, correlated to each other. Our analyses revealed that statistical approaches provide valid results and may be still particularly useful when detailed phylogenies are unavailable or when trait variation among species is difficult to describe by more standard Brownian or O-U evolutionary models.Sociedade Brasileira de Genética2012-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572012000400019Genetics and Molecular Biology v.35 n.3 2012reponame:Genetics and Molecular Biologyinstname:Sociedade Brasileira de Genética (SBG)instacron:SBG10.1590/S1415-47572012005000053info:eu-repo/semantics/openAccessDiniz-Filho,José Alexandre F.Santos,ThiagoRangel,Thiago FernandoBini,Luis Mauricioeng2012-08-10T00:00:00Zoai:scielo:S1415-47572012000400019Revistahttp://www.gmb.org.br/ONGhttps://old.scielo.br/oai/scielo-oai.php||editor@gmb.org.br1678-46851415-4757opendoar:2012-08-10T00:00Genetics and Molecular Biology - Sociedade Brasileira de Genética (SBG)false |
dc.title.none.fl_str_mv |
A comparison of metrics for estimating phylogenetic signal under alternative evolutionary models |
title |
A comparison of metrics for estimating phylogenetic signal under alternative evolutionary models |
spellingShingle |
A comparison of metrics for estimating phylogenetic signal under alternative evolutionary models Diniz-Filho,José Alexandre F. autocorrelation Blomberg's K Pagel's lambda autoregressive method Moran's I phylogenetic eigenvector regression |
title_short |
A comparison of metrics for estimating phylogenetic signal under alternative evolutionary models |
title_full |
A comparison of metrics for estimating phylogenetic signal under alternative evolutionary models |
title_fullStr |
A comparison of metrics for estimating phylogenetic signal under alternative evolutionary models |
title_full_unstemmed |
A comparison of metrics for estimating phylogenetic signal under alternative evolutionary models |
title_sort |
A comparison of metrics for estimating phylogenetic signal under alternative evolutionary models |
author |
Diniz-Filho,José Alexandre F. |
author_facet |
Diniz-Filho,José Alexandre F. Santos,Thiago Rangel,Thiago Fernando Bini,Luis Mauricio |
author_role |
author |
author2 |
Santos,Thiago Rangel,Thiago Fernando Bini,Luis Mauricio |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Diniz-Filho,José Alexandre F. Santos,Thiago Rangel,Thiago Fernando Bini,Luis Mauricio |
dc.subject.por.fl_str_mv |
autocorrelation Blomberg's K Pagel's lambda autoregressive method Moran's I phylogenetic eigenvector regression |
topic |
autocorrelation Blomberg's K Pagel's lambda autoregressive method Moran's I phylogenetic eigenvector regression |
description |
Several metrics have been developed for estimating phylogenetic signal in comparative data. These may be important both in guiding future studies on correlated evolution and for inferring broad-scale evolutionary and ecological processes (e.g., phylogenetic niche conservatism). Notwithstanding, the validity of some of these metrics is under debate, especially after the development of more sophisticated model-based approaches that estimate departure from particular evolutionary models (i.e., Brownian motion). Here, two of these model-based metrics (Blomberg's K-statistics and Pagel's λ) are compared with three statistical approaches [Moran's I autocorrelation coefficient, coefficients of determination from the autoregressive method (ARM), and phylogenetic eigenvector regression (PVR)]. Based on simulations of a trait evolving under Brownian motion for a phylogeny with 209 species, we showed that all metrics are strongly, although non-linearly, correlated to each other. Our analyses revealed that statistical approaches provide valid results and may be still particularly useful when detailed phylogenies are unavailable or when trait variation among species is difficult to describe by more standard Brownian or O-U evolutionary models. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572012000400019 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572012000400019 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S1415-47572012005000053 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Genética |
publisher.none.fl_str_mv |
Sociedade Brasileira de Genética |
dc.source.none.fl_str_mv |
Genetics and Molecular Biology v.35 n.3 2012 reponame:Genetics and Molecular Biology instname:Sociedade Brasileira de Genética (SBG) instacron:SBG |
instname_str |
Sociedade Brasileira de Genética (SBG) |
instacron_str |
SBG |
institution |
SBG |
reponame_str |
Genetics and Molecular Biology |
collection |
Genetics and Molecular Biology |
repository.name.fl_str_mv |
Genetics and Molecular Biology - Sociedade Brasileira de Genética (SBG) |
repository.mail.fl_str_mv |
||editor@gmb.org.br |
_version_ |
1752122385069244416 |