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New Heuristic Methods for Joint Species Delimitation and Species Tree Inference

Species delimitation and species tree inference are difficult problems in cases of recent divergence, especially when different loci have different histories. This paper quantifies the difficulty of jointly finding the division of samples to species and estimating a species tree without constraining...

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Detalles Bibliográficos
Autor principal: O'Meara, Brian C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5841455/
https://www.ncbi.nlm.nih.gov/pubmed/20525620
http://dx.doi.org/10.1093/sysbio/syp077
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author O'Meara, Brian C.
author_facet O'Meara, Brian C.
author_sort O'Meara, Brian C.
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description Species delimitation and species tree inference are difficult problems in cases of recent divergence, especially when different loci have different histories. This paper quantifies the difficulty of jointly finding the division of samples to species and estimating a species tree without constraining the possible assignments a priori. It introduces a parametric and a nonparametric method, including new heuristic search strategies, to do this delimitation and tree inference using individual gene trees as input. The new methods were evaluated using thousands of simulations and 4 empirical data sets. These analyses suggest that the new methods, especially the nonparametric one, may provide useful insights for systematists working at the species level with molecular data. However, they still often return incorrect results.
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spelling pubmed-58414552018-03-28 New Heuristic Methods for Joint Species Delimitation and Species Tree Inference O'Meara, Brian C. Syst Biol Regular Articles Species delimitation and species tree inference are difficult problems in cases of recent divergence, especially when different loci have different histories. This paper quantifies the difficulty of jointly finding the division of samples to species and estimating a species tree without constraining the possible assignments a priori. It introduces a parametric and a nonparametric method, including new heuristic search strategies, to do this delimitation and tree inference using individual gene trees as input. The new methods were evaluated using thousands of simulations and 4 empirical data sets. These analyses suggest that the new methods, especially the nonparametric one, may provide useful insights for systematists working at the species level with molecular data. However, they still often return incorrect results. Oxford University Press 2010-01 2009-11-10 /pmc/articles/PMC5841455/ /pubmed/20525620 http://dx.doi.org/10.1093/sysbio/syp077 Text en © The Author(s) 2009. Published by Oxford University Press on behalf of Society of Systematic Biologists. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Regular Articles
O'Meara, Brian C.
New Heuristic Methods for Joint Species Delimitation and Species Tree Inference
title New Heuristic Methods for Joint Species Delimitation and Species Tree Inference
title_full New Heuristic Methods for Joint Species Delimitation and Species Tree Inference
title_fullStr New Heuristic Methods for Joint Species Delimitation and Species Tree Inference
title_full_unstemmed New Heuristic Methods for Joint Species Delimitation and Species Tree Inference
title_short New Heuristic Methods for Joint Species Delimitation and Species Tree Inference
title_sort new heuristic methods for joint species delimitation and species tree inference
topic Regular Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5841455/
https://www.ncbi.nlm.nih.gov/pubmed/20525620
http://dx.doi.org/10.1093/sysbio/syp077
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