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      New Heuristic Methods for Joint Species Delimitation and Species Tree Inference

      research-article
      *
      Systematic Biology
      Oxford University Press
      Brownie, gene tree parsimony, gene tree species tree, speciation, species delimitation

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          Abstract

          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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          Most cited references66

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          Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach.

          A maximum likelihood estimator based on the coalescent for unequal migration rates and different subpopulation sizes is developed. The method uses a Markov chain Monte Carlo approach to investigate possible genealogies with branch lengths and with migration events. Properties of the new method are shown by using simulated data from a four-population n-island model and a source-sink population model. Our estimation method as coded in migrate is tested against genetree; both programs deliver a very similar likelihood surface. The algorithm converges to the estimates fairly quickly, even when the Markov chain is started from unfavorable parameters. The method was used to estimate gene flow in the Nile valley by using mtDNA data from three human populations.
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            Distinguishing homologous from analogous proteins.

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              A New Look at the Statistical Model Identification

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                Author and article information

                Journal
                Syst Biol
                Syst. Biol
                sysbio
                sysbio
                Systematic Biology
                Oxford University Press
                1063-5157
                1076-836X
                January 2010
                10 November 2009
                10 November 2009
                : 59
                : 1
                : 59-73
                Affiliations
                Department of Ecology & EvolutionaryBiology, University of Tennessee, 569 Dabney Hall, Knoxville, TN 37996-1610, USA
                Author notes
                [* ]Correspondence to be sent to: Department of Ecology & Evolutionary Biology, University of Tennessee, 569 Dabney Hall, Knoxville, TN 37996-1610, USA; E-mail: bomeara@ 123456utk.edu .

                Associate Editor: L. Lacey Knowles

                Article
                10.1093/sysbio/syp077
                5841455
                20525620
                c8e1d76a-768b-4e62-b4e5-ae3ddc33db87
                © 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.

                History
                : 14 February 2008
                : 3 June 2008
                : 21 September 2009
                Categories
                Regular Articles

                Animal science & Zoology
                brownie,gene tree parsimony,gene tree species tree,speciation,species delimitation

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