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      Speciation Hypotheses from Phylogeographic Delimitation Yield an Integrative Taxonomy for Seal Salamanders ( Desmognathus monticola)

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      Systematic Biology
      Oxford University Press (OUP)

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          Abstract

          Significant advances have been made in species delimitation and numerous methods can test precisely defined models of speciation, though the synthesis of phylogeography and taxonomy is still sometimes incomplete. Emerging consensus treats distinct genealogical clusters in genome-scale data as strong initial evidence of speciation in most cases, a hypothesis that must therefore be falsified under an explicit evolutionary model. We can now test speciation hypotheses linking trait differentiation to specific mechanisms of divergence with increasingly large data sets. Integrative taxonomy can, therefore, reflect an understanding of how each axis of variation relates to underlying speciation processes, with nomenclature for distinct evolutionary lineages. We illustrate this approach here with Seal Salamanders (Desmognathus monticola) and introduce a new unsupervised machine-learning approach for species delimitation. Plethodontid salamanders are renowned for their morphological conservatism despite extensive phylogeographic divergence. We discover 2 geographic genetic clusters, for which demographic and spatial models of ecology and gene flow provide robust support for ecogeographic speciation despite limited phenotypic divergence. These data are integrated under evolutionary mechanisms (e.g., spatially localized gene flow with reduced migration) and reflected in emergent properties expected under models of reinforcement (e.g., ethological isolation and selection against hybrids). Their genetic divergence is prima facie evidence for species-level distinctiveness, supported by speciation models and divergence along axes such as behavior, geography, and climate that suggest an ecological basis with subsequent reinforcement through prezygotic isolation. As data sets grow more comprehensive, species-delimitation models can be tested, rejected, or corroborated as explicit speciation hypotheses, providing for reciprocal illumination of evolutionary processes and integrative taxonomies. [Desmognathus; integrative taxonomy; machine learning; species delimitation.]

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          ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates

          Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates. The improvement is achieved by incorporating a model of rate-heterogeneity across sites not previously considered in this context, and by allowing concurrent searches of model-space and tree-space.
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            IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

            Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
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              New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

              PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Systematic Biology
                Oxford University Press (OUP)
                1063-5157
                1076-836X
                January 01 2023
                May 19 2023
                September 28 2022
                January 01 2023
                May 19 2023
                September 28 2022
                : 72
                : 1
                : 179-197
                Article
                10.1093/sysbio/syac065
                e15101ed-3588-4655-921a-7c560ccd8cf4
                © 2022

                https://academic.oup.com/pages/standard-publication-reuse-rights

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