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      Toward reconstructing the evolution of advanced moths and butterflies (Lepidoptera: Ditrysia): an initial molecular study

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          Abstract

          Background

          In the mega-diverse insect order Lepidoptera (butterflies and moths; 165,000 described species), deeper relationships are little understood within the clade Ditrysia, to which 98% of the species belong. To begin addressing this problem, we tested the ability of five protein-coding nuclear genes (6.7 kb total), and character subsets therein, to resolve relationships among 123 species representing 27 (of 33) superfamilies and 55 (of 100) families of Ditrysia under maximum likelihood analysis.

          Results

          Our trees show broad concordance with previous morphological hypotheses of ditrysian phylogeny, although most relationships among superfamilies are weakly supported. There are also notable surprises, such as a consistently closer relationship of Pyraloidea than of butterflies to most Macrolepidoptera. Monophyly is significantly rejected by one or more character sets for the putative clades Macrolepidoptera as currently defined ( P < 0.05) and Macrolepidoptera excluding Noctuoidea and Bombycoidea sensu lato ( P ≤ 0.005), and nearly so for the superfamily Drepanoidea as currently defined ( P < 0.08). Superfamilies are typically recovered or nearly so, but usually without strong support. Relationships within superfamilies and families, however, are often robustly resolved. We provide some of the first strong molecular evidence on deeper splits within Pyraloidea, Tortricoidea, Geometroidea, Noctuoidea and others.

          Separate analyses of mostly synonymous versus non-synonymous character sets revealed notable differences (though not strong conflict), including a marked influence of compositional heterogeneity on apparent signal in the third codon position (nt3). As available model partitioning methods cannot correct for this variation, we assessed overall phylogeny resolution through separate examination of trees from each character set. Exploration of "tree space" with GARLI, using grid computing, showed that hundreds of searches are typically needed to find the best-feasible phylogeny estimate for these data.

          Conclusion

          Our results (a) corroborate the broad outlines of the current working phylogenetic hypothesis for Ditrysia, (b) demonstrate that some prominent features of that hypothesis, including the position of the butterflies, need revision, and (c) resolve the majority of family and subfamily relationships within superfamilies as thus far sampled. Much further gene and taxon sampling will be needed, however, to strongly resolve individual deeper nodes.

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          Most cited references33

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          MRBAYES: Bayesian inference of phylogenetic trees.

          The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo. MRBAYES, including the source code, documentation, sample data files, and an executable, is available at http://brahms.biology.rochester.edu/software.html.
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            AWTY (are we there yet?): a system for graphical exploration of MCMC convergence in Bayesian phylogenetics.

            A key element to a successful Markov chain Monte Carlo (MCMC) inference is the programming and run performance of the Markov chain. However, the explicit use of quality assessments of the MCMC simulations-convergence diagnostics-in phylogenetics is still uncommon. Here, we present a simple tool that uses the output from MCMC simulations and visualizes a number of properties of primary interest in a Bayesian phylogenetic analysis, such as convergence rates of posterior split probabilities and branch lengths. Graphical exploration of the output from phylogenetic MCMC simulations gives intuitive and often crucial information on the success and reliability of the analysis. The tool presented here complements convergence diagnostics already available in other software packages primarily designed for other applications of MCMC. Importantly, the common practice of using trace-plots of a single parameter or summary statistic, such as the likelihood score of sampled trees, can be misleading for assessing the success of a phylogenetic MCMC simulation. The program is available as source under the GNU General Public License and as a web application at http://ceb.scs.fsu.edu/awty.
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              Overcredibility of molecular phylogenies obtained by Bayesian phylogenetics.

              Bayesian phylogenetics has recently been proposed as a powerful method for inferring molecular phylogenies, and it has been reported that the mammalian and some plant phylogenies were resolved by using this method. The statistical confidence of interior branches as judged by posterior probabilities in Bayesian analysis is generally higher than that as judged by bootstrap probabilities in maximum likelihood analysis, and this difference has been interpreted as an indication that bootstrap support may be too conservative. However, it is possible that the posterior probabilities are too high or too liberal instead. Here, we show by computer simulation that posterior probabilities in Bayesian analysis can be excessively liberal when concatenated gene sequences are used, whereas bootstrap probabilities in neighbor-joining and maximum likelihood analyses are generally slightly conservative. These results indicate that bootstrap probabilities are more suitable for assessing the reliability of phylogenetic trees than posterior probabilities and that the mammalian and plant phylogenies may not have been fully resolved.
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                Author and article information

                Journal
                BMC Evol Biol
                BMC Evolutionary Biology
                BioMed Central
                1471-2148
                2009
                2 December 2009
                : 9
                : 280
                Affiliations
                [1 ]Center for Biosystems Research, University of Maryland Biotechnology Institute, College Park, Maryland 20742, USA
                [2 ]Department of Entomology, University of Maryland, College Park, Maryland 20742, USA
                [3 ]Laboratory of Molecular Evolution, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA
                [4 ]Department of Plant Medicine, Chungbuk National University, Cheongju 361-763, Korea
                [5 ]Department of Entomology, University of Minnesota, St. Paul, Minnesota 55455, USA
                [6 ]Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2E9, Canada
                [7 ]Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia, Apartat de correus 2085, 46071 Valencia, Spain
                [8 ]Systematic Entomology Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, Maryland 20705, USA
                [9 ]Encyclopedia of Life, Smithsonian Institution, Washington, D.C. 20013-7012, USA
                [10 ]Department of Entomology, Smithsonian Institution, Washington, D.C. 20013-7012, USA
                [11 ]Plant Pest Diagnostics Branch, California Department of Food and Agriculture, 3294 Meadowview Road, Sacramento, California 95832-1448, USA
                [12 ]Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
                [13 ]Bavarian State Collection of Zoology, Münchhausenstrasse 21, D-81247 München, Germany
                [14 ]Department of Entomology, The Natural History Museum, Cromwell Road, London SW7 5BD, UK
                [15 ]Department of Biological Sciences, National Sun Yat-Sen University, Kaohsiung 804, Taiwan
                Article
                1471-2148-9-280
                10.1186/1471-2148-9-280
                2796670
                19954545
                9f73ae36-a3ce-4cc3-8155-e5db4c06e6e0
                Copyright ©2009 Regier et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 January 2009
                : 2 December 2009
                Categories
                Research article

                Evolutionary Biology
                Evolutionary Biology

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