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      Molecular data support placement of Cameronia in Ostropomycetidae (Lecanoromycetes, Ascomycota)

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          Abstract

          The phylogenetic position of the Tasmanian endemic genus Cameronia Kantvilas is studied using partial sequences of nuclear LSU and mitochondrial SSU ribosomal DNA. Monophyly of the genus is supported, as is its placement in Ostropomycetidae, although its position within this subclass remains uncertain. Given the lack of close relatives to Cameronia and its morphological differences compared to other families with perithecioid ascomata in Ostropomycetidae, the new family Cameroniaceae Kantvilas & Lumbsch is proposed.

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          Most cited references 38

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          Bayesian phylogenetic analysis of combined data.

          The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5% of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more parameter-rich models, but the best model overall is also the most complex and Bayes factors do not support exclusion of apparently weak parameters from this model. Thus, Bayes factors appear to be useful for selecting among complex models, but it is still unclear whether their use strikes a reasonable balance between model complexity and error in parameter estimates.
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            The general stochastic model of nucleotide substitution.

            DNA sequence evolution through nucleotide substitution may be assimilated to a stationary Markov process. The fundamental equations of the general model, with 12 independent substitution parameters, are used to obtain a formula which corrects the effect of multiple and parallel substitutions on the measure of evolutionary divergence between two homologous sequences. We show that only reversible models, with six independent parameters, allow the calculation of the substitution rates. Simulation experiments on DNA sequence evolution through nucleotide substitution call into question the effectiveness of the general model (and of any other more detailed description); nevertheless, the general model results are slightly superior to any of its particular cases.
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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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                Author and article information

                Journal
                MycoKeys
                MC
                Pensoft Publishers
                1314-4049
                1314-4057
                November 30 2012
                November 30 2012
                : 5
                : 31-44
                Article
                10.3897/mycokeys.5.4140
                © 2012
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