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      The history of early bee diversification based on five genes plus morphology

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      Proceedings of the National Academy of Sciences

      Proceedings of the National Academy of Sciences

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          Abstract

          Bees, the largest (>16,000 species) and most important radiation of pollinating insects, originated in early to mid-Cretaceous, roughly in synchrony with the angiosperms (flowering plants). Understanding the diversification of the bees and the coevolutionary history of bees and angiosperms requires a well supported phylogeny of bees (as well as angiosperms). We reconstructed a robust phylogeny of bees at the family and subfamily levels using a data set of five genes (4,299 nucleotide sites) plus morphology (109 characters). The molecular data set included protein coding (elongation factor-1alpha, RNA polymerase II, and LW rhodopsin), as well as ribosomal (28S and 18S) nuclear gene data. Analyses of both the DNA data set and the DNA+morphology data set by parsimony and Bayesian methods yielded a single well supported family-level tree topology that places Melittidae as a paraphyletic group at the base of the phylogeny of bees. This topology ("Melittidae-LT basal") is significantly better than a previously proposed alternative topology ("Colletidae basal") based both on likelihood and Bayesian methods. Our results have important implications for understanding the early diversification, historical biogeography, host-plant evolution, and fossil record of bees. The earliest branches of bee phylogeny include lineages that are predominantly host-plant specialists, suggesting that host-plant specificity is an ancestral trait in bees. Our results suggest an African origin for bees, because the earliest branches of the tree include predominantly African lineages. These results also help explain the predominance of Melittidae, Apidae, and Megachilidae among the earliest fossil bees.

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          MrBayes 3: Bayesian phylogenetic inference under mixed models.

          MrBayes 3 performs Bayesian phylogenetic analysis combining information from different data partitions or subsets evolving under different stochastic evolutionary models. This allows the user to analyze heterogeneous data sets consisting of different data types-e.g. morphological, nucleotide, and protein-and to explore a wide variety of structured models mixing partition-unique and shared parameters. The program employs MPI to parallelize Metropolis coupling on Macintosh or UNIX clusters.
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            Evaluation of the maximum likelihood estimate of the evolutionary tree topologies from DNA sequence data, and the branching order in hominoidea.

            A maximum likelihood method for inferring evolutionary trees from DNA sequence data was developed by Felsenstein (1981). In evaluating the extent to which the maximum likelihood tree is a significantly better representation of the true tree, it is important to estimate the variance of the difference between log likelihood of different tree topologies. Bootstrap resampling can be used for this purpose (Hasegawa et al. 1988; Hasegawa and Kishino 1989), but it imposes a great computation burden. To overcome this difficulty, we developed a new method for estimating the variance by expressing it explicitly. The method was applied to DNA sequence data from primates in order to evaluate the maximum likelihood branching order among Hominoidea. It was shown that, although the orangutan is convincingly placed as an outgroup of a human and African apes clade, the branching order among human, chimpanzee, and gorilla cannot be determined confidently from the DNA sequence data presently available when the evolutionary rate constancy is not assumed.
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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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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proceedings of the National Academy of Sciences
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                October 10 2006
                October 10 2006
                October 02 2006
                October 10 2006
                : 103
                : 41
                : 15118-15123
                Article
                10.1073/pnas.0604033103
                1586180
                17015826
                © 2006
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