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      Mitogenomic evaluation of the historical biogeography of cichlids toward reliable dating of teleostean divergences

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          Abstract

          Background

          Recent advances in DNA sequencing and computation offer the opportunity for reliable estimates of divergence times between organisms based on molecular data. Bayesian estimations of divergence times that do not assume the molecular clock use time constraints at multiple nodes, usually based on the fossil records, as major boundary conditions. However, the fossil records of bony fishes may not adequately provide effective time constraints at multiple nodes. We explored an alternative source of time constraints in teleostean phylogeny by evaluating a biogeographic hypothesis concerning freshwater fishes from the family Cichlidae (Perciformes: Labroidei).

          Results

          We added new mitogenomic sequence data from six cichlid species and conducted phylogenetic analyses using a large mitogenomic data set. We found a reciprocal monophyly of African and Neotropical cichlids and their sister group relationship to some Malagasy taxa (Ptychochrominae sensu Sparks and Smith). All of these taxa clustered with a Malagasy + Indo/Sri Lankan clade (Etroplinae sensu Sparks and Smith). The results of the phylogenetic analyses and divergence time estimations between continental cichlid clades were much more congruent with Gondwanaland origin and Cretaceous vicariant divergences than with Cenozoic transmarine dispersal between major continents.

          Conclusion

          We propose to add the biogeographic assumption of cichlid divergences by continental fragmentation as effective time constraints in dating teleostean divergence times. We conducted divergence time estimations among teleosts by incorporating these additional time constraints and achieved a considerable reduction in credibility intervals in the estimated divergence times.

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          Most cited references54

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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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            Bayes Factors

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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
                BMC Evol Biol
                BMC Evolutionary Biology
                BioMed Central
                1471-2148
                2008
                23 July 2008
                : 8
                : 215
                Affiliations
                [1 ]Ocean Research Institute, The University of Tokyo, 1-15-1 Minamidai, Nakano-ku, Tokyo 164-8639, Japan
                [2 ]Division of Material Science and Biological Science, Graduate School of Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8602, Japan
                [3 ]Department of Information and Biological Sciences, Graduate School of Natural Sciences, Nagoya City University, 1 Yamanohata, Mizuho-cho, Mizuho-ku, Nagoya 467-8501, Japan
                [4 ]Department of Zoology, Natural History Museum and Institute, Chiba, 955-2 Aoba-cho, Chuo-ku, Chiba 260-8682, Japan
                Article
                1471-2148-8-215
                10.1186/1471-2148-8-215
                2496912
                18651942
                045f478d-4a4c-4469-8590-ec9416e9729a
                Copyright © 2008 Azuma 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
                : 18 March 2008
                : 23 July 2008
                Categories
                Research Article

                Evolutionary Biology
                Evolutionary Biology

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