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      Phylogeography of the neotropical Anopheles triannulatus complex (Diptera: Culicidae) supports deep structure and complex patterns

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          Abstract

          Background

          The molecular phylogenetic relationships and population structure of the species of the Anopheles triannulatus complex: Anopheles triannulatus s.s., Anopheles halophylus and the putative species Anopheles triannulatus C were investigated.

          Methods

          The mitochondrial COI gene, the nuclear white gene and rDNA ITS2 of samples that include the known geographic distribution of these taxa were analyzed. Phylogenetic analyses were performed using Bayesian inference, Maximum parsimony and Maximum likelihood approaches.

          Results

          Each data set analyzed septely yielded a different topology but none provided evidence for the seption of An. halophylus and An. triannulatus C, consistent with the hypothesis that the two are undergoing incipient speciation. The phylogenetic analyses of the white gene found three main clades, whereas the statistical parsimony network detected only a single metapopulation of Anopheles triannulatus s.l. Seven COI lineages were detected by phylogenetic and network analysis. In contrast, the network, but not the phylogenetic analyses, strongly supported three ITS2 groups. Combined data analyses provided the best resolution of the trees, with two major clades, Amazonian (clade I) and trans-Andean + Amazon Delta (clade II). Clade I consists of multiple subclades: An. halophylus +  An. triannulatus C; trans-Andean Venezuela; central Amazonia + central Bolivia; Atlantic coastal lowland; and Amazon delta. Clade II includes three subclades: Panama; cis-Andean Colombia; and cis-Venezuela. The Amazon delta specimens are in both clades, likely indicating local sympatry. Spatial and molecular variance analyses detected nine groups, corroborating some of subclades obtained in the combined data analysis.

          Conclusion

          Combination of the three molecular markers provided the best resolution for differentiation within An. triannulatus s.s. and An. halophylus and C. The latest two species seem to be very closely related and the analyses performed were not conclusive regarding species differentiation. Further studies including new molecular markers would be desirable to solve this species status question. Besides, results of the study indicate a trans-Andean origin for An. triannulatus s.l. The potential implications for malaria epidemiology remain to be investigated.

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          Most cited references53

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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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            Speciation in amazonian forest birds.

            J Haffer (1969)
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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
                Parasit Vectors
                Parasit Vectors
                Parasites & Vectors
                BioMed Central
                1756-3305
                2013
                22 February 2013
                : 6
                : 47
                Affiliations
                [1 ]New York State Department of Health, Wadsworth Center, Griffin Laboratory, Albany, NY, USA
                [2 ]Department of Biomedical Sciences, School of Public Health, State University of New York, Albany, NY, USA
                [3 ]Departamento de Entomología, Instituto Oswaldo Cruz-Fiocruz, Rio de Janeiro, Brazil
                [4 ]Instituto de Investigaciones Científicas y Servicios de Alta Tecnología, Clayton, Panamá, República de Panamá
                [5 ]Division of Entomology, Walter Reed Army Institute of Research, Silver Spring, MD, USA
                [6 ]Departamento de Epidemiologia, Faculdade de Saúde Pública, Universidade de São Paulo, São Paulo, Brazil
                [7 ]Superintendência de Controle de Endemias, SUCEN, São Paulo, Brazil
                [8 ]Department of Biological Sciences, Eastern Illinois University, Charleston, IL, USA
                [9 ]Instituto Superior de Entomología "Dr. Abraham Willink", Facultad de Ciencias, Naturales e Instituto Miguel Lillo, Universidad Nacional de Tucumán, Tucumán, Argentina
                [10 ]Laboratorio de Biologia de Vectores, Instituto de Zoología y Ecología Tropical, Universidad Central de Venezuela, Caracas, Venezuela
                [11 ]Instituto Evandro Chagas, Secção de psitologia, Belém, Brazil
                [12 ]Grupo de Microbiología Molecular, Escuela de Microbiología, Universidad de Antioquia, Medellín, Colombia
                [13 ]Present address: Division Infectious Diseases University of California San Diego, George Palade Labs, School of Medicine, 92093, 9500 Gilman Drive, MC 0741, La Jolla, CA, USA
                Article
                1756-3305-6-47
                10.1186/1756-3305-6-47
                3606328
                23433428
                6423f658-30fe-431b-8044-5e214c12cb07
                Copyright ©2013 Moreno 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
                : 26 November 2012
                : 13 February 2013
                Categories
                Research

                Parasitology
                anopheles triannulatus s.s.,anopheles halophylus,anopheles triannulatus c,phylogeography,coi gene,white gene,its2

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