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      Sexual Dichromatism Drives Diversification within a Major Radiation of African Amphibians

      1 , 2 , 1 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 9 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 25 , 4 , 27 , 4 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 32 , 35 , 36 , 37 , 1 , 32 , 17 , 25 , 32 , 18 , 38 , 1
      Systematic Biology
      Oxford University Press (OUP)

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          Abstract

          Theory predicts that sexually dimorphic traits under strong sexual selection, particularly those involved with intersexual signaling, can accelerate speciation and produce bursts of diversification. Sexual dichromatism (sexual dimorphism in color) is widely used as a proxy for sexual selection and is associated with rapid diversification in several animal groups, yet studies using phylogenetic comparative methods to explicitly test for an association between sexual dichromatism and diversification have produced conflicting results. Sexual dichromatism is rare in frogs, but it is both striking and prevalent in African reed frogs, a major component of the diverse frog radiation termed Afrobatrachia. In contrast to most other vertebrates, reed frogs display female-biased dichromatism in which females undergo color transformation, often resulting in more ornate coloration in females than in males. We produce a robust phylogeny of Afrobatrachia to investigate the evolutionary origins of sexual dichromatism in this radiation and examine whether the presence of dichromatism is associated with increased rates of net diversification. We find that sexual dichromatism evolved once within hyperoliids and was followed by numerous independent reversals to monochromatism. We detect significant diversification rate heterogeneity in Afrobatrachia and find that sexually dichromatic lineages have double the average net diversification rate of monochromatic lineages. By conducting trait simulations on our empirical phylogeny, we demonstrate that our inference of trait-dependent diversification is robust. Although sexual dichromatism in hyperoliid frogs is linked to their rapid diversification and supports macroevolutionary predictions of speciation by sexual selection, the function of dichromatism in reed frogs remains unclear. We propose that reed frogs are a compelling system for studying the roles of natural and sexual selection on the evolution of sexual dichromatism across micro- and macroevolutionary timescales.

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          Universal and rapid salt-extraction of high quality genomic DNA for PCR-based techniques.

          A very simple, fast, universally applicable and reproducible method to extract high quality megabase genomic DNA from different organisms is described. We applied the same method to extract high quality complex genomic DNA from different tissues (wheat, barley, potato, beans, pear and almond leaves as well as fungi, insects and shrimps' fresh tissue) without any modification. The method does not require expensive and environmentally hazardous reagents and equipment. It can be performed even in low technology laboratories. The amount of tissue required by this method is approximately 50-100 mg. The quantity and the quality of the DNA extracted by this method is high enough to perform hundreds of PCR-based reactions and also to be used in other DNA manipulation techniques such as restriction digestion, Southern blot and cloning.
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            Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

            Recent developments in marginal likelihood estimation for model selection in the field of Bayesian phylogenetics and molecular evolution have emphasized the poor performance of the harmonic mean estimator (HME). Although these studies have shown the merits of new approaches applied to standard normally distributed examples and small real-world data sets, not much is currently known concerning the performance and computational issues of these methods when fitting complex evolutionary and population genetic models to empirical real-world data sets. Further, these approaches have not yet seen widespread application in the field due to the lack of implementations of these computationally demanding techniques in commonly used phylogenetic packages. We here investigate the performance of some of these new marginal likelihood estimators, specifically, path sampling (PS) and stepping-stone (SS) sampling for comparing models of demographic change and relaxed molecular clocks, using synthetic data and real-world examples for which unexpected inferences were made using the HME. Given the drastically increased computational demands of PS and SS sampling, we also investigate a posterior simulation-based analogue of Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC), a model comparison approach that shares with the HME the appealing feature of having a low computational overhead over the original MCMC analysis. We confirm that the HME systematically overestimates the marginal likelihood and fails to yield reliable model classification and show that the AICM performs better and may be a useful initial evaluation of model choice but that it is also, to a lesser degree, unreliable. We show that PS and SS sampling substantially outperform these estimators and adjust the conclusions made concerning previous analyses for the three real-world data sets that we reanalyzed. The methods used in this article are now available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.
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              Is Open Access

              ASTRAL: genome-scale coalescent-based species tree estimation

              Motivation: Species trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions. Results: We present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy—improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees. Availability and implementation: ASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral. Contact: warnow@illinois.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                Systematic Biology
                Oxford University Press (OUP)
                1063-5157
                1076-836X
                November 2019
                November 01 2019
                April 23 2019
                November 2019
                November 01 2019
                April 23 2019
                : 68
                : 6
                : 859-875
                Affiliations
                [1 ]Museum of Vertebrate Zoology, University of California, Berkeley, CA 94720, USA
                [2 ]Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA
                [3 ]Department of Vertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, DC 20560-0162, USA
                [4 ]Florida Museum of Natural History, University of Florida, Gainesville, FL 32611, USA
                [5 ]Department of Biology, Villanova University, 800 Lancaster Avenue, Villanova, PA 19085, USA
                [6 ]Department of Environmental Sciences, University of Basel, Basel 4056, Switzerland
                [7 ]German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig 0413, Germany
                [8 ]Max Planck Institute for Evolutionary Anthropology, Leipzig 0413, Germany
                [9 ]Port Elizabeth Museum, P.O. Box 11347, Humewood 6013, South Africa
                [10 ]Department of Zoology, Nelson Mandela Metropolitan University, P.O. Box 77000, Port Elizabeth 6031, South Africa
                [11 ]African Amphibian Conservation Research Group, Unit for Environmental Sciences and Management, North-West University, Potchefstroom 2520, South Africa
                [12 ]Flora Fauna & Man, Ecological Services Ltd. Tortola, British Virgin, Island
                [13 ]Unit for Environmental Sciences and Management, North-West University, Potchefstroom 2520, South Africa
                [14 ]Department of Biological Sciences, Florida State University, Tallahassee, FL 32306, USA
                [15 ]Zoological Natural History Museum, Addis Ababa University, Arat Kilo, Addis Ababa, Ethiopia
                [16 ]School of Natural Resource Management, Nelson Mandela University, George Campus, George 6530, South Africa
                [17 ]Department of Biology, Institute of Sciences, University of Koblenz-Landau, Universitätsstr. 1, D-56070 Koblenz, Germany
                [18 ]California Academy of Sciences, San Francisco, CA 94118, USA
                [19 ]Museum of Zoology, Senckenberg Natural History Collections Dresden, Königsbrücker Landstr. 159, Dresden 01109, Germany
                [20 ]Department of Ecology, Technische Universität Berlin, Rothenburgstr. 12, Berlin 12165, Germany
                [21 ]Department of Biological Sciences, University of Texas at El Paso, El Paso, TX 79968, USA
                [22 ]The Czech Academy of Sciences, Institute of Vertebrate Biology, Brno, Czech Republic
                [23 ]Department of Zoology, National Museum, Prague, Czech Republic
                [24 ]Pietermaritzburg, KwaZulu-Natal, South Africa
                [25 ]Museum für Naturkunde, Leibniz Institute for Evolution and Biodiversity Science, Biodiversity Dynamics, Invalidenstr. 43, Berlin 10115, Germany
                [26 ]Across the River – A Transboundary Peace Park for Sierra Leone and Liberia, The Royal Society for the Protection of Birds, 164 Dama Road, Kenema, Sierra Leone
                [27 ]Natural History Museum of Denmark, University of Copenhagen, Universitetsparken 15, Copenhagen 2100, Denmark
                [28 ]Department of Biological Sciences, University of Cincinnati, 614 Rieveschl Hall, Cincinnati, OH 45220, USA
                [29 ]Life Sciences, Field Museum of Natural History, 1400 S. Lake Shore Dr., Chicago, IL 60605, USA
                [30 ]Department of Biology, Burke Museum of Natural History and Culture, University of Washington, Seattle, WA, USA
                [31 ]Life Sciences Department, Natural History Museum, London SW7 5BD, UK
                [32 ]Biogeography Department, Trier University, Universitätsring 15, Trier 54296, Germany
                [33 ]CIBIO Research Centre in Biodiversity and Genetic Resources, InBIO, Universidade do Porto, Campus Agrario de Vairão, Rua Padre Armando Quintas, No. 7, 4485-661 Vairão, Vila do Conde, Portugal
                [34 ]Tropical Biodiversity Section, Science Museum of Trento, Corso del lavoro e della Scienza 3, Trento 38122, Italy
                [35 ]Royal Belgian Institute of Natural Sciences, OD Taxonomy and Phylogeny, Rue Vautier 29, B-1000 Brussels, Belgium
                [36 ]Forestry Research Institute of Ghana, P.O. Box 63, Fumesua, Kumasi, Ghana
                [37 ]Département Origines et Evolution, Muséum National d’Histoire Naturelle, UMR 7205 ISYEB, 25 rue Cuvier, Paris 75005, France
                [38 ]Institut National de Recherche en Sciences Exactes et Naturelles, Brazzaville BP 2400, République du Congo
                Article
                10.1093/sysbio/syz023
                6934645
                31140573
                baf16b27-ecef-46a8-816c-1117868d3f8a
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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