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      One species or four? Yes!...and, no. Or, arbitrary assignment of lineages to species obscures the diversification processes of Neotropical fishes

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          Abstract

          Species are fundamental units in many biological disciplines, but there is continuing disagreement as to what species are, how to define them, and even whether the concept is useful. While some of this debate can be attributed to inadequate data and insufficient statistical frameworks in alpha taxonomy, an equal part results from the ambiguity over what species are expected to represent by the many who use them. Here, mtDNA data, microsatellite data, and sequence data from 17 nuclear loci are used in an integrated and quantitative manner to resolve the presence of evolutionary lineages, their contemporary and historical structure, and their correspondence to species, in a species complex of Amazonian peacock “bass” cichlids ( Cichla pinima sensu lato). Results suggest that the historical narrative for these populations is more complex than can be portrayed by recognizing them as one, two, or four species: their history and contemporary dynamics cannot be unambiguously rendered as discrete units (taxa) at any level without both choosing the supremacy of one delimitation criterion and obscuring the very information that provides insight into the diversification process. This calls into question the utility of species as a rank, term, or concept, and suggests that while biologists may have a reasonable grasp of the structure of evolution, our methods of conveying these insights need updating. The lack of correspondence between evolutionary phenomena and discrete species should serve as a null hypothesis, and researchers should focus on quantifying the diversity in nature at whatever hierarchical level it occurs.

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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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            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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              What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity.

              We review commonly used population definitions under both the ecological paradigm (which emphasizes demographic cohesion) and the evolutionary paradigm (which emphasizes reproductive cohesion) and find that none are truly operational. We suggest several quantitative criteria that might be used to determine when groups of individuals are different enough to be considered 'populations'. Units for these criteria are migration rate (m) for the ecological paradigm and migrants per generation (Nm) for the evolutionary paradigm. These criteria are then evaluated by applying analytical methods to simulated genetic data for a finite island model. Under the standard parameter set that includes L = 20 High mutation (microsatellite-like) loci and samples of S = 50 individuals from each of n = 4 subpopulations, power to detect departures from panmixia was very high ( approximately 100%; P < 0.001) even with high gene flow (Nm = 25). A new method, comparing the number of correct population assignments with the random expectation, performed as well as a multilocus contingency test and warrants further consideration. Use of Low mutation (allozyme-like) markers reduced power more than did halving S or L. Under the standard parameter set, power to detect restricted gene flow below a certain level X (H(0): Nm < X) can also be high, provided that true Nm < or = 0.5X. Developing the appropriate test criterion, however, requires assumptions about several key parameters that are difficult to estimate in most natural populations. Methods that cluster individuals without using a priori sampling information detected the true number of populations only under conditions of moderate or low gene flow (Nm < or = 5), and power dropped sharply with smaller samples of loci and individuals. A simple algorithm based on a multilocus contingency test of allele frequencies in pairs of samples has high power to detect the true number of populations even with Nm = 25 but requires more rigorous statistical evaluation. The ecological paradigm remains challenging for evaluations using genetic markers, because the transition from demographic dependence to independence occurs in a region of high migration where genetic methods have relatively little power. Some recent theoretical developments and continued advances in computational power provide hope that this situation may change in the future.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                24 February 2017
                2017
                : 12
                : 2
                : e0172349
                Affiliations
                [001]Department of Life Sciences, Texas A&M University-Corpus Christi, Corpus Christi, Texas, United States of America
                National Cheng Kung University, TAIWAN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: SCW.

                • Data curation: SCW.

                • Formal analysis: SCW.

                • Investigation: SCW.

                • Methodology: SCW.

                • Project administration: SCW.

                • Resources: SCW.

                • Supervision: SCW.

                • Validation: SCW.

                • Visualization: SCW.

                • Writing – original draft: SCW.

                • Writing – review & editing: SCW.

                Article
                PONE-D-16-43203
                10.1371/journal.pone.0172349
                5325279
                28235096
                f70c3937-f0ee-46b7-9d30-8162087a6b2c
                © 2017 Stuart C. Willis

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 October 2016
                : 3 February 2017
                Page count
                Figures: 6, Tables: 4, Pages: 26
                Funding
                The author received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Processes
                Speciation
                Species Delimitation
                Biology and life sciences
                Genetics
                DNA
                Forms of DNA
                Mitochondrial DNA
                Biology and life sciences
                Biochemistry
                Nucleic acids
                DNA
                Forms of DNA
                Mitochondrial DNA
                Biology and Life Sciences
                Biogeography
                Phylogeography
                Ecology and Environmental Sciences
                Biogeography
                Phylogeography
                Earth Sciences
                Geography
                Biogeography
                Phylogeography
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Phylogeography
                Biology and Life Sciences
                Genetics
                Population Genetics
                Phylogeography
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Phylogeography
                Biology and Life Sciences
                Taxonomy
                Computer and Information Sciences
                Data Management
                Taxonomy
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Genetics
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Biology and Life Sciences
                Genetics
                Heredity
                Genetic Mapping
                Haplotypes
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Molecular Biology Assays and Analysis Techniques
                Phylogenetic Analysis
                Research and Analysis Methods
                Molecular Biology Techniques
                Molecular Biology Assays and Analysis Techniques
                Phylogenetic Analysis
                Custom metadata
                Data are available as found in: Genbank (sequences), DQ841819-DQ841946, GU295691-GU295801, JQ926745-JQ926982, KF299741-KF300519; Dryad (microsatellite genotypes), http://dx.doi.org/10.5061/dryad.h4s73s5c.

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