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Convergence of multiple markers and analysis methods defines the genetic distinctiveness of cryptic pitvipers

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NIAID Data Ecosystem2026-03-10 收录
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http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.q70b05k9
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Using multiple markers and multiple analytical approaches is critical for establishing species boundaries reliably, especially so in the case of cryptic species. Despite development of new and powerful analytical methods, most studies continue to adopt a few, with the choice often being subjective. One such example is routine analysis of Amplified Fragment Length Polymorphism (AFLP) data using population genetic models despite disparity between method assumptions and data properties. The application of newly developed methods for analyzing this dominant marker may not be entirely clear in the context of species delimitation. In this study, we use AFLPs and mtDNA to investigate cryptic speciation in the Trimeresurus macrops complex that belongs to a taxonomically difficult lineage of Asian pitvipers. We analyze AFLPs using population genetic, phylogenetic, multivariate statistical, and Bayes Factor Delimitation methods. A gene tree from three mtDNA markers provided additional evidence. Our results show that the inferences about species boundaries that can be derived from population genetic analysis of AFLPs have certain limitations. In contrast, four multivariate statistical analyses produced clear clusters that are consistent with each other, as well as with Bayes Factor Delimitation results, and with mtDNA and total evidence phylogenies. Furthermore, our results concur with allopatric distributions and patterns of variation in individual morphological characters previously identified in the three proposed species: T. macrops sensu stricto, T. cardamomensis, and T. rubeus. Our study provides evidence for reproductive isolation and genetic distinctiveness that define these taxa as full species. In addition, we re-emphasize the importance of examining congruence of results from multiple methods of AFLP analysis for inferring species diversity.
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2017-10-17
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