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      Evolution of East Asia’s Arcto-Tertiary relict Euptelea (Eupteleaceae) shaped by Late Neogene vicariance and Quaternary climate change

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          Abstract

          Background

          The evolutionary origin and historical demography of extant Arcto-Tertiary forest species in East Asia is still poorly understood. Here, we reconstructed the evolutionary and population demographic history of the two extant Euptelea species in China ( E. pleiosperma) and Japan ( E. polyandra) . Chloroplast/nuclear DNA sequences and microsatellite loci were obtained from 36 Euptelea populations to explore molecular structure and diversity in relation to past and present distributions based on ecological niche modelling (ENM). Time-calibrated phylogenetic/phylogeographic inferences and niche-identity tests were used to infer the historical process of lineage formation.

          Results

          Euptelea pleiosperma diverged from E. polyandra around the Late Miocene and experienced significant ecological differentiation. A near-simultaneous diversification of six phylogroups occurred during the mid-to-late Pliocene, in response to the abrupt uplift of the eastern Tibetan Plateau and an increasingly cooler and drier climate. Populations of E. pleiosperma seem to have been mostly stationary through the last glacial cycles, while those of E. polyandra reflect more recent climate-induced cycles of range contraction and expansion.

          Conclusions

          Our results illustrate how Late Neogene climatic/tectonic changes promoted speciation and lineage diversification in East Asia’s Tertiary relict flora. They also demonstrate for the first time a greater variation in such species’ responses to glacial cycles in Japan when compared to congeners in China.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12862-016-0636-x) contains supplementary material, which is available to authorized users.

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          The Community Climate System Model Version 3 (CCSM3)

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            Measuring the accuracy of diagnostic systems.

            J Swets (1988)
            Diagnostic systems of several kinds are used to distinguish between two classes of events, essentially "signals" and "noise". For them, analysis in terms of the "relative operating characteristic" of signal detection theory provides a precise and valid measure of diagnostic accuracy. It is the only measure available that is uninfluenced by decision biases and prior probabilities, and it places the performances of diverse systems on a common, easily interpreted scale. Representative values of this measure are reported here for systems in medical imaging, materials testing, weather forecasting, information retrieval, polygraph lie detection, and aptitude testing. Though the measure itself is sound, the values obtained from tests of diagnostic systems often require qualification because the test data on which they are based are of unsure quality. A common set of problems in testing is faced in all fields. How well these problems are handled, or can be handled in a given field, determines the degree of confidence that can be placed in a measured value of accuracy. Some fields fare much better than others.
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              DnaSP, DNA polymorphism analyses by the coalescent and other methods.

              DnaSP is a software package for the analysis of DNA polymorphism data. Present version introduces several new modules and features which, among other options allow: (1) handling big data sets (approximately 5 Mb per sequence); (2) conducting a large number of coalescent-based tests by Monte Carlo computer simulations; (3) extensive analyses of the genetic differentiation and gene flow among populations; (4) analysing the evolutionary pattern of preferred and unpreferred codons; (5) generating graphical outputs for an easy visualization of results. The software package, including complete documentation and examples, is freely available to academic users from: http://www.ub.es/dnasp
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                Author and article information

                Contributors
                qyxhero@zju.edu.cn
                Journal
                BMC Evol Biol
                BMC Evol. Biol
                BMC Evolutionary Biology
                BioMed Central (London )
                1471-2148
                22 March 2016
                22 March 2016
                2016
                : 16
                : 66
                Affiliations
                [ ]Key Laboratory of Conservation Biology for Endangered Wildlife of the Ministry of Education, and College of Life Sciences, Zhejiang University, Hangzhou, 310058 China
                [ ]Department of Ecology & Evolution, Salzburg University, A-5020 Salzburg, Austria
                [ ]Laboratory of Plant Evolution and Biodiversity, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, 153-0041 Japan
                Article
                636
                10.1186/s12862-016-0636-x
                4802896
                27001058
                82332a71-f376-49cf-8928-0a4a1145fbd6
                © Cao et al. 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 18 November 2015
                : 13 March 2016
                Funding
                Funded by: the National Science Foundation of China
                Award ID: 31570214
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Evolutionary Biology
                Evolutionary Biology

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