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Phylogeographic structure, cryptic speciation and demographic history of the sharpbelly (Hemiculter leucisculus), a freshwater habitat generalist from southern China

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      Species with broad distributions frequently divide into multiple genetic forms and may therefore be viewed as “cryptic species”. Here, we used the mitochondrial cytochrome b (Cytb) and 12 nuclear DNA loci to investigate phylogeographic structures of the sharpbelly ( Hemiculter leucisculus) in rivers in southern China and explored how the geological and climatic factors have shaped the genetic diversity and evolutionary history of this species.


      Our mitochondrial phylogenetic analysis identified three major lineages (lineages A, B, and C). Lineages B and C showed a relatively narrower geographic distribution, whereas lineage A was widely distributed in numerous drainages. Divergence dates suggested that H. leucisculus populations diverged between 1.61–2.38 Ma. Bayesian species delimitation analysis using 12 nuclear DNA loci indicated the three lineages probably represented three valid taxa. Isolation-with-migration (IM) analysis found substantial gene flow has occurred among the three lineages. Demographic analyses showed that lineages B and C have experienced rapid demographic expansion at 0.03 Ma and 0.08 Ma, respectively.


      Hemiculter leucisculus populations in drainages in southern China comprise three mtDNA lineages, and each of which may represent a separate species. Intense uplift of the Qinghai–Tibetan Plateau, evolution of Asian monsoons, changes in paleo-drainages, and poor dispersal ability may have driven the divergence of the three putative species. However, gene flow occurs among the three lineages. Climatic fluctuations have a prominent impact on the populations from the lineages B and C, but exerted little influence on the lineage A.

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      Most cited references 109

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      MUSCLE: multiple sequence alignment with high accuracy and high throughput.

       Robert Edgar (2004)
      We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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        MEGA6: Molecular Evolutionary Genetics Analysis version 6.0.

        We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from free of charge.
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          RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models.

          RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively.

            Author and article information

            [1 ]ISNI 0000 0004 1792 6029, GRID grid.429211.d, The Key Laboratory of Aquatic Biodiversity and Conservation of Chinese Academy of Sciences, , Institute of Hydrobiology, Chinese Academy of Sciences, ; Wuhan, Hubei 430072 China
            [2 ]ISNI 0000 0004 1797 8419, GRID grid.410726.6, University of Chinese Academy of Sciences, ; Beijing, 100049 People’s Republic of China
            BMC Evol Biol
            BMC Evol. Biol
            BMC Evolutionary Biology
            BioMed Central (London )
            12 September 2017
            12 September 2017
            : 17
            28899345 5596851 1058 10.1186/s12862-017-1058-0
            © The Author(s). 2017

            Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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 ( applies to the data made available in this article, unless otherwise stated.

            Funded by: the Key Fund and NSFC-Yunnan mutual funds of the National Natural Science Foundation of China
            Award ID: Nos. 31130049 and U1036603
            Award Recipient :
            Funded by: Pilot projects
            Award ID: Nos. XDB13020100
            Award Recipient :
            Research Article
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            © The Author(s) 2017


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