Blog
About

  • Record: found
  • Abstract: found
  • Article: found
Is Open Access

The importance of naming cryptic species and the conservation of endemic subterranean amphipods

Read this article at

Bookmark
      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

      Abstract

      Molecular taxonomy often uncovers cryptic species, reminding us that taxonomic incompleteness is even more severe than previous thought. The importance of cryptic species for conservation is poorly understood. Although some cryptic species may be seriously threatened or otherwise important, they are rarely included in conservation programs as most of them remain undescribed. We analysed the importance of cryptic species in conservation by scrutinizing the South European cryptic complex of the subterranean amphipod Niphargus stygius sensu lato. Using uni- and multilocus delineation methods we show that it consists of 15 parapatric and sympatric species, which we describe using molecular diagnoses. The new species are not mere “taxonomic inflation” as they originate from several distinct branches within the genus and coexist with no evidence of lineage sharing. They are as evolutionarily distinct as average nominal species of the same genus. Ignoring these cryptic species will underestimate the number of subterranean endemics in Slovenia by 12 and in Croatia by four species, although alpha diversity of single caves remains unchanged. The new taxonomy renders national Red Lists largely obsolete, as they list mostly large-ranged species but omit critically endangered single-site endemics. Formal naming of cryptic species is critical for them to be included in conservation policies and faunal listings.

      Related collections

      Most cited references 64

      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

      Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
        Bookmark
        • Record: found
        • Abstract: found
        • Article: not found

        New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

        PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
          Bookmark
          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

          Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
            Bookmark

            Author and article information

            Affiliations
            [1 ]ISNI 0000 0001 0721 6013, GRID grid.8954.0, SubBio lab, Department of Biology, Biotechnical Faculty, , University of Ljubljana, ; Večna pot 111, Ljubljana, 1000 Slovenia
            [2 ]State Nature Conservancy, Slovak Caves Administration, Hodžova 11, 031 01 Liptovský, Mikuláš Slovakia
            Contributors
            cene.fiser@bf.uni-lj.si
            Journal
            Sci Rep
            Sci Rep
            Scientific Reports
            Nature Publishing Group UK (London )
            2045-2322
            13 June 2017
            13 June 2017
            2017
            : 7
            28611400 5469755 2938 10.1038/s41598-017-02938-z
            © The Author(s) 2017

            Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

            Categories
            Article
            Custom metadata
            © The Author(s) 2017

            Uncategorized

            Comments

            Comment on this article