11
views
0
recommends
+1 Recommend
1 collections
    0
    shares

      Publish your biodiversity research with us!

      Submit your article here.

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

      A new Leptobrachella species (Anura, Megophryidae) from South China, with comments on the taxonomic status of L. chishuiensis and L. purpurus

      Read this article at

      ScienceOpenPublisher
      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

          A new species of Leaf Litter Toad, Leptobrachella shimentaina sp. nov., is described from the Shimentai and Luokeng nature reserves of northern Guangdong Province, southern China. The new taxon can be distinguished from all recognized congeners by a combination of discrete morphological character state differences relating to its small body size (SVL 26.4–28.9 mm in six adult males, 30.1 and 30.7 mm in two adult females); a number of apparently fixed color pattern character differences (including eye coloration and color pattern features from dorsal, ventral, and dorsolateral surfaces of its head, body, limbs, and ventrum); the morphological and discrete characteristics of the external phenotype (the skin texture of dorsum and ventrum, the presence of supra-axillary and ventrolateral glands, the wide dermal fringes and rudimentary webbing on toes, and the uninterrupted longitudinal ridges under toes). Two samples of this new species previously were proposed as representing a new, unnamed species. We now substantiate this claim by providing diagnostic comparisons of discrete character differences. In addition, we also discuss taxonomic uncertainty surrounding the identity of two congeners, L. chishuiensis and L. purpurus, which we interpret as indicative of taxonomic inflation in the species-rich subfamily Megophryidae.

          Related collections

          Most cited references84

          • 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
            • Record: found
            • Abstract: not found
            • Article: not found

            jModelTest 2: more models, new heuristics and parallel computing.

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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. icwww.epfl.ch/~stamatak
                Bookmark

                Author and article information

                Contributors
                Journal
                Zoosystematics and Evolution
                ZSE
                Pensoft Publishers
                1860-0743
                1435-1935
                June 03 2022
                June 03 2022
                : 98
                : 1
                : 165-180
                Article
                10.3897/zse.98.73162
                f559a315-812b-4d69-b6aa-99881e3cdb46
                © 2022

                http://creativecommons.org/licenses/by/4.0/

                History

                Comments

                Comment on this article