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      Multiple data revealed two new species of the Asian horned toad Megophrys Kuhl & Van Hasselt, 1822 (Anura, Megophryidae) from the eastern corner of the Himalayas

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          Multiple disciplines can help to discover cryptic species and resolve taxonomic confusions. The Asian horned toad genus Megophrys sensu lato as a diverse group was proposed to contain dozens of cryptic species. Based on molecular phylogenetics, morphology, osteology, and bioacoustics data, the species profiles of Megophrys toads in the eastern corner of Himalayas in Medog County, Tibet Autonomous Region, China was investigated. The results indicated that this small area harbored at least four Megophrys species, i.e., M. medogensis , M. pachyproctus , Megophrys zhoui sp. nov., and Megophrys yeae sp. nov., the latter two being described in this study. Additionally, the mitochondrial DNA trees nested the low-middle-elevation and high-elevation groups of M. medogensis into a monophyletic group, being in discordance with the paraphyletic relationship between them revealed in the nuclear DNA trees. The findings highlighted the underestimated biodiversity in Himalayas, and further indicated that the Megophrys toads here have been probably experienced complicated evolutionary history, for example, introgression between clades or incomplete lineage sorting and niche divergences in microhabitats. Anyway, it is urgent for us to explore the problems because these toads are suffering from increasing threats from human activities and climatic changes.

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          Most cited references 106

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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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 www.megasoftware.net free of charge.
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              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.

                Author and article information

                Pensoft Publishers
                22 October 2020
                : 977
                : 101-161
                [1 ] CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China Chinese Academy of Sciences Chengdu China
                [2 ] Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, Sichuan, China Sichuan University Chengdu China
                [3 ] University of Chinese Academy of Sciences, Beijing 100049, China University of Chinese Academy of Sciences Beijing China
                [4 ] Forestry Survey and Design Research Institute of the Tibet Autonomous Region, Lhasa 850000, China Forestry Survey and Design Research Institute of the Tibet Autonomous Region Lhasa China
                [5 ] Chengdu Survey and Design Research Institute of China Electric Power Construction Group Co., Ltd., Chengdu 610041, China Chengdu Survey and Design Research Institute of China Electric Power Construction Group Co., Ltd. Chengdu China
                Author notes
                Corresponding author: Bin Wang ( wangbin@ 123456cib.ac.cn ); Jianping Jiang ( jiangjp@ 123456cib.ac.cn )

                Academic editor: A. Ohler

                Shengchao Shi, Meihua Zhang, Feng Xie, Jianping Jiang, Wulin Liu, Li Ding, Li Luan, Bin Wang

                This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                This research is supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (Grant No. 2019QZKK05010503), and Project supported by the biodiversity investigation, observation and assessment program (2019–2023) of Ministry of Ecology and Environment of China.
                Research Article
                Far East


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