﻿A new species of the genus Leptobrachella (Anura, Megophryidae) from northwestern Guangdong Province, China

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Abstract

The genus Leptobrachella is a species-rich genus of megophrid frog. Rapid discovery of many new species within this genus emphasizes the importance of regional research. In this study, we describe a new species of Leptobrachella, Leptobrachella verrucosa sp. nov., from northwestern Guangdong Province, China, based on a combination of molecular and morphological data. A key including congeners from the same province, namely L. laui, L. liui, L. mangshanensis, L. shimentaina, and L. yunkaiensis, is provided.

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

Fredrik Ronquist, Maxim Teslenko, Paul van der Mark … (2012)

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.

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

Diego Darriba, Guillermo Taboada, Ramón Doallo … (2012)

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RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models.

Alexandros Stamatakis (2006)

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

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Author and article information

Contributors

Yu-Hong Lu: (View ORCID Profile)

Zhao-Chi Zeng: (View ORCID Profile)

Jian Wang: (View ORCID Profile)

Journal

Title: Herpetozoa

Abbreviated Title: Herpetozoa

Publisher: Pensoft Publishers

ISSN (Electronic): 2682-955X

ISSN (Print): 1013-4425

Publication date Created: September 28 2022

Publication date (Electronic): September 28 2022

Volume: 35

Pages: 165-178

Article

DOI: 10.3897/herpetozoa.35.e89981

SO-VID: eb11e2d4-d04e-4418-bed4-725ef5097e34

License:

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

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