Two new hybrids of the genus  Diplazium  ( Athyriaceae ) from Japan

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Abstract

In this study, we describe the ferns Diplazium × kanayamaense hyb. nov. and D. × tsukushiense hyb. nov. and further compare them to parental species D. chinense , D. deciduum and D. fauriei in terms of morphological characteristics, plastids and nuclear DNA markers. These new hybrids have been determined to be endemic to western Japan. The International Union for Conservation of Nature and Natural Resources status was evaluated for D. × kanayamaense as endangered (EN) and D. × tsukushiense as critically endangered (CR).

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Most cited references 21

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MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

Sudhir Kumar, Glen Stecher, Michael KaWah Li … (2018)

The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.

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

R. C. 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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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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Author and article information

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Kiyotaka Hori:

ORCID: https://orcid.org/0000-0002-6815-7595

Journal

Journal ID (nlm-ta): PhytoKeys

Journal ID (iso-abbrev): PhytoKeys

Journal ID (publisher-id): 3

Journal ID (index): urn:lsid:arphahub.com:pub:F7FCE910-8E78-573F-9C77-7788555F8AAD

Title: PhytoKeys

Publisher: Pensoft Publishers

ISSN (Print): 1314-2011

ISSN (Electronic): 1314-2003

Publication date Collection: 2021

Publication date (Electronic): 01 February 2021

Volume: 172

Pages: 39-55

Affiliations

[1 ] The Kochi Prefectural Makino Botanical Garden 4200-6 Godaisan, Kochi 781-8125, Japan The Kochi Prefectural Makino Botanical Garden Kochi Japan

[2 ] IDEA Consultants, Inc., 1-5-12 Higashihama, Higashi-ku, Fukuoka City, Fukuoka 812-0055, Japan IDEA Consultants, Inc. Fukuoka Japan

Author notes

Corresponding author: Kiyotaka Hori ( khori@ 123456makino.or.jp )

Academic editor: T. Almeida

Author information

Kiyotaka Hori https://orcid.org/0000-0002-6815-7595

Article

Publisher ID: 60660

DOI: 10.3897/phytokeys.172.60660

PMC ID: 7870639

SO-VID: 455c3693-ea6d-434b-91ca-51e1807bebcb

License:

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.

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Two new hybrids of the genus Diplazium (Athyriaceae) from Japan

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Pensoft Biodiversity

Most cited references 21

MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

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

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

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