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      Phylogeny and Taxonomy on Cryptic Species of Forked Ferns of Asia

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          Abstract

          Cryptic species comprise two or more taxa that are grounded under a single name because they are more-or-less indistinguishable morphologically. These species are potentially important for detailed assessments of biodiversity, but there now appear to be many more cryptic species than previously estimated. One taxonomic group likely to contain many cryptic species is Dicranopteris, a genus of forked ferns that occurs commonly along roadsides in Asia. The genus has a complex taxonomical history, and D. linearis has been particularly challenging with many intra-specific taxa dubiously erected to accommodate morphological variation that lacks clear discontinuities. To resolve species boundaries within Dicranopteris, we applied a molecular phylogenetic approach as complementary to morphology. Specifically, we used five chloroplast gene regions ( rbcL, atpB, rps4, matK, and trnL-trnF) to generate a well-resolved phylogeny based on 37 samples representing 13 taxa of Dicranopteris, spanning the major distributional area in Asia. The results showed that Dicranopteris consists of ten highly supported clades, and D. linearis is polyphyletic, suggesting cryptic diversity within the species. Further through morphological comparison, we certainly erected Dicranopteris austrosinensis Y.H. Yan & Z.Y. Wei sp. nov. and Dicranopteris baliensis Y.H. Yan & Z.Y. Wei sp. nov. as distinct species and proposed five new combinations. We also inferred that the extant diversity of the genus Dicranopteris may result from relatively recent diversification in the Miocene based on divergence time dating. Overall, our study not only provided additional insights on the Gleicheniaceae tree of life, but also served as a case of integrating molecular and morphological approaches to elucidate cryptic diversity in taxonomically difficult groups.

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            MUSCLE: multiple sequence alignment with high accuracy and high throughput.

            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

              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

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                17 December 2021
                2021
                : 12
                : 748562
                Affiliations
                [1] 1Key Laboratory of National Forestry and Grassland Administration for Orchid Conservation and Utilization, The National Orchid Conservation Center of China and The Orchid Conservation and Research Center of Shenzhen , Shenzhen, China
                [2] 2Eastern China Conservation Centre for Wild Endangered Plant Resources, Shanghai Chenshan Botanical Garden , Shanghai, China
                [3] 3College of Life Sciences, Shanghai Normal University , Shanghai, China
                [4] 4CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences , Shanghai, China
                [5] 5Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences , Guangzhou, China
                [6] 6College of Science and Engineering, James Cook University , Cairns, QLD, Australia
                [7] 7Xiangxi Tujia and Miao Autonomous Prefecture Forest Resources Monitoring Center , Jishou, China
                [8] 8Research Center for Plants Conservation and Botanic Gardens, National Research and Innovation Agency of Indonesia , Bali, Indonesia
                Author notes

                Edited by: Alexandre Salino, Federal University of Minas Gerais, Brazil

                Reviewed by: Michael Kessler, University of Zurich, Switzerland; Atsushi Ebihara, National Museum of Nature and Science, Japan

                *Correspondence: Yuehong Yan, yhyan@ 123456sibs.ac.cn

                This article was submitted to Plant Systematics and Evolution, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2021.748562
                8718997
                ec843378-5a90-46c3-80c4-e011786a7fb6
                Copyright © 2021 Wei, Xia, Shu, Shang, Maxwell, Chen, Zhou, Xi, Adjie, Yuan, Cao and Yan.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 28 July 2021
                : 08 November 2021
                Page count
                Figures: 4, Tables: 1, Equations: 0, References: 58, Pages: 12, Words: 8027
                Funding
                Funded by: Strategic Innovation Fund , doi 10.13039/100015403;
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                gleicheniaceae,cryptic diversity,species delimitation,phylogeny,taxonomy,new combination

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