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      Morphology, phylogeny and host specificity of two new Ophiocordyceps species belonging to the “zombie-ant fungi” clade (Ophiocordycipitaceae, Hypocreales)

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          Abstract

          Species of the genus Ophiocordyceps , which include species able to manipulate the behaviour of ants, are known as the “zombie-ant fungi” and have attracted much attention over the last decade. They are widespread within tropical, subtropical and even temperate forests worldwide, with relatively few reports from subtropical monsoon evergreen broad-leaved forest. Fungal specimens have been collected from China, occurring on ants and producing hirsutella-like anamorphs. Based on a combination of morphological characters, phylogenetic analyses (LSU, SSU, TEF1a, RPB1 and RPB2) and ecological data, two new species, Ophiocordyceps tortuosa and O. ansiformis , are identified and proposed herein. Ophiocordyceps tortuosa and O. ansiformis are recorded on the same species of Colobopsis ant, based on phylogenetic analyses ( COI), which may be sharing the same host. Ophiocordyceps tortuosa and O. ansiformis share the morphological character of producing lanceolate ascospores. They have typical characteristics distinguished from other species. The ascospore of O. tortuosa are tortuously arranged in the ascus and the ascospore of O. ansiformis have a structure like a handle-shape in the middle. Our molecular data also indicate that O. tortuosa and O. ansiformis are clearly distinct from other species.

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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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            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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              ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates

              Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates. The improvement is achieved by incorporating a model of rate-heterogeneity across sites not previously considered in this context, and by allowing concurrent searches of model-space and tree-space.
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                Author and article information

                Contributors
                Role: Writing - original draft
                Role: Methodology
                Role: Investigation
                Role: Investigation
                Role: Investigation
                Role: Investigation
                Role: Funding acquisition
                Journal
                MycoKeys
                MycoKeys
                11
                urn:lsid:arphahub.com:pub:C004A564-9D6A-5F9F-B058-6A3815DFE9C3
                urn:lsid:zoobank.org:pub:A4FD9303-3F01-45D9-B438-FCCFE465D2F7
                MycoKeys
                Pensoft Publishers
                1314-4057
                1314-4049
                2023
                16 October 2023
                : 99
                : 269-296
                Affiliations
                [1 ] Yunnan Herbal Laboratory, College of Ecology and Environmental Sciences, Yunnan University, Kunming, 650504, China Yunnan University Kunming China
                [2 ] School of Life Science, Yunnan University, Kunming, 650504, China Yunnan University Kunming China
                Author notes
                Corresponding author: Hong Yu ( hongyu@ 123456ynu.edu.cn ; herbfish@ 123456163.com)

                Academic editor: Marc Stadler

                Author information
                https://orcid.org/0000-0002-7662-224X
                https://orcid.org/0000-0001-7871-2209
                https://orcid.org/0009-0008-8119-1975
                https://orcid.org/0000-0001-9022-3635
                https://orcid.org/0000-0001-7837-2101
                https://orcid.org/0000-0003-4118-3694
                https://orcid.org/0000-0002-2149-5714
                Article
                107565
                10.3897/mycokeys.99.107565
                10594121
                37881189
                a24f01d9-e14e-4790-a223-6c3e0889c6f2
                Dexiang Tang, Jing Zhao, Yingling Lu, Zhiqin Wang, Tao Sun, Zuoheng Liu, Hong Yu

                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.

                History
                : 06 June 2023
                : 15 September 2023
                Categories
                Research Article
                Hypocreales
                Ophiocordycipitaceae
                Sordariomycetes
                Molecular Systematics
                Taxonomy
                Asia
                Central Asia
                China

                colobopsis ,entomopathogenic fungi, ophiocordyceps ,taxonomy

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