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      Zehneria grandibracteata (Cucurbitaceae), an overlooked new species from western Kenyan forests

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          Abstract

          Zehneria grandibracteata , a new species of Cucurbitaceae from western Kenya, is described here, based on morphological and molecular data. It has long been misidentified as the widely-distributed species Z. scabra . However, it differs by its ovate leafy probract at the base of the inflorescences, subglabrous condition of the entire plant, shorter receptacle-tube and filaments, as well as denser and sessile inflorescences. Furthermore, the molecular phylogenetic analysis of Zehneria , based on nrITS sequences, further supports the argument that Z. grandibracteata should be segregated from Z. scabra .

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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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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                Author and article information

                Contributors
                Journal
                PhytoKeys
                PhytoKeys
                3
                urn:lsid:arphahub.com:pub:F7FCE910-8E78-573F-9C77-7788555F8AAD
                PhytoKeys
                Pensoft Publishers
                1314-2011
                1314-2003
                2020
                28 October 2020
                : 165
                : 85-98
                Affiliations
                [1 ] CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, CN-430074, China
                [2 ] Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan, CN-430074, China
                [3 ] University of Chinese Academy of Sciences, Beijing, CN-100049, China
                [4 ] East Africa Herbarium, National Museums of Kenya, P.O. Box 451660-0100, Nairobi, Kenya
                Author notes
                Corresponding author: Guang-Wan Hu ( guangwanhu@ 123456wbgcas.cn )

                Academic editor: N. Holstein

                Author information
                https://orcid.org/0000-0002-0313-3912
                https://orcid.org/0000-0003-1280-7103
                https://orcid.org/0000-0001-5122-1776
                https://orcid.org/0000-0002-3677-6201
                https://orcid.org/0000-0002-2545-8494
                https://orcid.org/0000-0001-7728-7976
                Article
                57399
                10.3897/phytokeys.165.57399
                7642130
                ca93df1e-f0c8-4905-9e46-065ba362e19c
                Neng Wei, Zhi-Xiang Zhong, David Kimutai Melly, Solomon Kipkoech, Benjamin Muema Watuma, Veronicah Mutele Ngumbau, Peris Kamau, Guang-Wan Hu, Qing-Feng 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.

                History
                : 08 August 2020
                : 06 October 2020
                Categories
                Research Article
                Cucurbitaceae
                Nomenclature
                Systematics
                Taxonomy
                Vascular Plants
                Africa
                East Africa
                Kenya

                Plant science & Botany
                east africa,flora of kenya,phylogeny,taxonomy,zehneria scabra
                Plant science & Botany
                east africa, flora of kenya, phylogeny, taxonomy, zehneria scabra

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