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      Multiple species delimitation approaches with COI barcodes poorly fit each other and morphospecies – An integrative taxonomy case of Sri Lankan Sericini chafers (Coleoptera: Scarabaeidae)

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          Abstract

          DNA taxonomy including barcoding and metabarcoding is widely used to explore the diversity in biodiversity hotspots. In most of these hotspot areas, chafers are represented by a multitude of species, which are well defined by the complex shape of male genitalia. Here, we explore how well COI barcode data reflect morphological species entities and thus their usability for accelerated species inventorization. We conducted dedicated field surveys in Sri Lanka to collect the species‐rich and highly endemic Sericini chafers (Coleoptera: Scarabaeidae). Congruence among results of a series of protocols for de novo species delimitation and with morphology‐based species identifications was investigated. Different delimitation methods, such as the Poisson tree processes (PTP) model, Statistical Parsimony Analysis (TCS), Automatic Barcode Gap Discovery (ABGD), Assemble Species by Automatic Partitioning (ASAP), and Barcode Index Number (BIN) assignments, resulted in different numbers of molecular operational taxonomic units (MOTUs). All methods showed both over‐splitting and lumping of morphologically identified species. Only 18 of the observed 45 morphospecies perfectly matched MOTUs from all methods. The congruence of delimitation between MOTUs and morphospecies expressed by the match ratio was low, ranging from 0.57 to 0.67. TCS and multirate PTP (mPTP) showed the highest match ratio, while (BIN) assignment resulted in the lowest match ratio and most splitting events. mPTP lumped more species than any other method. Principal coordinate analysis (PCoA) on a match ratio‐based distance matrix revealed incongruent outcomes of multiple DNA delimitation methods, although applied to the same data. Our results confirm that COI barcode data alone are unlikely to correctly delimit all species, in particular, when using only a single delimitation approach. We encourage the integration of various approaches and data, particularly morphology, to validate species boundaries.

          Abstract

          How well COI barcode data reflects species entities of morphologically well‐identified Sericini chafers as the study group in a diverse tropical hotspot. The study inferred the maximum likelihood trees represent the first molecular phylogenetic hypotheses for Sri Lankan Sericini.

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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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            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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              New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

              PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
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                Author and article information

                Contributors
                lakmalisanky@gmail.com
                ahrens.dirk_col@gmx.de
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                19 May 2022
                May 2022
                : 12
                : 5 ( doiID: 10.1002/ece3.v12.5 )
                : e8942
                Affiliations
                [ 1 ] Zoological Research Museum A. Koenig Leibniz Institute for the Analysis of Biodiversity Change (LIB) Bonn Germany
                [ 2 ] University of Salzburg Salzburg Austria
                [ 3 ] National Institute of Fundamental Studies Kandy Sri Lanka
                Author notes
                [*] [* ] Correspondence

                Uda Gedara Sasanka Lakmali Ranasinghe and Dirk Ahrens, Zoological Research Museum A. Koenig, Leibniz Institute for the Analysis of Biodiversity Change (LIB), Bonn, Germany.

                Emails: lakmalisanky@ 123456gmail.com and ahrens.dirk_col@ 123456gmx.de

                Author information
                https://orcid.org/0000-0001-6455-8079
                https://orcid.org/0000-0003-3524-7153
                Article
                ECE38942
                10.1002/ece3.8942
                9120212
                35600695
                9541f458-0d2f-4a2f-872c-4aa256a4e56e
                © 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 April 2022
                : 13 December 2021
                : 04 May 2022
                Page count
                Figures: 4, Tables: 1, Pages: 15, Words: 12681
                Funding
                Funded by: Alexander Koenig Stiftung
                Funded by: Deutscher Akademischer Austauschdienst , doi 10.13039/501100001655;
                Funded by: Zoological Research Museum A. Koenig, Bonn
                Categories
                Entomology
                Research Article
                Research Articles
                Custom metadata
                2.0
                May 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.6 mode:remove_FC converted:19.05.2022

                Evolutionary Biology
                barcoding,integrative taxonomy,taxonomic match ratio
                Evolutionary Biology
                barcoding, integrative taxonomy, taxonomic match ratio

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