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      Molecular identification and larval morphology of spionid polychaetes (Annelida, Spionidae) from northeastern Japan

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          Abstract

          Planktonic larvae of spionid polychaetes are among the most common and abundant group in coastal meroplankton worldwide. The present study reports the morphology of spionid larvae collected mainly from coastal waters of northeastern Japan that were identified by the comparison of adult and larval 18S and 16S rRNA gene sequences. The molecular analysis effectively discriminated the species. Adult sequences of 48 species from 14 genera ( Aonides Claparède, 1864; Boccardia Carazzi, 1893; Boccardiella Blake & Kudenov, 1978; Dipolydora Verrill, 1881; Laonice Malmgren, 1867; Malacoceros Quatrefages, 1843; Paraprionospio Caullery, 1914; Polydora Bosc, 1802; Prionospio Malmgren, 1867; Pseudopolydora Czerniavsky, 1881; Rhynchospio Hartman, 1936; Scolelepis Blainville, 1828; Spio Fabricius, 1785; Spiophanes Grube, 1860) and larval sequences of 41 species from 14 genera ( Aonides ; Boccardia ; Boccardiella ; Dipolydora ; Laonice ; Paraprionospio ; Poecilochaetus Claparède in Ehlers, 1875; Polydora ; Prionospio ; Pseudopolydora ; Rhynchospio ; Scolelepis ; Spio ; Spiophanes ) of spionid polychaetes were obtained; sequences of 27 of these species matched between adults and larvae. Morphology of the larvae was generally species‐specific, and larvae from the same genus mostly shared morphological features, with some exceptions. Color and number of eyes, overall body shape, and type and arrangement of pigmentation are the most obvious differences between genera or species. The morphological information on spionid larvae provided in this study contributes to species or genus level larval identification of this taxon in the studied area. Identification keys to genera and species of planktonic spionid larvae in northeastern Japan are provided. The preliminary results of the molecular phylogeny of the family Spionidae using 18S and 16S rRNA gene regions are also provided.

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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
                Journal
                Zookeys
                Zookeys
                2
                urn:lsid:arphahub.com:pub:45048D35-BB1D-5CE8-9668-537E44BD4C7E
                urn:lsid:zoobank.org:pub:91BD42D4-90F1-4B45-9350-EEF175B1727A
                ZooKeys
                Pensoft Publishers
                1313-2989
                1313-2970
                2021
                04 February 2021
                : 1015
                : 1-86
                Affiliations
                [1 ] Department of Biology, Center for Liberal Arts & Sciences, Iwate Medical University, Idaidori 1‐1‐1, Yahaba‐cho, Shiwa‐gun, Iwate 028‐3694, Japan Iwate Medical University Yahaba Japan
                [2 ] Laboratory of Biological Oceanography, Graduate School of Agricultural Science, Tohoku University, Aramaki‐Aza‐Aoba 468‐1, Aoba‐ku, Sendai 980‐8572, Japan Tohoku University Sendai Japan
                Author notes

                Academic editor: G. Rouse

                Author information
                https://orcid.org/0000-0002-7753-9368
                Article
                54387
                10.3897/zookeys.1015.54387
                7878468
                12b1432d-a6be-46e6-a310-1bcf5a688b5e
                Hirokazu Abe, Waka Sato‐Okoshi

                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
                : 21 May 2020
                : 29 December 2020
                Funding
                This study was partly supported by the research grant from Research Institute of Marine Invertebrates (No. 2011 IKU‐1), Rishiri Research Project (2017), the Japanese Association for Marine Biology (JAMBIO) as a joint‐research project (No. 27‐56), and JSPS KAKENHI (Grant Number: JP21580216, JP15K07540, JP18K05777, JP19K15899).
                Categories
                Research Article
                Spionidae
                Identification Key
                Molecular Systematics
                Taxonomy
                Cenozoic
                Japan

                Animal science & Zoology
                larval identification,meroplankton,molecular identification,phylogeny,planktonic larvae,16s rrna,18s rrna

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