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      The importance of molecular characters when morphological variability hinders diagnosability: systematics of the moon jellyfish genus Aurelia (Cnidaria: Scyphozoa)

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          Abstract

          Cryptic species have been detected across Metazoa, and while no apparent morphological features distinguish them, it should not impede taxonomists from formal descriptions. We accepted this challenge for the jellyfish genus Aurelia, which has a long and confusing taxonomic history. We demonstrate that morphological variability in Aurelia medusae overlaps across very distant geographic localities. Even though some morphological features seem responsible for most of the variation, regional geographic patterns of dissimilarities are lacking. This is further emphasized by morphological differences found when comparing lab-cultured Aurelia coerulea medusae with the diagnostic features in its recent redescription. Previous studies have also highlighted the difficulties in distinguishing Aurelia polyps and ephyrae, and their morphological plasticity. Therefore, mostly based on genetic data, we recognize 28 species of Aurelia, of which seven were already described, 10 are formally described herein, four are resurrected and seven remain undescribed. We present diagnostic genetic characters for all species and designate type materials for newly described and some resurrected species. Recognizing moon jellyfish diversity with formal names is vital for conservation efforts and other studies. This work clarifies the practical implications of molecular genetic data as diagnostic characters, and sheds light on the patterns and processes that generate crypsis.

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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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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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ Inc. (San Diego, USA )
                2167-8359
                9 September 2021
                2021
                : 9
                : e11954
                Affiliations
                [1 ]Departamento de Zoologia, Instituto de Biociências, Universidade de São Paulo , São Paulo, São Paulo, Brazil
                [2 ]School of Environment and Science, Coastal and Marine Research Centre, Australian Rivers Institute, Griffith University , Gold Coast, Queensland, Australia
                [3 ]Instituto Nacional de Investigación y Desarrollo Pesquero , Mar del Plata, Buenos Aires, Argentina
                [4 ]Departamento de Ciências Biológicas, Faculdade de Ciências e Letras, Universidade Estadual Paulista , Assis, São Paulo, Brazil
                [5 ]College of Fisheries and Ocean Sciences, University of Alaska—Fairbanks , Fairbanks, Alaska, United States
                [6 ]National Systematics Laboratory of the National Oceanic and Atmospheric Administration Fisheries Service, National Museum of Natural History, Smithsonian Institution , Washington, District of Columbia, United States
                [7 ]Centro de Biologia Marinha, Universidade de São Paulo , São Sebastião, São Paulo, Brazil
                Author information
                http://orcid.org/0000-0003-1267-5294
                http://orcid.org/0000-0003-4322-7339
                http://orcid.org/0000-0002-2590-639X
                http://orcid.org/0000-0001-5021-6000
                http://orcid.org/0000-0002-3664-9691
                http://orcid.org/0000-0003-3747-8748
                Article
                11954
                10.7717/peerj.11954
                8435205
                34589293
                539e4158-e07b-450b-8380-c7d29069fab9
                © 2021 Lawley et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 3 May 2021
                : 21 July 2021
                Funding
                Funded by: Fundação de Amparo à Pesquisa do Estado de São Paulo
                Award ID: 2015/21007-9, 2016/04560-9, 2016/12163-0, 2017/07317-0, 2019/03552-0
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico
                Award ID: 133900/2016-9, 309440/2019-0
                Funded by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
                Award ID: 238.273.628-30
                This work was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (2015/21007-9, 2016/04560-9, 2016/12163-0, 2017/07317-0, 2019/03552-0), Conselho Nacional de Desenvolvimento Científico e Tecnológico (133900/2016-9, 309440/2019-0) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (238.273.628-30). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Biodiversity
                Marine Biology
                Molecular Biology
                Taxonomy
                Zoology

                cryptic species,diagnosis,dna barcoding,medusa,phylogeny,species delimitation,species description,synapomorphy,taxonomy

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