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      Hidden in plain sight: two co-occurring cryptic species of Supplanaxis in the Caribbean (Cerithioidea, Planaxidae)

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          Abstract

          The cerithioid Supplanaxis nucleus (Bruguière, 1789) is widespread in the Caribbean, where it lives in often dense aggregates on hard surfaces in the middle-high intertidal. Molecular evidence shows that it comprises two species that are in fact morphologically diagnosable. We fix the nomenclature of Supplanaxis nucleus by designating a sequenced neotype from Bruguière’s historical locality of Barbados, and identify the second, cryptic species as S. nancyae (Petuch, 2013). The two live syntopically across the Caribbean and form a closely related species group with the Panamic S. planicostatus (G.B. Sowerby I, 1825). Planaxis nucleola Mörch, 1876, described from St Croix, in the Virgin Islands, never again recorded in the literature but listed as a synonym of S. nucleus in taxonomic authority lists, is recognized as a valid species of Hinea Gray, 1847. Proplanaxis Thiele, 1929 and Supplanaxis Thiele, 1929, are synonyms and the latter is given precedence over the former.

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          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

           Robert Edgar (2004)
          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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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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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                ZooKeys
                ZK
                Pensoft Publishers
                1313-2970
                1313-2989
                November 11 2020
                November 11 2020
                : 991
                : 85-109
                Article
                10.3897/zookeys.991.57521
                7674383
                33223900
                © 2020

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