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      Revision of the South American genus Gaujonia Dognin (Noctuidae, Pantheinae) with descriptions of five new genera and twenty-one new species

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          Abstract

          The endemic Neotropical genus Gaujonia Dognin is revised. Morphological characters and a phylogenetic analysis demonstrate paraphyletic relationships among the species. Four different groups are interpreted to represent four different genera. The G. arbosi group is the only remaining clade in the genus Gaujonia, and the other groups have been arranged into three new genera: Millerana gen. nov., Oculicattus gen. nov., and Cicadoforma gen. nov. Additionally, two other genera Cicadomorphus gen. nov., and Gaujoptera gen. nov. were found using morphological and molecular analyses based on some specimens that were misidentified as Gaujonia spp. A total of five new genera, three new combinations (Cicadoforma vau-nigrum Hampson, comb. nov., Oculicattus renifera Hampson, comb. nov., and Millerana arbosioides Dognin, comb. nov.) and 21 new species (Cicadoforma ocelotus sp. nov., Cicadomorphus chicharra sp. nov., Cicadomorphus chuya sp. nov., Cicadomorphus falkasiska sp. nov., Cicadomorphus lilianae sp. nov., Gaujonia bichu sp. nov., Gaujonia chiqyaq sp. nov., Gaujonia kanakusika sp. nov., Gaujonia sourakovi sp. nov., Gaujoptera amsa sp. nov., Millerana austini sp. nov., Millerana cajas sp. nov., Millerana cundinamarquensis sp. nov., Millerana matthewsae sp. nov., Millerana tigrina sp. nov., Oculicattus boliviana sp. nov., Oculicattus brehmi sp. nov., Oculicattus inca sp. nov., Oculicattus raizae sp. nov., Oculicattus schmidti sp. nov., and Oculicattus uturunku sp. nov.) are established.

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          Most cited references 16

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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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            UFBoot2: Improving the Ultrafast Bootstrap Approximation

            Abstract The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased branch supports than SBS under mild model violations. UFBoot has been steadily adopted as an efficient alternative to SBS and other bootstrap approaches. Here, we present UFBoot2, which substantially accelerates UFBoot and reduces the risk of overestimating branch supports due to polytomies or severe model violations. Additionally, UFBoot2 provides suitable bootstrap resampling strategies for phylogenomic data. UFBoot2 is 778 times (median) faster than SBS and 8.4 times (median) faster than RAxML rapid bootstrap on tested data sets. UFBoot2 is implemented in the IQ-TREE software package version 1.6 and freely available at http://www.iqtree.org.
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              IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

              Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                ZooKeys
                ZK
                Pensoft Publishers
                1313-2970
                1313-2989
                November 05 2020
                November 05 2020
                : 985
                : 71-126
                Article
                10.3897/zookeys.985.51622
                7661952
                33223876
                © 2020

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