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      Marine catfishes (Ariidae—Siluriformes) from the Coastal Amazon: mitochondrial DNA barcode for a recent diversification group?

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          Abstract

          Background

          Ariidae species play a significant role as fishing resources in the Amazon region. However, the family’s systematic classification is notably challenging, particularly regarding species delimitation within certain genera. This difficulty arises from pronounced morphological similarities among species, posing obstacles to accurate species recognition.

          Methods

          Following morphological identification, mitochondrial markers (COI and Cytb) were employed to assess the diversity of Ariidae species in the Amazon.

          Results

          Our sampling efforts yielded 12 species, representing 92% of the coastal Amazon region’s diversity. Morphological identification findings were largely corroborated by molecular data, particularly for species within the Sciades and Bagre genera. Nonetheless, despite morphological support, Cathorops agassizii and Cathorops spixii displayed minimal genetic divergence (0.010). Similarly, Notarius quadriscutis and Notarius phrygiatus formed a single clade with no genetic divergence, indicating mitochondrial introgression. For the majority of taxa examined, both COI and Cytb demonstrated efficacy as DNA barcodes, with Cytb exhibiting greater polymorphism and resolution. Consequently, the molecular tools utilized proved highly effective for species discrimination and identification.

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Lam-Tung Nguyen, Heiko Schmidt, Arndt von Haeseler … (2014)
          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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            MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

            Fredrik Ronquist, Maxim Teslenko, Paul van der Mark … (2012)
            Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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              MEGA11: Molecular Evolutionary Genetics Analysis Version 11

              Koichiro Tamura, Glen Stecher, Sudhir Kumar (2021)
              The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .
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                Author and article information

                Contributors
                Grazielle Evangelista-Gomes
                Journal
                Journal ID (nlm-ta): PeerJ
                Journal ID (iso-abbrev): PeerJ
                Journal ID (publisher-id): PeerJ
                Title: PeerJ
                Publisher: PeerJ Inc. (San Diego, USA )
                ISSN (Electronic): 2167-8359
                Publication date (Electronic): 28 August 2024
                Publication date Collection: 2024
                Volume: 12
                Electronic Location Identifier: e17581
                Affiliations
                [1 ]Laboratório de Genética Aplicada (LAGA), Instituto de Estudos Costeiros (IECOS), Universidade Federal do Pará , Bragança, Brazil
                [2 ]Divisão de Agricultura, Instituto Superior Politécnico de Gaza , Chókwè, Mozambique
                [3 ]Laboratório de Evolução (LEVO), Instituto de Estudos Costeiros (IECOS), Universidade Federal do Pará , Bragança, Brazil
                Author information
                http://orcid.org/0000-0002-3465-3830
                Article
                Publisher ID: 17581
                DOI: 10.7717/peerj.17581
                PMC ID: 11365480
                PubMed ID: 39221281
                SO-VID: 2d8a375f-f4f4-4832-814c-45555cbb82f1
                Copyright © © 2024 Lutz et al.
                License:

                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
                Date received : 9 February 2024
                Date accepted : 25 May 2024
                Funding
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
                Award ID: 439113/2018-0
                Funded by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
                Award ID: 88887.494974/2020-00
                Funded by: Programa de Apoio à Publicação Qualificada (PAPQ)/Universidade Federal do Pará (UFPA)
                This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), under the Universal Call (process: 439113/2018-0) and from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), which provided a doctoral scholarship (88887.494974/2020-00) for Ítalo Lutz. The APC was covered by the Programa de Apoio à Publicação Qualificada (PAPQ)/Universidade Federal do Pará (UFPA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Aquaculture, Fisheries and Fish Science
                Subject: Genetics
                Subject: Marine Biology
                Subject: Molecular Biology
                Subject: Zoology

                Keywords: amazon coast,ariidae,coi,cytb,mtdna
                Data availability:
                Keywords: amazon coast, ariidae, coi, cytb, mtdna

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