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Identification of fish specimens of the Tocantins River, Brazil, using DNA barcoding.

Renato Corrêia LimaSabrina Rufino de LimaMarcelo Salles RochaHélio Daniel Beltrão Dos AnjosYasmin Catarina Alves DantasIgnácio Davi Navegante BenitesCleonilde da Conceição Silva QueirozElmary da Costa FragaJacqueline da Silva Batista
Published in: Journal of fish biology (2024)
The fish fauna of the Tocantins River possesses many endemic species; however, it is little studied in molecular terms and is quite threatened by the construction of several hydroelectric dams. Therefore, the objective of this study was to identify the ichthyofauna of the Tocantins River using DNA barcoding. For this, collections were carried out in five points of this river, which resulted in the capture of 725 individuals from which partial sequences of the cytochrome oxidase subunit I (COI) gene were obtained for genetic analysis. A total of 443 haplotypes were recovered with the mean intraspecific K2P genetic distance of 1.82%. Altogether, 138 species were identified based on morphological criteria, which was a quantity that was much lower than that indicated by the four molecular methods (assemble species by automatic partitioning [ASAP], barcode index number [BIN], generalized mixed Yule coalescent (GMYC), and Bayesian Poisson tree processes [bPTP]) through which 152-157 molecular entities were identified. In all, 41 unique BINs were obtained based on the data generated in the BOLDSystems platform. According to the result indicated by ASAP (species delimitation approach considered the most appropriate in the present study), there was an increase of 17 molecular entities (12.32%), when compared to the number of species identified through their morphological criteria, as it can show cryptic diversity, candidates for new species, and misidentifications. There were 21 incongruities indicated between the different identification approaches for species. Therefore, it is suggested that these taxonomic problems be cautiously evaluated by experts to solve such taxonomic issues.
Keyphrases
  • single molecule
  • genetic diversity
  • machine learning
  • genome wide
  • water quality
  • gene expression
  • big data