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Mitochondrial divergence suggests unexpected high species diversity in the opsariichthine fishes (Teleostei: Cyprinidae) and the revalidation of Opsariichthys macrolepis.

Xue WangFei LiuDan YuHuanzhang Liu
Published in: Ecology and evolution (2019)
Opsariichthine (sensu Oceanologi Et Limnologia Sinica, 1982, 13, 293-298) is a cyprinid group consisting of five genera and endemic to East Asia. Previous studies suggested that there may be many possible cryptic species in this group, but this has not been confirmed. In this study, using mitochondrial cyt b sequences on 1,388 samples and 739 haplotypes, we showed very high species diversity within this group. The results showed that phylogenetic relationships of the opsariichthine group were as ([Nipponocypris-Parazacco-Candidia] + [Zacco + Opsariichthys]), and there were multiple deep lineages within several species, flagging putative cryptic species. When a 3% genetic distance was used as a threshold for species delimitation, 35 haplogroups were found, nine haplogroups in Candidia-Parazacco-Nipponocypris group, six haplogroups in the Zacco group, and 20 haplogroups in the Opsariichthys group. We consider all of them to be putative until determination of distinct species based on the tree topology, geographic distributions, or a combination of both. In addition, two kinds of species delimitation tools, ABGD and PTP, were applied to construct molecular operational taxonomic units (MOTUs). The ABGD method revealed nine MOTUs in Candidia-Parazacco-Nipponocypris group, two MOTUs in the Zacco group, and 17 MOTUs in the Opsariichthys group. And the PTP method revealed 10 MOTUs in Candidia-Parazacco-Nipponocypris group, 10 MOTUs in the Zacco group, and 29 MOTUs in the Opsariichthys group. Therefore, there should be more species in the opsariichthine group than presently described. Based on the molecular data and morphological characteristics, we proposed Opsariichthys macrolepis as a valid species and described its morphological diagnostic characters.
Keyphrases
  • machine learning
  • dna methylation
  • artificial intelligence