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Genomic, ecological, and morphological approaches to investigating species limits: A case study in modern taxonomy from Tropical Eastern Pacific surgeonfishes.

William B LudtMoisés A BernalErica KenworthyE M SalasProsanta Chakrabarty
Published in: Ecology and evolution (2019)
A wide variety of species are distinguished by slight color variations. However, molecular analyses have repeatedly demonstrated that coloration does not always correspond to distinct evolutionary histories between closely related groups, suggesting that this trait is labile and can be misleading for species identification. In the present study, we analyze the evolutionary history of sister species of Prionurus surgeonfishes in the Tropical Eastern Pacific (TEP), which are distinguished by the presence or absence of dark spots on their body. We examined the species limits in this system using comparative specimen-based approaches, a mitochondrial gene (COI), more than 800 nuclear loci (Ultraconserved Elements), and abiotic niche comparisons. The results indicate there is a complete overlap of meristic counts and morphometric measurements between the two species. Further, we detected multiple individuals with intermediate spotting patterns suggesting that coloration is not diagnostic. Mitochondrial data recovered a single main haplotype shared between the species and all locations resulting in a complete lack of structure (ΦST = 0). Genomic analyses also suggest low levels of genetic differentiation (F ST = 0.013), and no alternatively fixed SNPs were detected between the two phenotypes. Furthermore, niche comparisons could not reject niche equivalency or similarity between the species. These results suggest that these two phenotypes are conspecific and widely distributed in the TEP. Here, we recognize Prionurus punctatus Gill 1862 as a junior subjective synonym of P. laticlavius (Valenciennes 1846). The underlying causes of phenotypic variation in this species are unknown. However, this system gives insight into general evolutionary dynamics within the TEP.
Keyphrases
  • genome wide
  • genetic diversity
  • copy number
  • climate change
  • machine learning
  • transcription factor
  • artificial intelligence
  • physical activity
  • deep learning
  • electronic health record