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Seascape genetics of the Atlantic spotted dolphin (Stenella frontalis) based on mitochondrial DNA.

Karina Bohrer do AmaralDalia C Barragán-BarreraRoosevelt A Mesa-GutiérrezNohelia Farías-CurtidorSusana Josefina Caballero GaitánPaula Méndez-FernandezMarcos C Oliveira SantosCaroline RinaldiRenato RinaldiSalvatore SicilianoVidal MartínManuel CarrilloAna Carolina O de MeirellesValentina Franco-TrecuNelson Rosa FagundesIgnacio Benites MorenoL Lacey KnowlesAna Rita Amaral
Published in: The Journal of heredity (2021)
The Atlantic spotted dolphin (Stenella frontalis) is endemic to tropical, subtropical, and warm temperate waters of the Atlantic Ocean. Throughout its distribution, both geographic distance and environmental variation may contribute to population structure of the species. In this study we follow a seascape genetics approach to investigate population differentiation of Atlantic spotted dolphins based on a large worldwide dataset and the relationship with marine environmental variables. The results revealed that the Atlantic spotted dolphin exhibits population genetic structure across its distribution based on mitochondrial DNA control region (mtDNA-CR) data. Analyses based on the contemporary landscape suggested, at both the individual and population-level, that the population genetic structure is consistent with the isolation-by-distance model. However, because geography and environmental matrices were correlated, and because in some, but not all analyses, we found a significant effect for the environment, we cannot rule out the addition contribution of environmental factors in structuring genetic variation. Future analyses based on nuclear data are needed to evaluate whether local processes, such as social structure and some level of philopatry within populations, may be contributing to the associations among genetic structure, geographic, and environmental distance.
Keyphrases
  • mitochondrial dna
  • copy number
  • genome wide
  • human health
  • healthcare
  • dna methylation
  • life cycle
  • single cell
  • electronic health record
  • machine learning
  • big data
  • climate change
  • data analysis
  • genetic diversity