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Extremely Low mtDNA Diversity and High Genetic Differentiation Reveal the Precarious Genetic Status of Dugongs in New Caledonia, South Pacific.

Claire GarrigueClaire Daisy BonnevilleChristophe CleguerMarc Oremus
Published in: The Journal of heredity (2022)
New Caledonia is home to one of the largest remaining populations of dugongs (Dugong dugon) and is located at the southeastern limit of the species range. Local knowledge suggests that current levels of removal due to anthropogenic pressures are unsustainable, whereas trends suggest an ongoing decline in the population. Considering this unfavorable conservation context, this study aimed to assess the New Caledonian dugong population's resilience by determining its level of genetic diversity and degree of isolation relative to other populations. Mitochondrial DNA (mtDNA) control region sequences (n = 55) collected from live and dead dugongs in New Caledonia were compared with a global data set of previously published sequences (n = 631) representing dugong populations throughout the species range. The New Caledonian dugong population displayed the lowest level of mtDNA diversity documented worldwide (3 haplotypes with 1-bp difference), suggesting a recent origin of the current population through limited colonization events. Population structure analyses indicate a strong genetic differentiation with all the putative populations represented in the global data set, including large neighboring Australian populations. These results show that the dugong population in New Caledonia is particularly isolated, fragile, and vulnerable to anthropogenic threats and diseases with low potential for resilience through incoming gene flow. Our findings call for an instant conservation response and consideration for IUCN population assessment to support the long-term survival of the New Caledonian dugong population.
Keyphrases
  • genetic diversity
  • mitochondrial dna
  • copy number
  • genome wide
  • healthcare
  • randomized controlled trial
  • electronic health record
  • systematic review
  • machine learning
  • risk assessment
  • big data
  • transcription factor