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Stepping into the past to conserve the future: Archived skin swabs from extant and extirpated populations inform genetic management of an endangered amphibian.

Andrew P RothsteinRoland A KnappGideon S BradburdDaniel M BoianoCheryl J BriggsErica Bree Rosenblum
Published in: Molecular ecology (2020)
Moving animals on a landscape through translocations and reintroductions is an important management tool used in the recovery of endangered species, particularly for the maintenance of population genetic diversity and structure. Management of imperiled amphibian species rely heavily on translocations and reintroductions, especially for species that have been brought to the brink of extinction by habitat loss, introduced species, and disease. One striking example of amphibian declines and associated management efforts is in California's Sequoia and Kings Canyon National Parks with the mountain yellow-legged frog species complex (Rana sierrae/muscosa). Mountain yellow-legged frogs have been extirpated from more than 93% of their historic range, and limited knowledge of their population genetics has made long-term conservation planning difficult. To address this, we used 598 archived skin swabs from both extant and extirpated populations across 48 lake basins to generate a robust Illumina-based nuclear amplicon data set. We found that samples grouped into three main genetic clusters, concordant with watershed boundaries. We also found evidence for historical gene flow across watershed boundaries with a north-to-south axis of migration. Finally, our results indicate that genetic diversity is not significantly different between populations with different disease histories. Our study offers specific management recommendations for imperiled mountain yellow-legged frogs and, more broadly, provides a population genetic framework for leveraging minimally invasive samples for the conservation of threatened species.
Keyphrases
  • genetic diversity
  • minimally invasive
  • genome wide
  • copy number
  • healthcare
  • climate change
  • machine learning
  • transcription factor
  • wound healing
  • single cell