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Integrating invasive species risk assessment into environmental DNA metabarcoding reference libraries.

Andrew R MahonErin K GreyChristopher L Jerde
Published in: Ecological applications : a publication of the Ecological Society of America (2022)
Environmental DNA (eDNA) metabarcoding has shown promise as a tool for estimating biodiversity and early detection of invasive species. In aquatic systems, advantages of this method include the ability to concurrently monitor biodiversity and detect incipient invasions simply through the collection and analysis of water samples. However, depending on the molecular markers chosen for a given study, reference libraries containing target sequences from present species may limit the usefulness of eDNA metabarcoding. To explore the extent of this issue and how it may be resolved to aid biodiversity and invasive species early detection goals, we focus on fishes in the well-studied Laurentian Great Lakes region. First, we provide a synthesis of species currently known from the region and of non-indigenous species identified as threats by international, national, regional, and introduction pathway-specific fish risk assessments. With these species lists, we then evaluate 23 primer pairs commonly used in fish eDNA metabarcoding with available databases of sequence coverage and species specificity. Finally, we identify established and potentially invasive non-indigenous fish that should be prioritized for genetic sequencing to ensure robust eDNA metabarcoding for the region. Our results should increase confidence in using eDNA metabarcoding for fisheries conservation and management in the Great Lakes region and help prioritize reference sequencing efforts. The ultimate utility of eDNA metabarcoding approaches will come when conservation management of existing fish communities is integrated with early detection efforts for invasive species surveillance to assess total fish biodiversity.
Keyphrases
  • risk assessment
  • genetic diversity
  • healthcare
  • public health
  • single molecule
  • machine learning
  • health insurance