Given the costs associated with traditional taxonomic identification of many aquatic organisms, metabarcoding analyses have gained recognition as potentially powerful tools for early detection of aquatic invasive species. A practical early detection strategy, however, demands balancing detection costs with an acceptable level of non-detection risk. Here we evaluated non-detection risk associated with some standard metabarcoding methods by constructing artificial community samples with known species richness and relative biomass abundance composed of fish tissue from multiple "non-target"� species and spiked with various proportions "target" tissue from a single species not already present in the sample. Our main findings provided convincing experimental evidence that we can detect the genetic signal produced by target species comprising as low as 0.02% - 1% of total sample biomass and demonstrated the lowest limit of detection observed for each target species varied between experiments.
University of Minnesota M.S. thesis. June 2015. Major: Integrated Biosciences. Advisor: John Kelly. 1 computer file (PDF); viii, 87 pages.
Evaluating non-detection risk associated with high-throughput metabarcoding methods for early detection of aquatic invasive species.
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