Essential Site Maintenance: Authorea-powered sites will be updated circa 15:00-17:00 Eastern on Tuesday 5 November.
There should be no interruption to normal services, but please contact us at [email protected] in case you face any issues.

loading page

Optimizing a metabarcoding primer portfolio for species-level detection of taxa in complex mixtures of diverse fishes
  • +3
  • Diana Baetscher,
  • Nicolas Locatelli,
  • Eugene Won,
  • Timothy Fitzgerald,
  • Peter McIntyre,
  • Nina Overgaard Therkildsen
Diana Baetscher
Cornell University College of Agriculture and Life Sciences

Corresponding Author:[email protected]

Author Profile
Nicolas Locatelli
Cornell University College of Agriculture and Life Sciences
Author Profile
Eugene Won
Cornell University
Author Profile
Timothy Fitzgerald
Environmental Defense Fund Washington DC
Author Profile
Peter McIntyre
Cornell University College of Agriculture and Life Sciences
Author Profile
Nina Overgaard Therkildsen
Cornell University College of Agriculture and Life Sciences
Author Profile

Abstract

DNA metabarcoding is used to enumerate and identify taxa in both environmental samples and tissue mixtures. The composition and resolution of metabarcoding data depend on the primer(s) used. Markers that amplify different genes can mitigate biases in primer affinity, amplification efficiency, and reference database resolution, but few empirical studies have evaluated markers for complementary performance. Here, we assess the individual and joint performance of 22 markers for detecting species in a DNA pool of >100 species of primarily marine and freshwater fishes, but also including representatives of elasmobranchs, cephalopods, and crustaceans. Marker performance includes the integrated effect of primer specificity and reference availability. We find that a portfolio of four markers targeting 12S, 16S, and multiple regions of COI identifies 100% of reference taxa to family and nearly 60% to species. We then use the four markers in this portfolio to evaluate metabarcoding of heterogeneous tissue mixtures, using experimental fishmeal to test: 1) the tissue input threshold to ensure detection; 2) how read depth scales with tissue abundance; and 3) the effect of non-target material in the mixture on recovery of target taxa. We consistently detect taxa that make up >1% of fishmeal mixtures and can detect taxa at the lowest input level of 0.01%, but rare taxa (<1%) were detected inconsistently across markers and replicates. Read counts showed weak correlation with tissue input, suggesting they are not a valid proxy for relative abundance. Despite this limitation, our results demonstrate the value of a primer portfolio approach—tailored to the taxa of interest—for detecting and identifying both rare and abundant species in heterogeneous tissue mixtures.