Taxonomic Blind Spots: A Limitation of Environmental DNA Metabarcoding-Based Detection for Canadian Freshwater Fishes

Abstract

With increasing utilization of eDNA metabarcoding for fish community assessment, it is critical to identify, address, and communicate its capabilities and limitations. One limitation of great concern is the reliability of taxonomic coverage. Taxonomic blind spots, defined as consistent false negatives for specific taxa despite known presence, reduce corroboration with conventional surveys and can limit the uptake of eDNA metabarcoding for biomonitoring. These blind spots result from gaps in reference sequence libraries, issues with taxonomic resolution, inefficient binding of universal primers to the DNA of certain species, and ineffective collection during the sampling of eDNA. To explore this, a multiproject empirical dataset was compiled and analyzed to evaluate the taxonomic coverage of eDNA metabarcoding for a subset of Canadian freshwater fishes using a standardized workflow for two genetic markers: 12S MiFish-U and Vertebrate COI. The compiled dataset consists of species lists generated by eDNA surveys, paired conventional surveys, and historical records. In total, 59 fish species across 15 families were evaluated of which approximately 40% were unable to be consistently detected by either marker because of a blind spot. The 12S and COI markers also differed in which kinds of blind spots were most frequently observed, with 12S markers exhibiting more reference and resolution blind spots and the COI marker exhibiting more unclassified blind spots. Additionally, in silico primer testing exhibited inconsistent predictions for amplification when using multiple software packages, suggesting the need for further in vitro analysis to troubleshoot primer-related blind spots. This study highlights the impact of these blind spots in taxonomic coverage on eDNA metabarcoding studies of Canadian freshwater fishes. The limitations imposed by taxonomic blind spots should be addressed in future optimization efforts as eDNA metabarcoding sees broader acceptance as an applied method for fish biomonitoring.