Casting a broader net: Using microfluidic metagenomics to capture aquatic biodiversity data from diverse taxonomic targets

Abstract

AbstractEnvironmental DNA (eDNA) assays for single‐ and multi‐species detection show promise for providing standardized assessment methods for diverse taxa, but techniques for evaluating multiple taxonomically divergent assemblages are in their infancy. We evaluated whether microfluidic multiplex metabarcoding on the Fluidigm Access Array™ platform and high‐throughput sequencing could identify diverse stream and riparian assemblages from 48 taxon‐general and taxon‐specific metabarcode primers. eDNA screening was paired with electrofishing along a stream continuum to evaluate congruence between methods. A fish hatchery located midway along the stream continuum provided a dispersal barrier, and a point source for non‐native White Sturgeon (Acipencer transmontanus). Microfluidic metabarcoding had 87% accuracy with respect to electrofishing and detected all 13 species electrofishing observed. Taxon‐specific barcoding primers were more successful than taxon‐general universal metabarcoding primers at classifying sequences to species. Both types of markers detected a transition from downstream sites dominated by multiple fish species, to upstream sites dominated by a single species; however, we failed to detect a complementary transition in amphibian occupancy. White Sturgeon was only detected at the hatchery outflow, indicating eDNA transport was not detectable ~2.4 km from its source. Overall, we identified 878 predicted taxa. Most sequences (50.1%) derived from fish (Actinopteri, Petromyzontidae), oomycetes (21.3%), arthropoda (classes Insecta, Decapoda; 16.5%), and apicomplexan parasites (3.8%). Taxa accounting for ~1% or less of sequences included freshwater red algae, diatoms, amphibians, and beaver. Our work shows that microfluidic metabarcoding can survey multiple phyla per assay, providing fine discrimination required to resolve closely related species, and enable data‐driven prioritization for multiple forest health objectives.