eDNA metabarcoding to detect aquatic invasive species and estimate community composition in lakes

Session: Application of Genomic Tools to Inform Management of the Great Lakes (5)

Lilian Pukk, Michigan State University, [email protected]
Nicholas Sard, State University of New York, [email protected]
Seth Herbst, Michigan DNR-Fisheries, [email protected]
Jeannette Kanefsky, Michigan State University, [email protected]
Ellen Weise, Michigan State University, [email protected]
Amanda Heathman, Michigan State University, [email protected]
John Robinson, Michigan State University, [email protected]
Kim Scribner, Dept. Fisheries and Wildlife, Michigan State, [email protected]

Abstract

Environmental DNA (eDNA) metabarcoding combines next-generation sequencing methods with eDNA sampling to characterize aquatic communities. These approaches provide a powerful tool for detecting species at low abundance and aquatic invasive species (AIS), facilitating informed management decision making. Here, we investigate the use of eDNA metabarcoding for early AIS detection and multi-trophic community diversity estimation in Michigan’s inland lakes. Altogether, 1,110 eDNA samples from 22 lakes were sequenced at both 12S and 16S rDNA regions, using fish-specific primers. Sequence data were combined with information on the hydrological and physical characteristics of sampled lakes to identify environmental correlates of community diversity and species occupancy. Comparisons between eDNA and traditional fisheries surveys in the same lakes show that most species caught by traditional gear were also detected in eDNA samples, while eDNA metabarcoding detected species (including AIS) in lakes where traditional gears did not. Our results demonstrate the value of combining genetic and environmental information with traditional fisheries gear, especially in surveys targeting AIS and endangered species. Integrating additional metabarcoding markers for aquatic plants and invertebrates provides a more complete description of the aquatic community across trophic levels, leading to better informed management decisions.