eDNA metabarcoding in lakes to quantify influences of landscape features and human activity on aquatic invasive species prevalence and fish community diversity

Aim: Our goal was to use eDNA metabarcoding to characterize fish community diversity, detect aquatic invasive species (AIS), and assess how measures of community (or AIS) diversity are influenced by lake physical and environmental covariates, measures of hydrological connectivity, and human accessibility.
Location: Michigan, USA.
Methods: eDNA samples collected from 22 lakes were sequenced using two mitochondrial gene regions (12S and 16S rRNA). Metabarcoding data were compared to traditional fisheries survey data for a subset of lakes, and data from all 22 lakes were combined with environmental information to identify significant associations with community diversity and AIS relative abundance.
Results: Occupancy modeling indicated that detection probabilities were generally higher with eDNA than traditional fisheries gear. Measures of connectivity with upstream aquatic habitats were positively associated with both AIS relative abundance and fish species diversity. We also demonstrate the use of spatial interpolation methods to map distributions of species diversity and AIS relative abundance within lakes.
Conclusions: eDNA metabarcoding methods provided information on the composition and diversity of fish assemblages and the presence of AIS in freshwater lakes that varied greatly in drainage connectivity and anthropogenic development. Our case study identified associations between environmental covariates and fish diversity or AIS relative abundance across lakes. This information is of particular importance given increasing anthropogenic disturbance, invasive species spread, and associated declines in aquatic biodiversity. Incorporating eDNA metabarcoding as a supplement to traditional fisheries surveys will permit managers to identify greater numbers of taxa, including early detection of AIS, with less field effort and fish mortality. Further, eDNA methods may more accurately identify physical and biological features that correlate with diversity and abundance, and allow agencies to more effectively direct AIS management activities.