Finding the Practical Value of Environmental DNA Data: A Case Study with Invasive European Green Crab

Abstract

Advances in environmental DNA (eDNA) applications hold promise for revolutionizing environmental monitoring and management, providing increased detection sensitivity at reduced cost and survey effort. However, implementation of eDNA methods in decision-making contexts lags significantly behind technical progress in research, mainly due to uncertainty in data interpretation and a lack of standard operating procedures for how to best integrate eDNA approaches into existing management efforts. I address these key challenges by developing a Bayesian model that uses heterogeneity in molecular detection probability to interpret patterns of eDNA signals, integrating information from both traditional and eDNA monitoring methods to jointly estimate local species density. Critically, the joint model offers a framework for quantifying the marginal benefit of eDNA data, specifically how the addition of eDNA data improves the precision of species density estimates. I illustrate the approach using environmental DNA information from a marine invasive species, Carcinus maenas (European green crab), across Washington State, USA. Importantly, I document green crab eDNA beyond the previously known invasion front and find exponential increases in the value of environmental DNA data at these locations. In addition, the joint model quantifies uncertainty in estimates of species density and provides the necessary interface for combining molecular and traditional data streams. These results provide managers the analytical framework needed to successfully integrate environmental DNA information into management decisions and suggest that eDNA approaches are best suited for mapping invasion fronts and locations with low species density.