A Hierarchical Model for eDNA Fate and Transport Dynamics Accommodating Low Concentration Samples

Abstract

AbstractEnvironmental DNA (eDNA) sampling is an increasingly important tool for answering ecological questions and informing aquatic species management; however, several factors currently limit the reliability of ecological inference from eDNA sampling. Two particular challenges are 1) determining species source location(s) and 2) accurately and precisely measuring low concentration eDNA samples in the presence of multiple sources of ecological and measurement variability. The recently introduced eDNA Integrating Transport and Hydrology (eDITH) model provides a framework for relating eDNA measurements to source locations in riverine networks, but little empirical work has been done to test and refine model assumptions or accommodate low concentration samples, that can be systematically undermeasured. To better understand eDNA fate and transport dynamics and our ability to reliably quantify low concentration samples, we developed a hierarchical model and used it to evaluate a fate and transport experiment. Our model addresses several low concentration challenges by modeling the number of copies in each PCR replicate as a latent variable with a count distribution and conditioning detection and quantification on replicate copy number. We provide evidence that the eDNA removal rate declined through time, estimating that over 80% of eDNA was removed over the first 10 meters, traversed in 41 seconds. After this initial period of rapid decay, eDNA decayed slowly with consistent detection through our farthest site 1km from the release location, traversed in 250 seconds. Our model further allowed us to detect extra-Poisson variation in the allocation of copies to replicates. We extended our hierarchical model to accommodate a continuous effect of inhibitors and used our model to provide evidence for the inhibitor hypothesis and explore the potential implications. While our model is not a panacea for all challenges faced when quantifying low-concentration eDNA samples, it provides a framework for a more complete accounting of uncertainty.