Environmental DNA (eDNA) sampling is an increasingly important tool for answering ecological questions and informing aquatic species management . Challenges of using eDNA include determining species source location(s) and accurately and precisely measuring low concentration eDNA samples, especially considering inhibitory compounds and multiple sources of ecological and measurement variability. These challenges must be overcome to optimize our use of modeling frameworks like the eDNA Integrating Transport and Hydrology (eDITH) model. To better understand eDNA fate and transport dynamics, our ability to estimate parameters within the eDITH framework, 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 latent variables with a count distribution and conditioning detection and quantification on replicate copy number. We provide evidence that the eDNA removal rate was not constant through time, estimating that over 80% of eDNA was removed over the first 10 m, traversed in 41 seconds. After this initial period of rapid decay, eDNA decayed slowly with consistent detection through our furthest site 1km from the release location, traversed in 250 seconds. We show that the eDITH model parameters can be difficult to estimate in this scenario. Our model further allowed us to detect extra-Poisson variation in the allocation of copies to replicates. Despite not observing evidence for inhibition as typically quantified using internal positive controls in conjunction with a binary decision rule (e.g., $\Delta$Cq>3), we hypothesized this overdispersion could be due to inhibitors. 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 implications, if true. We show that inhibitors can cause substantial underestimation of eDNA site concentration, bias eDITH model parameter estimates, and attribute measurement variability erroneously to ecological variability. While our model is not a panacea for all challenges faced when quantifying low eDNA concentrations, it provides a framework for a more complete accounting of uncertainty that can be further tested and refined.

Funding provided by: United States Geological SurveyCrossref Funder Registry ID: https://ror.org/035a68863Award Number: