A hierarchical model for eDNA fate and transport dynamics accommodating low concentration samples

Environmental 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 1 km from the release location, traversed in 67.8 minutes.
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.