A probabilistic model for designing and assessing the performance of eDNA sampling protocols.

Environmental DNA (eDNA) sampling, the detection of species-specific genetic material in water samples, is an emerging tool for monitoring aquatic invasive species. Optimizing eDNA sampling protocols can be challenging because there is imperfect understanding of how each step of the protocol influences its sensitivity. This paper develops a probabilistic model that characterizes each step of an eDNA sampling protocol to evaluate the protocol's overall detection sensitivity for one sample. The model is then applied to analyse how changes over time made to the eDNA sampling protocol to detect bighead (BH) and silver carp (SC) eDNA have influenced its sensitivity, and hence interpretation of the results. The model shows that changes to the protocol have caused the sensitivity of the protocol to fluctuate. A more efficient extraction method in 2013, new species-specific markers with a qPCR assay in 2014, and a more efficient capture method in 2015 have improved the sensitivity, while switching to a larger elution volume in 2013 and a smaller sample volume in 2015 have reduced the sensitivity. Overall, the sensitivity of the current protocol is higher for BH eDNA detection and SC eDNA detection compared to the original protocol used from 2009 to 2012. The paper shows how this model of eDNA sampling can be used to evaluate the effect of proposed changes in an eDNA sampling and analysis protocol on the sensitivity of that protocol to help researchers optimize their design.

Making sense of the noise: The effect of hydrology on silver carp eDNA detection in the Chicago area waterway system.

Environmental DNA (eDNA) sampling is an emerging tool for monitoring the spread of aquatic invasive species. One confounding factor when interpreting eDNA sampling evidence is that eDNA can be present in the water in the absence of living target organisms, originating from excreta, dead tissue, boats, or sewage effluent, etc. In the Chicago Area Waterway System (CAWS), electric fish dispersal barriers were built to prevent non-native Asian carp species from invading Lake Michigan, and yet Asian carp eDNA has been detected above the barriers sporadically since 2009. In this paper the influence of stream flow characteristics in the CAWS on the probability of invasive Asian carp eDNA detection in the CAWS from 2009 to 2012 was examined. In the CAWS, the direction of stream flow is mostly away from Lake Michigan, though there are infrequent reversals in flow direction towards Lake Michigan during dry spells. We find that the flow reversal volume into the Lake has a statistically significant positive relationship with eDNA detection probability, while other covariates, like gage height, precipitation, season, water temperature, dissolved oxygen concentration, pH and chlorophyll concentration do not. This suggests that stream flow direction is highly influential on eDNA detection in the CAWS and should be considered when interpreting eDNA evidence. We also find that the beta-binomial regression model provides a stronger fit for eDNA detection probability compared to a binomial regression model. This paper provides a statistical modeling framework for interpreting eDNA sampling evidence and for evaluating covariates influencing eDNA detection.