An RShiny app for modelling environmental DNA data: accounting for false positive and false negative observation error

Environmental DNA (eDNA) surveys have become a popular tool for assessing the distribution of species. However, it is known that false positive and false negative observation error can occur at both stages of eDNA surveys, namely the field sampling stage and laboratory analysis stage. We present an RShiny app that implements the Griffin et al. (2020) statistical method, which accounts for false positive and false negative errors in both stages of eDNA surveys that target single species using quantitative PCR methods. Following Griffin et al. (2020), we employ a Bayesian approach and perform efficient Bayesian variable selection to identify important predictors for the probability of species presence as well as the probabilities of observation error at either stage. We demonstrate the RShiny app using a data set on great crested newts collected by Natural England in 2018, and we identify water quality, pond area, fish presence, macrophyte cover and frequency of drying as important predictors for species presence at a site. The state-of-the-art statistical method that we have implemented is the only one that has specifically been developed for the purposes of modelling false negative and false positive observation error in eDNA data. Our RShiny app is user-friendly, requires no prior knowledge of R and fits the models very efficiently. Therefore, it should be part of the tool-kit of any researcher or practitioner who is collecting or analysing eDNA data.

Automated summary

Environmental DNA (eDNA) surveys are a popular tool for assessing species distribution, but false positive and false negative observation errors can occur in both field sampling and laboratory analysis stages. Our RShiny app implements a state-of-the-art statistical method that uses Bayesian variable selection to identify important predictors for species presence and model observation errors in eDNA data. The app is user-friendly and efficient, making it valuable for researchers and practitioners working with eDNA data.