Abstract

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.

Highlights

  • Environmental DNA is increasingly used within biodiversity assessments (McClenaghan et al 2020)

  • We present an RShiny app, ‘Environmental DNA (eDNA) 1.0’, for modelling single-species eDNA data resulting from qPCR analysis by implementing the Bayesian model developed by Griffin et al (2020)

  • This is due to the specification of novel informative prior distributions that reflect our belief that the probability of a false positive observation is smaller than the probability of a true positive observation at either stage

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Summary

Citation for published version

Alex, Matechou, Eleni, Griffin, Jim E., Buxton, Andrew S. and Griffiths, Richard A. (2021) An RShiny app for modelling environmental DNA data: accounting for false positive and false negative observation error. Alex, Matechou, Eleni, Griffin, Jim E., Buxton, Andrew S. and Griffiths, Richard A. (2021) An RShiny app for modelling environmental DNA data: accounting for false positive and false negative observation error.

Software notes
Background
Bayesian model
Discussion
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