Skip to main content

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

Diana, Alex, Matechou, Eleni, Griffin, Jim E., Buxton, Andrew S., Griffiths, Richard A. (2021) An RShiny app for modelling environmental DNA data: accounting for false positive and false negative observation error. Ecography, 44 . pp. 1838-1844. ISSN 0906-7590. (doi:10.1111/ecog.05718) (KAR id:91832)

PDF Publisher pdf
Language: English
Download (929kB) Preview
[thumbnail of ecog.05718.pdf]
Preview
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL:
https://onlinelibrary.wiley.com/doi/10.1111/ecog.0...

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.

Item Type: Article
DOI/Identification number: 10.1111/ecog.05718
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Funders: [239] NERC
Depositing User: Eleni Matechou
Date Deposited: 01 Dec 2021 06:27 UTC
Last Modified: 02 Dec 2021 23:00 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/91832 (The current URI for this page, for reference purposes)
Matechou, Eleni: https://orcid.org/0000-0003-3626-844X
Griffin, Jim E.: https://orcid.org/0000-0002-4828-7368
Buxton, Andrew S.: https://orcid.org/0000-0002-0555-2491
Griffiths, Richard A.: https://orcid.org/0000-0002-5533-1013
  • Depositors only (login required):

Downloads

Downloads per month over past year