An RShiny app for modelling environmental DNA data: accounting for false positive and false negative observation error
An RShiny app for modelling environmental DNA data: accounting for false positive and false negative observation error
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DOI：
10.1111/ecog.05718
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发表时间：
2021-10-20
期刊：
影响因子：
5.9
通讯作者：
Griffiths, Richard A.
中科院分区：
文献类型：
--
作者：
Diana, Alex;Matechou, Eleni;Griffiths, Richard A.
环境DNA（eDNA）调查已成为评估物种分布的流行工具。然而，已知假阳性和假阴性观测误差可发生在eDNA调查的两个阶段，即现场取样阶段和实验室分析阶段。我们提出了一个RShiny应用程序，该应用程序实现了Griffin et al.（2020）统计方法，该方法在使用定量PCR方法针对单个物种的eDNA调查的两个阶段中都考虑了假阳性和假阴性错误。在Griffin等人（2020）之后，我们采用贝叶斯方法并进行有效的贝叶斯变量选择，以确定物种存在概率的重要预测因子以及任何阶段的观测误差概率。我们使用Natural England在2018年收集的大冠蝾螈数据集演示了RShiny应用程序，并将水质，池塘面积，鱼类存在，大型植物覆盖和干燥频率确定为物种存在的重要预测因素。我们实施的最先进的统计方法是唯一一种专门为模拟eDNA数据中假阴性和假阳性观察误差而开发的方法。我们的RShiny应用程序是用户友好的，不需要R的先验知识，并且非常有效地拟合模型。因此，它应该是任何正在收集或分析eDNA数据的研究人员或从业者的工具包的一部分。
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.