Furlan Elise M, Davis Jenny, Duncan Richard P
Institute for Applied Ecology, University of Canberra, Bruce, ACT, Australia.
Research Institute for Environment and Livelihoods, College of Engineering, IT and Environment, Charles Darwin University, Casuarina, NT, Australia.
Mol Ecol Resour. 2020 Sep;20(5):1259-1276. doi: 10.1111/1755-0998.13170. Epub 2020 Jul 13.
Environmental DNA (eDNA) metabarcoding surveys enable rapid, noninvasive identification of taxa from trace samples with wide-ranging applications from characterizing local biodiversity to identifying food-web interactions. However, the technique is prone to error from two major sources: (a) contamination through foreign DNA entering the workflow, and (b) misidentification of DNA within the workflow. Both types of error have the potential to obscure true taxon presence or to increase taxonomic richness by incorrectly identifying taxa as present at sample sites, but multiple error sources can remain unaccounted for in metabarcoding studies. Here, we use data from an eDNA metabarcoding study designed to detect vertebrate species at waterholes in Australia's arid zone to illustrate where and how in the workflow errors can arise, and how to mitigate those errors. We detected the DNA of 36 taxa spanning 34 families, 19 orders and five vertebrate classes in water samples from waterholes, demonstrating the potential for eDNA metabarcoding surveys to provide rapid, noninvasive detection in remote locations, and to widely sample taxonomic diversity from aquatic through to terrestrial taxa. However, we initially identified 152 taxa in the samples, meaning there were many false positive detections. We identified the sources of these errors, allowing us to design a stepwise process to detect and remove error, and provide a template to minimize similar errors that are likely to arise in other metabarcoding studies. Our findings suggest eDNA metabarcoding surveys need to be carefully conducted and screened for errors to ensure their accuracy.
环境DNA(eDNA)宏条形码调查能够从微量样本中快速、非侵入性地识别分类群,具有广泛的应用,从表征当地生物多样性到识别食物网相互作用。然而,该技术容易出现两种主要来源的错误:(a)外来DNA进入工作流程导致的污染,以及(b)工作流程中DNA的错误识别。这两种错误都有可能掩盖真正的分类群存在,或者通过错误地将分类群识别为在样本地点存在而增加分类丰富度,但在宏条形码研究中,多种错误来源可能仍未得到考虑。在这里,我们使用一项eDNA宏条形码研究的数据,该研究旨在检测澳大利亚干旱地区水坑中的脊椎动物物种,以说明在工作流程中错误可能出现在何处以及如何出现,以及如何减轻这些错误。我们在水坑的水样中检测到了36个分类群的DNA,涵盖34个科、19个目和五个脊椎动物纲,这表明eDNA宏条形码调查有潜力在偏远地区提供快速、非侵入性的检测,并广泛采样从水生到陆生分类群的分类多样性。然而,我们最初在样本中识别出了152个分类群,这意味着有许多假阳性检测结果。我们确定了这些错误的来源,从而能够设计一个逐步检测和消除错误的过程,并提供一个模板,以尽量减少其他宏条形码研究中可能出现的类似错误。我们的研究结果表明,eDNA宏条形码调查需要谨慎进行并筛选错误,以确保其准确性。