Ficetola Gentile Francesco, Taberlet Pierre, Coissac Eric
Universite Grenoble-Alpes, Laboratoire d'Ecologie Alpine (LECA), F-38000, Grenoble, France.
Centre National de la Recherche Scientifique, Laboratoire d'Ecologie Alpine (LECA), F-38000, Grenoble, France.
Mol Ecol Resour. 2016 May;16(3):604-7. doi: 10.1111/1755-0998.12508.
Environmental DNA (eDNA) and metabarcoding are boosting our ability to acquire data on species distribution in a variety of ecosystems. Nevertheless, as most of sampling approaches, eDNA is not perfect. It can fail to detect species that are actually present, and even false positives are possible: a species may be apparently detected in areas where it is actually absent. Controlling false positives remains a main challenge for eDNA analyses: in this issue of Molecular Ecology Resources, Lahoz-Monfort et al. () test the performance of multiple statistical modelling approaches to estimate the rate of detection and false positives from eDNA data. Here, we discuss the importance of controlling for false detection from early steps of eDNA analyses (laboratory, bioinformatics), to improve the quality of results and allow an efficient use of the site occupancy-detection modelling (SODM) framework for limiting false presences in eDNA analysis.
环境DNA（eDNA）和宏条形码技术正在提升我们获取各种生态系统中物种分布数据的能力。然而，与大多数采样方法一样，eDNA并不完美。它可能无法检测到实际存在的物种，甚至可能出现假阳性结果：在实际不存在某物种的区域可能会明显检测到该物种。控制假阳性仍然是eDNA分析的一个主要挑战：在本期《分子生态学资源》中，拉霍斯 - 蒙福特等人（）测试了多种统计建模方法的性能，以从eDNA数据中估计检测率和假阳性率。在此，我们讨论在eDNA分析的早期步骤（实验室、生物信息学）中控制假检测的重要性，以提高结果质量，并允许有效使用位点占用 - 检测建模（SODM）框架来限制eDNA分析中的假存在情况。
