Centre for Environmental Genomics Applications, eDNAtec Inc., St. John's, NL, Canada.
Department of Integrative Biology, University of Guelph, Guelph, ON, Canada.
PLoS One. 2020 Mar 19;15(3):e0224119. doi: 10.1371/journal.pone.0224119. eCollection 2020.
Environmental DNA (eDNA) metabarcoding is an increasingly popular method for rapid biodiversity assessment. As with any ecological survey, false negatives can arise during sampling and, if unaccounted for, lead to biased results and potentially misdiagnosed environmental assessments. We developed a multi-scale, multi-species occupancy model for the analysis of community biodiversity data resulting from eDNA metabarcoding; this model accounts for imperfect detection and additional sources of environmental and experimental variation. We present methods for model assessment and model comparison and demonstrate how these tools improve the inferential power of eDNA metabarcoding data using a case study in a coastal, marine environment. Using occupancy models to account for factors often overlooked in the analysis of eDNA metabarcoding data will dramatically improve ecological inference, sampling design, and methodologies, empowering practitioners with an approach to wield the high-resolution biodiversity data of next-generation sequencing platforms.
环境 DNA(eDNA) 代谢组学是一种快速评估生物多样性的新兴方法。与任何生态调查一样,在采样过程中可能会出现假阴性,如果不加以考虑,可能会导致结果产生偏差,并可能对环境评估产生误诊。我们开发了一种多尺度、多物种的占有模型,用于分析 eDNA 代谢组学产生的群落生物多样性数据;该模型考虑了不完全检测和额外的环境和实验变化来源。我们介绍了用于模型评估和比较的方法,并展示了如何使用沿海海洋环境中的案例研究来提高 eDNA 代谢组学数据的推断能力。使用占有模型来解释在分析 eDNA 代谢组学数据时经常被忽视的因素,将极大地提高生态推断、采样设计和方法学,使从业人员能够使用下一代测序平台的高分辨率生物多样性数据。