Data_Sheet_1_Studying Ecosystems With DNA Metabarcoding: Lessons From Biomonitoring of Aquatic Macroinvertebrates.PDF

An ongoing challenge for ecological studies has been the collection of data with high precision and accuracy at a suitable scale to detect and manage critical global change processes. A major hurdle has been the time-consuming and challenging process of sorting and identification of organisms, but the rapid development of DNA metabarcoding as a biodiversity observation tool provides a potential solution. As high-throughput sequencing becomes more rapid and cost-effective, a “big data” revolution is anticipated, based on higher and more accurate taxonomic resolution, more efficient detection, and greater sample processing capacity. These advances have the potential to amplify the power of ecological studies to detect change and diagnose its cause, through a methodology termed “Biomonitoring 2.0.” Despite its promise, the unfamiliar terminology and pace of development in high-throughput sequencing technologies has contributed to a growing concern that an unproven technology is supplanting tried and tested approaches, lowering trust among potential users, and reducing uptake by ecologists and environmental management practitioners. While it is reasonable to exercise caution, we argue that any criticism of new methods must also acknowledge the shortcomings and lower capacity of current observation methods. Broader understanding of the statistical properties of metabarcoding data will help ecologists to design, test and review evidence for new hypotheses. We highlight the uncertainties and challenges underlying DNA metabarcoding and traditional methods for compositional analysis, specifically comparing the interpretation of otherwise identical bulk-community samples of freshwater benthic invertebrates. We explore how taxonomic resolution, sample similarity, taxon misidentification, and taxon abundance affect the statistical properties of these samples, but recognize these issues are relevant to applications across all ecosystem types. In conclusion, metabarcoding has the capacity to improve the quality and utility of ecological data, and consequently the quality of new research and efficacy of management responses.

生态学研究长期面临的一项核心挑战，是在适宜尺度下获取高精度、高准确度的数据，以监测并管控关键的全球变化过程。其中一大阻碍是生物样本分拣与物种鉴定流程耗时耗力且难度颇高，但DNA宏条形码（DNA metabarcoding）作为生物多样性观测工具的快速发展，为解决这一难题提供了可行路径。随着高通量测序（high-throughput sequencing）技术愈发高效且成本愈发低廉，基于更高精度、更准确的分类学分辨率、更高效的检测能力与更强的样本处理能力的"大数据"革命已可预期。这些技术进步有望通过被称为"生物监测2.0（Biomonitoring 2.0）"的方法论，强化生态学研究监测变化并解析其成因的能力。尽管该技术前景可观，但高通量测序技术的术语晦涩与发展速度过快，引发了日益加剧的担忧：一项尚未经过充分验证的技术正取代久经考验的传统方法，这降低了潜在用户的信任度，也削弱了生态学家与环境管理从业者对其的采纳意愿。尽管保持审慎态度无可厚非，但我们认为，对新方法的任何批判，也应正视当前观测方法存在的缺陷与能力不足。加深对宏条形码数据统计特性的理解，将有助于生态学家设计、验证并复盘新假说的相关证据。我们着重剖析了DNA宏条形码与传统组成分析方法背后存在的不确定性与挑战，并专门针对完全一致的淡水底栖无脊椎动物批量群落样本的解读结果进行了对比分析。我们探究了分类学分辨率、样本相似度、分类单元误鉴定与分类单元丰度如何影响这些样本的统计特性，同时也意识到这些问题适用于所有生态系统类型的应用场景。综上，宏条形码技术能够提升生态学数据的质量与实用性，进而优化新兴研究的质量与环境管理响应的成效。