Studying Ecosystems With DNA Metabarcoding: Lessons From Biomonitoring of Aquatic Macroinvertebrates

Abstract

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.