Assessment of the diversity and composition of biological communities is central to studies in ecology as well as for ecological monitoring. Historically, individual taxonomic groups have been assessed separately, while for an understanding of the state and change of biodiversity under ongoing global change an integrated assessment would be necessary. DNA metabarcoding has been proposed to be a highly promising approach especially for the assessment of aquatic communities, and numerous studies have investigated the consistency of this new technique with traditional morpho-taxonomic approaches. These individual studies have used DNA metabarcoding to assess diversity and community structure of aquatic organisms both in marine and freshwater systems globally over the last decade. However, a systematic analysis of the comparability and effectiveness of DNA-based community assessment across all of these studies has hitherto been lacking. Here we performed the first meta-analysis of all available studies comparing traditional methods and DNA metabarcoding to measure and assess biological diversity of key aquatic groups, including microorganisms, macroinvertebrates, and fish. Across 215 datasets, we found that DNA metabarcoding provides diversity estimates (richness) that are globally consistent to those obtained using traditional methods. DNA metabarcoding also generates species inventories that are highly congruent with traditional methods for fish. Contrastingly, however, species inventories of microorganisms and macroinvertebrates obtained by DNA metabarcoding showed pronounced differences to traditional methods, missing some taxa but at the same time detecting otherwise overseen diversity. Our results indicate that DNA metabarcoding is efficient to estimate local and regional richness. The method is generally sufficiently advanced to study the composition of fish communities and replace more invasive traditional methods. For smaller organisms, like macroinvertebrates and microorganisms, DNA metabarcoding may continue to give complementary rather than identical estimates compared to traditional approaches. Systematic and comparable data collection will increase the understanding of different aspects of this complementarity, and increase the effectiveness of the method and adequate interpretation of the results.
