Divergence thresholds and divergent biodiversity estimates: can metabarcoding reliably describe zooplankton communities?

Abstract

DNA metabarcoding is a promising method for describing communities and estimating biodiversity. This approach uses high-throughput sequencing of targeted markers to identify species in a complex sample. By convention, sequences are clustered at a predefined sequence divergence threshold (often 3%) into operational taxonomic units (OTUs) that serve as a proxy for species. However, variable levels of interspecific marker variation across taxonomic groups make clustering sequences from a phylogenetically diverse dataset into OTUs at a uniform threshold problematic. In this study, we use mock zooplankton communities to evaluate the accuracy of species richness estimates when following conventional protocols to cluster hypervariable sequences of the V4 region of the small subunit ribosomal RNA gene (18S) into OTUs. By including individually tagged single specimens and “populations” of various species in our communities, we examine the impact of intra- and interspecific diversity on OTU clustering. Communities consisting of single individuals per species generated a correspondence of 59–84% between OTU number and species richness at a 3% divergence threshold. However, when multiple individuals per species were included, the correspondence between OTU number and species richness dropped to 31–63%. Our results suggest that intraspecific variation in this marker can often exceed 3%, such that a single species does not always correspond to one OTU. We advocate the need to apply group-specific divergence thresholds when analyzing complex and taxonomically diverse communities, but also encourage the development of additional filtering steps that allow identification of artifactual rRNA gene sequences or pseudogenes that may generate spurious OTUs.

Benchmarking DNA Metabarcoding for Biodiversity-Based Monitoring and Assessment

“…Only two studies (Dowle et al, 2015;Elbrecht and Leese, 2015) have performed a robust benchmarking of metabarcoding using freshwater invertebrates and showed that this technique can be successfully applied to biodiversity assessment. In marine metazoans, all studies have focused only on plankton samples (Brown et al, 2015;Mohrbeck et al, 2015;Albaina et al, 2016). Thus, an exhaustive evaluation of metabarcoding for marine benthic metazoan taxonomic inferences is still lacking.…”

Comparison of three clustering approaches for detecting novel environmental microbial diversity

“…On the other hand, these high numbers may be due to missing intraspecific sequence variation in the PR 2 reference database, which usually contains only one accession per species. In natural communities, intraspecific genetic variation of microbial organisms may be much more diverse than just a few base pair differences, especially in hypervariable gene regions (Brown et al, 2015;Decelle et al, 2014;Dunthorn et al, 2012;Pernice et al, 2013 (2014) and Gimmler and Stoeck (2015).…”

Novel Diversity of Deeply Branching Holomycota and Unicellular Holozoans Revealed by Metabarcoding in Middle Paraná River, Argentina

“…These clean reads were then clustered into Operational Taxonomic Units (OTUs) using UPARSE with a 97% similarity threshold, resulting in a total of 4,598 OTUs for V4 and 4,176 OTUs for V8-9 region. Even though there is no clear consensus on the optimal threshold to use in clustering algorithms Brown et al, 2015), we decided to use 97% because it has been widely used (Stoeck et al, 2010;Kammerlander et al, 2015;Massana et al, 2015;Giner et al, 2016;Kim et al, 2016). We are aware that this threshold might group species or even genus into the same OTU.…”