Reference databases, primer choice, and assay sensitivity for environmental metabarcoding: Lessons learnt from a re‐evaluation of an eDNA fish assessment in the Volga headwaters

Abstract

Biodiversity monitoring via environmental DNA, particularly metabarcoding, is evolving into a powerful assessment tool for riverine systems. However, for metabarcoding to be fully integrated into standardized monitoring programmes, some current challenges concerning sampling design, laboratory workflow, and data analysis need to be overcome. Here, we review some of these major challenges and potential solutions. We further illustrate three potential pitfalls, namely the choice of suitable metabarcoding primers, the necessity of complete reference databases, and varying assay sensitivities, by a reappraisal of our‐own recently carried out metabarcoding study in the Volga headwaters. TaqMan qPCRs had detected catfish (Silurus glanis) and European eel (Anguilla anguilla), whereas metabarcoding had not, in the same samples. Furthermore, after extending the genetic reference database by 12 additional species and re‐analysing the metabarcoding data, we additionally detected the Siberian spiny loach (Cobitis sibirica) and Ukrainian brook lamprey (Eudontomyzon mariae) and reassigned the operational taxonomic units previously assigned to Misgurnus fossilis to Cobitis sibirica. In silico analysis of metabarcoding primer efficiencies revealed considerable variability among primer pairs and among target species, which could lead to strong primer bias and potential false‐negatives in metabarcoding studies if not properly compensated for. These results highlight some of the pitfalls of eDNA‐metabarcoding as a means of monitoring fish biodiversity in large rivers, which need to be considered in order to fully unleash the full potential of these approaches for freshwater biodiversity monitoring.

Harnessing the power of eDNA metabarcoding for the detection of deep-sea fishes

“…The low resolution is due to a combination of low sequence diversity between species (where query sequences matched multiple species in the reference database) and poor reference database coverage (where query sequences did not match any reference sequences at our species-level threshold) [ 52 ]. Other studies comparing primer sets for the detection of fish have found similar taxonomic biases and showed that a lack of reference database coverage negatively affects the resolution of several targeted primer sets [ 53 , 54 ]. This reinforces the importance of marker selection and highlights the need to use multiple markers to maximize detection and taxonomic resolution even within a relatively narrow target group, such as fish.…”

Calibrating Environmental DNA Metabarcoding to Conventional Surveys for Measuring Fish Species Richness

“…Lecaudey et al (2019) detected only 23 of 43 fish species (53%) known to occur in their study area despite using three gene markers (cytb, 12S, 16S) in their analysis of Volga River eDNA samples. In a follow-up study reanalyzing the metabarcoding data, Schenekar et al (2020) revealed that an incomplete reference database led to several false negatives and mis-assigned species. They also highlighted significant differences in primer efficiencies between markers and among species and the associated potential for false negatives.…”

Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes

“…In fact, many species recorded by UVC were not recovered with eDNA simply because they were not represented in the reference database. In order to fully exploit the potential detection power of eDNA metabarcoding, a vast effort is needed to improve taxonomic coverage of reference databases (Schenekar et al., 2020; Weigand et al., 2019). Addressing these important database gaps requires analyses that are not based solely on species assignment.…”