eDNA metabarcoding is an effective method for studying fish communities but allows only an estimation of relative species abundance (density / biomass). Here, we combine metabarcoding with an estimation of the total abundance of eDNA amplified by our universal marker (teleo) using a qPCR approach to infer the absolute abundance of fish species. We carried out a 2,850 km eDNA survey within the Danube catchment using a spatial integrative sampling protocol coupled with traditional electrofishing for fish biomass and density estimation. Total fish eDNA concentrations and total fish abundance were highly correlated. The correlation between eDNA concentrations per taxon and absolute specific abundance was of comparable strength when all sites were pooled and remained significant when the sites were considered separately. Furthermore, a non-linear mixed model showed that species richness was underestimated when the amount of teleo-DNA extracted from a sample was below a threshold of 0.65.106 copies of eDNA. This result, combined with the decrease in teleo-DNA concentration by several orders of magnitude with river size, highlights the need to increase sampling effort in large rivers. Our results show a comprehensive description of longitudinal changes in fish communities and underline our combined metabarcoding/qPCR approach for biomonitoring and bioassessment surveys when a rough estimate of absolute species abundance is sufficient.

The eDNA metabarcoding workflow (extraction, amplification using “teleo” primers, high-throughput sequencing and bioinformatic analysis) was performed following a previously described protocol (Pont et al., 2018). After eDNA extraction, 12 PCR replicates were conducted per sample. Twelve libraries were prepared using the Fasteris MetaFast protocol, and twelve independent paired-end sequencing reactions (2 × 125 bp) were carried out on a MiSeq sequencer (Illumina) with the MiSeq Kit v3 (Illumina) following the manufacturer’s instructions at Fasteris facilities. To monitor possible contaminants, eleven negative extraction controls and seven negative PCR controls (ultrapure water) were amplified with 12 replicates and sequenced in parallel with the samples. Sequence reads were analysed using programs implemented in the OBITools package (Boyer et al., 2016). The forward and reverse reads were assembled with the ILLUMINAPAIREDEND program using a minimum score of 40 and retrieving only joined sequences. Then, we assigned the reads to each sample using NGSFILTER software, and a separate data set was created for each sample by splitting the original data set into several files using OBISPLIT. After this step, we analysed each sample individually before merging the taxon list for the final ecological analysis. Strictly identical sequences were clustered together using OBIUNIQ. Sequences shorter than 20 bp, or with fewer than 10 reads or labelled “internal” by the OBICLEAN program were excluded. The final taxonomic assignment of molecular operational taxonomic units (MOTUs) was performed using the program ECOTAG, with our two reference databases and the sequences extracted from the release 142 (standard sequences) of the ENA database (http://www.ebi.ac.uk/ena). Considering the incorrect assignment of a few sequences to the sample due to tag jumps (Schnell, Bohmann, & Gilbert, 2015), all the sequences with a frequency of occurrence < 0.001 per sequence and per library were discarded. Then, the data were curated for Index-Hopping (MacConaill et al., 2018) with a threshold empirically determined per sequencing batch using experimental blanks (i.e., combinations of tags not present in the libraries) for a given sequencing batch between libraries. The final taxonomic assignment of molecular operational taxonomic units (MOTUs) was performed using the program ECOTAG, with our two reference databases and the sequences extracted from the release 142 (standard sequences) of the ENA database (http://www.ebi.ac.uk/ena). Considering the incorrect assignment of a few sequences to the sample due to tag jumps (Schnell, Bohmann, & Gilbert, 2015), all the sequences with a frequency of occurrence < 0.001 per sequence and per library were discarded. Then, the data were curated for Index-Hopping (MacConaill et al., 2018) with a threshold empirically determined per sequencing batch using experimental blanks (i.e., combinations of tags not present in the libraries) for a given sequencing batch between libraries.

Compressed files containing forward and reverse Illumina sequence files after adapter trimming and a text file containing the correspondence between sample code, the code of the library in which it was sequenced and the tag used for PCR.