Towards quantitative metabarcoding of eukaryotic plankton: an approach to improve 18S rRNA gene copy number bias

Plankton metabarcoding is increasingly implemented in marine ecosystem assessments and is more cost-efficient and less time-consuming than monitoring based on microscopy (morphological). 18S rRNA gene is the most widely used marker for groups’ and species’ detection and classification within marine eukaryotic microorganisms. These datasets have commonly relied on the acquisition of organismal abundances directly from the number of DNA sequences (i.e. reads). Besides the inherent technical biases in metabarcoding, the largely varying 18S rRNA gene copy numbers (GCN) among marine protists (ranging from tens to thousands) is one of the most important biological biases for species quantification. In this work, we present a gene copy number correction factor (CF) for four marine planktonic groups: Bacillariophyta, Dinoflagellata, Ciliophora miscellaneous and flagellated cells. On the basis of the theoretical assumption that ‘1 read’ is equivalent to ‘1 GCN’, we used the GCN median values per plankton group to calculate the corrected cell number and biomass relative abundances. The species-specific absolute GCN per cell were obtained from various studies published in the literature. We contributed to the development of a species-specific 18S rRNA GCN database proposed by previous authors. To assess the efficiency of the correction factor we compared the metabarcoding, morphological and corrected relative abundances (in cell number and biomass) of 15 surface water samples collected in the Belgian Coastal Zone. Results showed that the application of the correction factor over metabarcoding results enables us to significantly improve the estimates of cell abundances for Dinoflagellata, Ciliophora and flagellated cells, but not for Bacillariophyta. This is likely to due to large biovolume plasticity in diatoms not corresponding to genome size and gene copy numbers. C-biomass relative abundance estimations directly from amplicon reads were only improved for Dinoflagellata and Ciliophora. The method is still facing biases related to the low number of species GCN assessed. Nevertheless, the increase of species in the GCN database may lead to the refinement of the proposed correction factor.