Abstract
Phytoplankton is the basis of the food web and an indicator of environmental change in aquatic ecosystems. Phytoplankton assessment uses microscopy, which estimates the composition, absolute abundance and biomass of taxa, and metabarcoding, which estimates the composition, high richness and diversity, and relative abundance of taxa. Problems remain with the consistency of results from these two methods and the quantification of metabarcoding. Using 18S rRNA metabarcoding and microscopy we compared the relative or absolute abundance and biomass of phytoplankton taxa (class or genus/species) in the south basin of Lake Baikal in spring over 3 years. Absolute abundance/biomass of phytoplankton taxa estimated by metabarcoding was obtained by combining relative abundances of amplicon sequence variants (ASV produced by error-correcting method) derived from the V8-V9 region of 18S rRNA gene amplicon sequencing (primers were used that accurately represented the mean relative abundance of different microalgae) with total or class-specific abundance/biomass of phytoplankton estimated by light microscopy. Many Spearman correlations were found between relative (non- or clr-transformed) or absolute abundances/biomasses of the same phytoplankton classes or genus/species. Correlation coefficients were higher between absolute values than between relative values. Correlations were found between relative or absolute abundance/biomass, estimated by both methods, of the classes Bacillariophyceae, Coscinodiscophyceae, Mediophyceae, Chrysophyceae, Cryptophyceae, and Chlorophyceae, but not Dinophyceae and Trebouxiophyceae. Correlations were found between relative or absolute abundance/biomass of dominant species and ASVs of diatoms (Ulnaria, Aulacoseira, Stephanodiscus), Chrysophyceae (Dinobryon), and Cryptophyceae (Cryptomonas). Thus, the consistency of the dynamics of the relative or absolute abundance/biomass of phytoplankton taxa estimated by the two methods was revealed. Absolute abundances/biomasses of taxa estimated by metabarcoding in combination with microscopy improve the accuracy of metabarcoding-based ecological assessment.