Monitoring phytoplankton community dynamics, indicator taxa, and harmful algal bloom risks in a subtropical reservoir using eDNA metabarcoding

Effective phytoplankton monitoring is vital for maintaining healthy aquatic ecosystems. To accurately assess phytoplankton community composition and distribution of harmful algal blooms (HABs), this study compared environmental DNA (eDNA) metabarcoding and the morphological approach in subtropical Xikeng Reservoir, southern China. eDNA metabarcoding identified 124 genera from 17 sites by metabarcoding of 18S rDNA V9 and 16S rDNA V3 regions. eDNA metabarcoding outperformed the morphological approach in capturing nano- and picoplankton and indicator taxa (e.g., Paraphysomonas, Synechococcus). eDNA metabarcoding showed that genus richness was higher in the wet season than in the dry season, with marked community divergence driven by Pseudanabaena and Cryptomonas. The association between environmental factors and family- or order-level amplicon sequence variant (ASV) abundance was stronger than that with other taxonomic levels, with dissolved oxygen, chlorophyll-a, total phosphorus, chemical oxygen demand by Mn and ammonia nitrogen significantly correlated across all taxonomic levels. Additionally, HAB risks varied seasonally: from cyanobacteria-dominated toxic and odor risks in wet season to toxic, odor, and structural disruptions co-dominated by both cyanobacteria and eukaryotic phytoplankton in dry season. These findings demonstrate the advantages of eDNA metabarcoding in capturing phytoplankton diversity and highlight its complementary role in existing biomonitoring programs.