Taxon-specific metabarcoding provides new insights into marine phytoplankton diversity and ecological interactions: a case study of Kareniaceae in China's coastal seas.

Universal molecular metabarcoding frequently lacks sufficient resolution and analytical sensitivity to characterize cryptic diversity and fine-scale ecological interactions in marine environments. Harmful algal blooms (HABs) caused by the dinoflagellate family Kareniaceae are increasing in frequency and geographic extent worldwide, posing serious threats to marine ecosystems, fisheries, and human health. These HAB events are characterized by frequent co-occurrence of multiple Kareniaceae species (termed Kareniaceae multi-species algal blooms, KMSABs). However, Kareniaceae species diversity and ecological interactions during KMSABs cannot be accurately assessed by universal 18S rDNA metabarcoding. In this study, we developed and validated a high-resolution, taxon-specific ITS metabarcoding assay targeting the family Kareniaceae and applied it to phytoplankton samples collected from China's coastal seas, including the Bohai Sea, the Yellow Sea and the East China Sea. Our approach revealed a significant increase in detected species richness compared to universal 18S rDNA metabarcoding and uncovered that over 60% of amplicon sequence variants (ASVs) represent previously undescribed lineages. By integrating phylogenetic reconstruction with environmental niche and spatiotemporal distribution data (an eco-evolutionary framework), we partitioned unclassified sequences into biologically meaningful assemblages with distinct environmental preferences. Furthermore, the high sequence saturation achieved by targeted enrichment enabled robust joint species distribution modeling (JSDM), which disentangled abiotic filtering (temperature and nutrients) from intrinsic biotic associations. The dense positive residual correlations among specific lineages provide a mechanistic explanation for the phenomenon of KMSABs. This scalable framework translates ambiguous genetic data into ecologically functional taxonomic units, providing a powerful tool for predicting and managing the dynamics of marine microbial communities.