Seasonal Hydrological Variability Shapes Protistan Community Diversity and Assembly in a Subtropical River as Revealed by Environmental DNA Metabarcoding

Hydrological seasonality is a major driver of microbial community dynamics in river ecosystems, yet how seasonal flow regimes regulate protistan diversity, community assembly, and functional organization remains poorly understood. In this study, environmental DNA metabarcoding targeting the 18S rRNA gene was employed to investigate seasonal variations in protistan communities across 17 sites along the Hongshuihe River, a subtropical tributary of the Pearl River, during flood and normal seasons. Flood events significantly altered environmental conditions and increased protistan alpha diversity, while community composition showed clear seasonal differentiation. Although dominant protistan lineages remained relatively stable, seasonal turnover was primarily associated with rare and low-abundance taxa, highlighting their critical role in maintaining biodiversity and driving community reassembly. Null model analyses revealed a seasonal shift in community assembly mechanisms, with deterministic processes, particularly heterogeneous selection, dominating during the flood season, whereas stochastic drift prevailed during the normal season. Distance–decay and variance partitioning analyses further demonstrated that environmental and spatial processes jointly regulated protistan community variation, with stronger environment–spatial coupling during flood periods. Functional trait analysis revealed an increased proportion of consumer taxa and a reduced contribution of phototrophic taxa during floods, suggesting a shift toward stronger heterotrophic pathways under hydrological disturbance. Collectively, our findings demonstrate that hydrological seasonality regulates riverine protistan communities through coordinated changes in rare taxa dynamics, assembly processes, and functional restructuring, emphasizing the importance of incorporating protistan diversity and functional traits into long-term river ecosystem monitoring.