The microbial community metabolic regime adapts to hydraulic disturbance in river–lake systems with high–frequency regulation

Background: River–lake ecosystems are crucial for the rational allocation of water resources, but frequent water diversion can destabilize water quality due to hydraulic disturbance. Microbial communities can respond rapidly to such external perturbations and influence these systems through the effects on nutrient metabolism. Therefore, understanding how microbial communities respond to hydraulic shocks in aquatic systems and whether they can adapt to such disturbances is essential for maintaining the health of river–lake systems. We used 16S rRNA and metagenomic sequencing technologies to examine the metabolic regimes of microbial communities during water regulation and non-regulation periods in river–lake systems. Results: We found that hydraulic disturbance tended to drive the microbial community toward homogenized selection, thereby weakening its stability. Flow velocity (V) and the nitrate (NO<inf>3</inf>⁻–N) concentration significantly affected microbial community composition and abundance, with clear threshold effects. We established low (V = 0.284 m/s, NO<inf>3</inf>⁻–N = 0.031 mg/L) and high (V = 0.461 m/s, NO<inf>3</inf>⁻–N = 0.055 mg/L) thresholds. These thresholds categorize microbial communities into three distinct regimes: regime1 (R1), regime 2 (R2), and regime 3 (R3). The microbial abundances in R1 and R3 were significantly higher than those in R2 (p < 0.01), while the community in R3 exhibited a strong denitrification capacity. In R3, the microbial community enhanced its denitrification metabolism by promoting the growth of denitrifying microbial genera (e.g., Pseudomonas and Flavobacterium) to counterbalance the impact of high V and NO<inf>3</inf>⁻–N. These strains contributed the denitrification-related genes nasA, narB, nirB, and nirD to the community, thereby promoting the NO<inf>3</inf>⁻–N metabolism and reducing environmental NO<inf>3</inf>⁻–N concentrations. In addition, we predicted microbial community abundance using an artificial neural network to validate the thresholds we identified. Conclusions: Our study provides theoretical support for understanding how microbial communities adapt to high-frequency hydraulic disturbances and offer valuable insights for managers to adjust water diversion strategies in a timely manner, thereby safeguarding the integrity of river–lake ecosystems.