Indicators and predictive frameworks for microbial regime shifts in aquatic ecosystems: From collapse detection to recovery prediction

Microbial communities regulate essential biogeochemical processes in aquatic ecosystems and play a central role in maintaining ecosystem stability. But the increase in environmental pressures resulting from climate change, eutrophication, pollution, and hydrological alteration has intensified interest in predicting microbial community collapse before irreversible ecological change occurs. This review examines the current knowledge on the prediction of microbial community collapse in aquatic ecosystems through four interconnected areas, including multi-omics-based early-warning indicators, quantitative network and functional thresholds, computational frameworks for predicting collapse and recovery, and emerging spatio-temporal network approaches. The current evidence shows that the integration of metagenomics, meta transcriptomics, metabolomics, ecological network analysis, and mechanistic modeling has substantially improved understanding of microbial instability and resilience. Functional perturbations often precede detectable taxonomic changes, while recovery trajectories frequently involve restoration of ecosystem function without complete recovery of community composition. Constraint-based metabolic models, machine-learning approaches, and dynamic ecological models each provide complementary predictive capabilities but differ in their mechanistic interpretability, data requirements, and applicability across spatial and temporal scales. Likewise, spatio-temporal network frameworks provide an increasingly comprehensive representation of microbial ecosystem dynamics but remain limited by high computational demands and the scarcity of high-resolution environmental observations. Despite these advances, operational prediction of microbial collapse remains constrained by limited long-term monitoring datasets, insufficient validation of proposed ecological thresholds across ecosystem types, incomplete integration of environmental drivers into predictive models, and persistent uncertainty associated with nonlinear ecological regime shifts. Future progress will require standardized monitoring protocols, systematic cross-ecosystem validation of predictive indicators, integration of multi-omics with mechanistic and data-driven modeling, and adaptive validation frameworks capable of quantifying predictive uncertainty.