Catastrophic regime shifts in shallow lakes are hard to predict due to a lack of clear understanding of the associate mechanisms. Theory of alternative stable states suggests that eutrophication has profound negative effects on the structure, function and stability of freshwater ecosystems. However, it is still unclear how eutrophication destabilizes ecosystems stoichiometrically before a tipping point is reached. The stoichiometric homeostasis (H), which links fine-scale process to broad-scale patterns, is a key parameter in ecological stoichiometry. Based on investigation of 97 shallow lakes on the Yangtze Plain, China, we measured nitrogen (N) and phosphorus (P) concentrations of the aboveground tissues of common submerged macrophyte species and their corresponding sediments. We found submerged macrophytes showed significant stoichiometric homeostasis for P (H-p) but not for N (H-N). Furthermore, H-p was positively correlated with dominance and stability at the species level, and community production and stability at the community level. Identifying where macrophyte community collapse is a fundamental way to quantify their resilience. Threshold detection showed that macrophyte community dominated by high-H-p species had a higher value of tipping point (0.08 vs. 0.06 mg P L-1 in lake water), indicating their strong resilience to eutrophication. In addition, macrophytes with high H-p were predominant in relative oligotrophic sediments and have higher ability in stabilizing the water environment compared to those low-H-p ones. Our results suggested that ecosystem dominated by homeostatic macrophyte communities was more productive, stable and resilient to eutrophication. Eutrophication-induced stoichiometric imbalance may destabilize the ecosystem by altering the community structure from high-to low-H-p species. Efforts should be focused on maintaining and restoration of high homeostatic communities to make ecosystem more resilient, which can significantly improve our understanding of the critical transition mechanisms. (C) 2018 Elsevier Ltd. All rights reserved.