The decomposition of macrophytes plays a crucial role in the nutrient cycles of macrophyte-dominated eutrophication lakes. While research on plant decomposition mechanisms and microbial influences has rapid developed, it is curious that plant decomposition models have remained stagnant at the single-stage model from 50 years ago, without endeavor to consider any important factors. Our research conducted in-situ experiments and identified the optimal metrics for decomposition-related microbes, thereby establishing models for microbial impacts on decomposition rates (k_RDR). Using backward elimination in stepwise regression, we found that the optimal subset of independent variables-specifically Gammaproteobacteria-Q-L, Actinobacteriota-Q-L, and Ascomycota-Q-L-increased the adjusted R-squared (Ra2) to 0.93, providing the best modeling for decomposition rate (p = 0.002). Additionally, k_RDR can be modeled by synergic parameters of ACHB-Q-L, LDB-Q-L, and AB-Q-L for bacteria, and SFQ for fungi, albeit with a slightly lower Ra2 of 0.7-0.9 (p < 0.01). The primary contribution of our research lies in two key aspects. Firstly, we introduced optimal metrics for modeling microbes, opting for debris surface microbes over sediment microbes, and prioritizing absolute abundance over relative abundance. Secondly, our model represents a noteworthy advancement in debris modeling. Alongside elucidating the focus and innovative aspects of our work, we also addressed existing limitations and proposed directions for future research. SYNOPSIS: This study explores optimum metrics for decomposition-related microbes, offering precise microbial models for enhanced lake nutrient cycle simulation.