A Computationally Efficient Method for Parameter Sensitivity Analysis of Microbially Explicit Biogeochemical Models Accounting for Long-Term Behavior

Microbial ecological models become increasingly complex owing to the incorporation of many parameters and multiple biotic and abiotic processes. However, little attention has been paid to the variations in the parameter sensitivity during long-term versus short-term simulations. Here, we developed a Multi-Objective Parameter Sensitivity Analysis (MOPSA) method to efficiently identify the important parameters in complex microbial ecological models with multiple response variables of interest in terms of short- and long-term model simulations. We found that MOPSA was more computationally efficient for complex microbial ecological models than Sobol's method because of MOPSA's reliability and low computational sample size. In addition, we address the increased significance of microbial physiology in mediating long-term than short-term soil C-N cycling, indicating that experiment-model integration practices should examine model behaviors beyond the conventional short-term experimental period. The outcomes of this study provide an efficient global sensitivity analysis method for parameterization and the scientific foundation for microbial physiology in mediating long-term microbial ecological processes. Biogeochemical cycles can be effectively characterized by process-based modeling. Microbial ecological models are more intricate and include more parameters in comparison to traditional ecological models. This complexity demands substantial computational resources when using current methods to estimate model sensitivity to different parameters. To address this challenge, we developed an efficient global sensitivity analysis method, known as Multi-Objective Parameter Sensitivity Analysis (MOPSA). Using MOPSA, we successfully conducted sensitivity analyses over both short and long timeframes for the complex microbial model. Our study reveals that microbial physiology becomes more significant in mediating long-term carbon and nitrogen cycling, compared to short-term processes. This highlights the need to pay close attention to the long-term processes when we conduct experiment-model integration practices. A new, computationally efficient parameter sensitivity analysis method was applied to a microbially explicit soil carbon model The importance of microbial physiology is higher in long-term than in short-term simulations The outcomes have implications for other microbially explicit biogeochemical models