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

Abstract

AbstractMicrobial 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.