Enhanced understanding of soil methane processes through modeling microbial kinetics and taxonomy

Soil methane (CH₄) emissions significantly impact climate change. However, microbial controls of CH₄ in global carbon cycle gain less attention than CO₂, hindering the understanding of CH₄ processes. Here, stemming from a baseline model (MENDmm1) with one microbial group, we developed a microbial-explicit CH₄ model by representing six microbial groups following Michaelis-Menten kinetics (MENDmm6). We compared MENDmm6 with MENDfo6 (first-order kinetics) and MENDmm5 (excluding syntrophic acetate oxidation, SAO), alongside MENDmm1. Split-sample calibration and validation were conducted using high-temporal-resolution CO₂ and CH₄ effluxes from two soils (Oxisol and Mollisol) under five oxygen-fluctuation treatments. MENDmm6 (mean R² = 0.66) improved CH₄ modeling by 47% over MENDmm1 (mean R² = 0.45), with a 15% improvement for CO₂. MENDmm6-simulated methanogenic and methanotrophic biomass closely matched observed OTU abundances (r = 0.69–0.94), except for methanotrophs in the Oxisol (r = 0.13). Furthermore, including microbial processes without explicit microbial kinetics (MENDfo6) did not improve model performance over MENDmm1. Neglecting SAO in MENDmm5 failed to explain the observed hydrogenotrophic methanogenesis dominance. Our results emphasize the significance of explicit microbial communities and kinetics in CH₄ modeling. The proposed MENDmm6 model, leveraging molecular measurements of CH₄-cycling microbes, will enhance predictions of management impacts on CH₄ emissions, crucial for climate mitigation.