Informing Litter Decomposition Models With Molecular Diversity Data

Abstract Predicting litter decomposition remains a central challenge in biogeochemical modeling. Traditional models of litter decomposition lack the chemical resolution necessary to capture intra‐molecular interactions like chemical protection of carbohydrates and proteins by lignins, which creates a bottleneck during decomposition, and do not describe microbial responses specific to compound classes. Solid‐state 13 C Nuclear Magnetic Resonance (NMR) spectroscopy offers molecular‐level resolution of litter composition, which can help constrain decomposition models—yet its potential has not been fully exploited. Here, we develop a chemically resolved litter decomposition model informed by 13 C NMR‐derived compound classes—carbohydrates, proteins, lignins, lipids, and carbonyls—to evaluate how litter chemistry and microbial physiology influence decomposition dynamics. We synthesized data from 17 studies reporting temporal changes in litter mass and NMR spectra across 89 litter types. Four model scenarios were tested, varying in their treatment of lignin protection and the metabolic cost of oxidative enzyme production. Calibrating four model scenarios yielded strong agreement with observed mass loss across chemical classes (Nash–Sutcliffe modeling efficiency > 0.95). While the inclusion of a lignin‐specific rate modifier did not universally enhance predictive accuracy, it enabled mechanistic representation of microbial trade‐offs between enzyme allocation and substrate decomposition. In scenarios incorporating enzyme production costs, the initial carbon use efficiency and the efficiency of nitrogen recycling at microbial senescence were reduced, consistent with theoretical expectations under nutrient‐limited conditions. These findings demonstrate that integrating molecular‐scale litter chemistry into litter decomposition models allows explicit representation of substrate specific microbial processing of diverse compounds and metabolic tradeoffs.