A First-Principle Based Theory Predicts Observed Moisture Sensitivity of Soil Heterotrophic Respiration
Microbial substrate affinity parameters are critical for modeling soil biogeochemistry dynamics, including CH4, oxygen, and dissolved organic carbon. Traditionally, modelers have relied on calibration for these parameters, which often leads to unresolvable equifinality. Here, LBNL scientists developed a first-principle based theory to predict these parameters in variably saturated soils (Tang and Riley, 2019). The theory uses readily measured soil physical parameters and theoretically well-constrained microbial parameters and explains the large variability in observed affinity parameters used in many existing land biogeochemistry models. The authors then predicted how soil moisture regulates soil heterotrophic respiration and demonstrated that the method can reduce this substantial uncertainty in current land models.
The analysis demonstrates that: (1) existing model applications have underestimated the variability of affinity parameters for gases like CH4 and O2, and aqueous solutes like DOC and NO3- ; (2) a significant fraction of affinity parameter variability results from abiotic, rather than biological, factors; and (3) it is possible to predict the dynamic moisture sensitivity of soil heterotrophic respiration.
Land biogeochemistry models often attempt to represent how various substrates, such as CH4, O2, dissolved organic carbon, and aqueous nutrients, are acquired by microbes and plants. These calculations rely on Michaelis-Menten-Monod-type substrate kinetics, which include a substrate affinity parameter. Traditionally, these parameters are obtained by parameter fitting against very uncertain measurements, and they are often assumed not to be affected by soil moisture. As a result, the parameters are usually very uncertain, and model predictions are therefore compromised by parametric equifinality. LBNL scientists developed a new theory and analytical formulations of how such parameters vary with soil moisture by combining concepts of diffusion integrated across spatial scales (soil matrix, soil aggregate, microbial cells) and biogeochemical kinetic theory. The theory successfully explained why calibration often results in large ranges of the affinity parameter for a given substrate, and demonstrated that it is possible to predict how the coupled interactions between soil physical properties, substrate characteristics, microbial traits, and chemical pathways lead to the often-observed parabolic shape of soil heterotrophic respiration moisture sensitivity. This study presents a first-principle based modeling approach for soil biogeochemical interactions with soil moisture, is extensible to other poorly constrained controls on soil biogeochemistry, and can significantly reduce the parameterization equifinality common in land models.