Evaluation of a Thermodynamically Based Soil Microbial Decomposition Model Based on a 13c Tracer Study in Arctic Tundra Soils

Monday, 15 December 2014
Xudong Zhu1,2, Jinyun Tang1, William J Riley1, Matthew D Wallenstein2, M Francesca Cotrufo2, Megan B Machmuller2 and Laurel Lynch3, (1)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (2)Colorado State University, Fort Collins, CO, United States, (3)Colorado State University, Natural Resource Ecology Laboratory, Fort Collins, CO, United States
The incorporation of explicit representation of biological complexity in soil carbon decomposition models may improve our ability to accurately predict terrestrial carbon-climate feedbacks. A new generation of microbe-explicit soil decomposition models (MEMs) are being developed that represent soil biological complexity, but only a few take into account detailed biotic and abiotic components and competitive interactions in the complex soil system. In view of this, we have developed a thermodynamically based MEM with a detailed component network (polymeric organic carbon, dissolved organic carbon, microbes, extracellular enzymes, and mineral surfaces), in which competitive interactions and microbial metabolism are modeled using Equilibrium Chemistry Approximation kinetics and Dynamic Energy Budget theory, respectively. The model behavior has been tested and is qualitatively consistent with many empirical studies, but further evaluation of the model with field or lab experimental data in specific ecosystems is needed. Stable carbon isotope (13C) tracer experiments provide a means to directly evaluate soil carbon dynamics simulated by MEMs. In this study, we further develop the model to explicitly account for different carbon isotopes, including 13C and 14C. Isotopic fractionations in soil decomposition processes, including soil organic matter transformations and microbial metabolism, are considered. The 13C signals of different soil components derived from a 13C tracer experiment in Arctic tundra soils are used to test the model behavior and identify needed parametric and structural improvements. Our modeling and data comparison identify several key mechanisms that need to be included in MEMs. Finally, we present an analysis of the relative benefits and costs of additional complexity in MEMs compared to traditional pool-based modeling structures.