Traitbased modeling of microbial distribution and carbon turnover in the rhizosphere

Microorganisms possess the ability to adapt to different environmental conditions through the use of various strategies. This diversity in strategies allows us to categorize them based on their functions in the ecosystem. Copiotrophs have a fast growth rate but a low carbon use efficiency (CUE), while oligotrophs have a slow growth rate but a high CUE. In the rhizosphere, the effect of root exudation on different functional microbial groups is not well understood. Process-based modeling is a useful tool to analyze the complex feedback between roots and soil in the rhizosphere. Here, we present a rhizosphere model that explicitly considers two different microbial groups (oligotrophs and copiotrophs) classified based on their microbial traits that correlates each other due to physiological trade-offs and organic carbon accessibility (dissolved organic carbon, mucilage and sorbed carbon). The model is one-dimensional axisymmetric, simulating a soil cylinder around individual root segments. The model was conditioned using a novel constraint-based Markov chain Monte Carlo parameter sampling method. Applying this approach enabled the identification of parameter sets that led to plausible model results in agreement with experimental findings from a comprehensive literature review. The conditioned model predicts organic matter concentration curves from the root surface into the soil driven by root exudation. Our simulations show a decreasing pattern of dissolved organic carbon, which is utilized by oligotrophs and copiotrophs, away from the root surface. Furthermore, we observe a slightly higher proportion of copiotrophs than oligotrophs near the root surface and dominance of copiotrophic biomass at very high nutrient availability conditions as expected from ecological theory and experimental evidence. However, the model predictions are still highly uncertain. Thus, further experimental data and observations are required for model conditioning.