Implications of optimal resource allocation in soil microorganisms

Soil microbes are key players in the cycling of soil organic carbon. In the complex soil system, microbes are faced with multiple stresses and trade-offs. In order to build biomass and proliferate, microbes have to mine accessible substrate and simultaneously have to survive abiotic stresses such as dry conditions. How they allocate carbon to the production of microbial biomass, extracellular enzymes, or biomolecules that help resist abiotic stresses is an important control of soil organic carbon fate. High carbon use efficiency fuels the build-up of microbial necromass, while increased production of exoenzymes might accelerate the breakdown of particulate organic matter. Production of additional biopolymers needed to sustain metabolic activity under stress – e.g., the production of osmolytes for maintaining turgor pressure in drying soils – poses an additional carbon cost that trades-off with the production of biomass and extracellular enzymes. Here we propose a conceptual model of soil carbon cycling with an explicit representation of these microbial allocation trade-offs. The model resolves physical processes such as saturation dependent substrate diffusion and is formulated at steady-state. It is based on the premise that microbes are optimally adapted to the environment they inhabit – meaning that the allocation trade-offs between the production of biomass, extracellular enzymes, and biomolecular stress response are adapted to maximize the microbial growth rate under these conditions. Using this conceptual model, we investigate how microbial allocation traits (fraction of carbon taken up and allocated to new biomass, extracellular enzymes, or osmolytes) might vary over a range of environmental conditions. Optimal allocation of carbon leads to increased investment in extracellular enzymes when carbon is scarce, and to progressively higher investment in osmolytes in drier conditions. While these trends are somewhat expected, the model predicts (rather than prescribing) the sensitivity of these allocation traits to changes in soil moisture and available carbon as a consequence of the optimality assumption. We conclude by exploring what implications these results might have for soil organic carbon fate.