SSS4.11 | Beyond Growth: Microbial Carbon and Energy Use in Soils
Beyond Growth: Microbial Carbon and Energy Use in Soils
Convener: Rebeca Leme OlivaECSECS | Co-conveners: Martin-Georg EndressECSECS, Thomas Maskow

Soil microorganisms allocate carbon (C) and energy not only to growth but also to maintenance and other non-growth processes, including cellular repair and stress tolerance, dormancy, storage, and the production of extracellular enzymes and polymeric substances. How microorganisms partition resources among these growth and non-growth pathways determines whether C is respired, released to the extracellular environment, or retained in biomass, with consequences for necromass formation and the persistence of soil organic matter. Yet, the energetic costs and trade-offs underlying this partitioning remain poorly quantified and are not fully reflected in common metrics like the microbial carbon use efficiency (CUE). Thermodynamic approaches offer a complementary mechanistic framework, with the potential to constrain and predict metabolic responses even in complex systems where individual processes cannot be fully resolved.
This session explores microbial C and energy allocation as a mechanistic link between microbial physiology and soil C persistence, from single cells to the ecosystem scale. We welcome experimental, theoretical and modelling contributions on microbial maintenance and non-growth metabolism, carbon and energy use efficiency, microbial responses to environmental change (e.g., warming, drought, land-use change), and implications for C stabilization. We particularly encourage studies applying or combining methods to quantify microbial C and energy use, such as isotope tracing, calorespirometry, thermodynamic approaches, and process-based modelling. By connecting microbial ecology and physiology with soil biogeochemistry, we aim to advance a bioenergetic understanding of how microbial metabolism regulates the fate of soil C and to explore how thermodynamic constraints can improve the mechanistic basis of predictive models.