2026/06/09 by Erik Schwarz, Elsa Abs, Pierre Quévreux +2 · 1 voice
Agricultural and Biological Sciences · Environmental Science · #Legume Nitrogen Fixing Symbiosis #Microbial Community Ecology and Physiology #Polar Research and Ecology
paper · doi:10.1016/j.soilbio.2026.110211
openalex publication_date 2026/06/09 · openalex created_date 2026/06/10 · openalex updated_date 2026/07/22
Microbial-explicit models of soil organic carbon (SOC) turnover aim to harness our understanding of soil processes to improve predictions of the global carbon cycle. Although being “microbial-explicit”, these models often describe microbes similar to a chemical catalyst rather than a biological entity that interacts with the environment through adaptation (evolution and community processes). Eco-evolutionary optimization (EEO) approaches abstract from the complexity of adaptive processes, allowing for a minimalist representation of microbial ecology in models. Here we develop a novel microbial-explicit model that accounts for micro-scale microbial interactions but remains structurally simple. Specifically, the model describes microbial production of a public good that requires microbes to invest into costly extracellular enzymes. To understand the relevance of underlying assumptions about competition within the microbial community (i.e., by cheaters that consume the public good, but contribute less to its production), we apply different EEO approaches to this model that either neglect or account for effects of competition. We then compare predictions of both approaches against empirical data on biomass-specific potential enzyme activities from the LUCAS 2018 topsoil data. The eco-evolutionary optimal production of extracellular enzymes predicted by the model matched general qualitative trends along a SOC gradient in the observational data. However, results were unrealistically sensitive to exploitive competition by cheaters—pointing to difficulties of realistically resolving micro-scale processes in simple model formulations. While quantitative predictions critically depend on the EEO approach, agreement on qualitative patterns that also match empirical observations indicate that optimal control of extracellular enzyme production could help to improve representations of soil carbon dynamics in predictive models.