Rewriting ecosystem carbon budgets with genome-calibrated soil microbial enzyme kinetics – EcoKin

The fate of vast soil carbon stocks under climate change is highly uncertain, risking accelerated warming via microbial decomposition feedbacks. Yet large-scale models ignore microbial processes, hampering reliable predictions of future warming. Microbial decomposition is mediated by extracellular enzymes (EE) and although EE-based soil microbial models exist, they remain difficult to parameterize and do not outperform traditional models due to limited measurable microbial traits. Using an unorthodox approach across 15 orders of spatial magnitude, EcoKin will (1) use machine learning to predict EE kinetic traits from genes, (2) quantify EE abundances and kinetics in environmental microbial communities from metagenomic sequences, and (3) create an EE-based dynamic ecosystem model by calibrating OM decomposition rates with genome-informed EE kinetics. EcoKin has the potential to rewrite soil C fate under future global warming.