资源环境科技发展态势分析平台

Oxygen (O₂) limitation contributes to persistence of large carbon (C) stocks in saturated soils. However, many soils experience spatiotemporal O₂ fluctuations impacted by climate and land-use change, and O₂-mediated climate feedbacks from soil greenhouse gas emissions remain poorly constrained. Current theory and models posit that anoxia uniformly suppresses carbon (C) decomposition. Here we show that periodic anoxia may sustain or even stimulate decomposition over weeks to months in two disparate soils by increasing turnover and/or size of fast-cycling C pools relative to static oxic conditions, and by sustaining decomposition of reduced organic molecules. Cumulative C losses did not decrease consistently as cumulative O₂ exposure decreased. After >1 year, soils anoxic for 75% of the time had similar C losses as the oxic control but nearly threefold greater climate impact on a CO₂-equivalent basis (20-year timescale) due to high methane (CH₄) emission. A mechanistic model incorporating current theory closely reproduced oxic control results but systematically underestimated C losses under O₂ fluctuations. Using a model-experiment integration (ModEx) approach, we found that models were improved by varying microbial maintenance respiration and the fraction of CH₄ production in total C mineralization as a function of O₂ availability. Consistent with thermodynamic expectations, the calibrated models predicted lower microbial C-use efficiency with increasing anoxic duration in one soil; in the other soil, dynamic organo-mineral interactions implied by our empirical data but not represented in the model may have obscured this relationship. In both soils, the updated model was better able to capture transient spikes in C mineralization that occurred following anoxic–oxic transitions, where decomposition from the fluctuating-O₂ treatments greatly exceeded the control. Overall, our data-model comparison indicates that incorporating emergent biogeochemical properties of soil O₂ variability will be critical for effectively modeling C-climate feedbacks in humid ecosystems.