Convergent dynamics and shared mechanisms of three-pool soil carbon mineralization under different grassland managements
Bibliographic record
Abstract
• A three-pool model coupled with 553-day incubation reveals carbon pool decomposition. • Mineralization shifts from active to passive pools in enclosed and grazed soils. • Similar mineralization mechanisms are shared, despite faster rates in enclosed soils. • Microbial strategies and enzyme activities jointly regulate SOC mineralization. The mineralization dynamics of soil organic carbon (SOC) in grasslands are crucial to terrestrial biogeochemical cycles. However, the regulatory mechanisms underlying extracellular enzyme metabolism and microbial community structure during SOC mineralization across different carbon pools remain poorly understood. In this study, a 553-day incubation experiment was conducted to examine temporal changes in CO 2 emissions, extracellular enzyme activities, microbial biomass, and microbial community composition in soils from both enclosed and grazed grasslands. Using a three-pool model, SOC dynamics were quantified within active, slow, and passive carbon pools, revealing a shift in the dominance of mineralization from the active carbon pool to the passive carbon pool during the long-term carbon turnover, with differences observed across grassland management strategies. Compared to grazed grasslands, enclosed grasslands exhibited an approximately 110% larger active carbon pool and higher initial SOC mineralization rates (significantly higher during the first 113 days), yet long-term microbial and enzymatic regulatory mechanisms—particularly shifts in microbial strategies, enzyme activity patterns, and their interactions with carbon pools—were similar across both management regimes. The observed shifts in carbon pool dynamics were driven by enhanced microbial capacity to decompose passive carbon, associated with substantially increased oxidative enzyme production (e.g., mass-specific oxidase activity increased by 190.6% in enclosed soil and by 256.1% in grazed soil) and elevated nitrogen and phosphorus demands. Notably, microbial communities shifted from fast-growing copiotrophic taxa (e.g., Proteobacteria, Bacteroidetes, Ascomycota) to slower-growing oligotrophic taxa (e.g., Acidobacteria, Actinobacteria, Planctomycetes, Basidiomycota), with the oligotroph-to-copiotroph ratio increasing by 55.5–62.6% for bacteria and 96.9–247.5% for fungi. These changes were closely linked to shifts in enzyme activity profiles and stoichiometric ratios. Overall, this study provides mechanistic insights into how microbial ecological strategies and enzyme activities interact to regulate SOC mineralization across different pools under contrasting grassland management regimes. These findings advance our understanding of SOC turnover and improve predictive capabilities for carbon cycling, with broader implications for global climate change feedbacks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".