Community leaf nutrient characteristics drive soil carbon stabilization by regulating soil nutrient and microbial community in a subtropical forest plantation
Bibliographic record
Abstract
Abstract Tree species diversity has been found to promote soil organic carbon (SOC) in forests, but its effects on SOC stability have been poorly studied. Using a 6-year-old forest biodiversity experiment with monocultures and mixtures of two, four, and eight tree species, we evaluated how functional diversity (FDis) and community-weighted mean (CWM) of leaf nutrients influence the formation of mineral-associated organic carbon (MAOC) via altering the soil microbial community. Our results revealed that FDis of leaf nitrogen (LNmass) and phosphorus (LPmass) contents, as well as CWM of LPmass were negatively associated with MAOC, patterns that were mediated by microbial biomass. In addition, CWM of LNmass was negatively associated with the MAOC:SOC ratio, a relationship mediated by a decrease in the ratio of fungal to bacterial biomass (F:B ratio), while CWM of LPmass exhibited a direct positive effect on the MAOC:SOC ratio. Our results also showed that soil nutrient availability mediated the relationship between the diversity of leaf nutrients on the soil microbial community. These results suggest that the diversity of leaf nutrient contents may shape SOC stabilization through moderating microbial biomass and F:B ratio, offering insights into the ecological importance of plant chemical traits in driving SOC stabilization in forest ecosystems.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".