Changing plant species composition and richness benefit soil carbon sequestration under climate warming
Bibliographic record
Abstract
Abstract Anthropogenic warming and land‐use change are expected to accelerate global soil organic carbon (SOC) losses and change plant species composition and richness. However, how changes in plant composition and species richness mediate SOC responses to climate warming and land‐use change remains poorly understood. Using data from a 7‐year warming and clipping field experiment in an alpine meadow on the Qinghai–Tibetan Plateau, we examined the direct effects of warming and clipping on SOC storage versus their indirect effects mediated by plant functional type and species richness. We found that warming significantly increased SOC storage by 8.1% and clipping decreased it by 6.4%, which was closely correlated with the corresponding response of below‐ground net primary productivity (BNPP). We also found a negative correlation between SOC storage and species richness, which was ascribed to the increased BNPP via enhancing the dominance of grasses and decreasing species richness under warming. The lower SOC storage under clipping was caused by the clipping‐induced decrease in BNPP via weakening the dominance of grasses and increasing species richness. Our findings highlight that the SOC storage in this alpine meadow under climate warming and clipping was primarily governed by BNPP changes, which was mediated by changes in the dominance of grasses and species richness. Overall, our study demonstrates that shifting to the dominance of grasses and changing species richness would benefit soil C sequestration under climate warming, but this positive effect would be dampened by grazing or hay harvest. Read the free Plain Language Summary for this article on the Journal blog.
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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) | 0.001 | 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".