Effects of plant diversity on soil carbon in diverse ecosystems: a global meta‐analysis
Bibliographic record
Abstract
Soil organic carbon (SOC) is a valuable resource for mediating global climate change and securing food production. Despite an alarming rate of global plant diversity loss, uncertainties concerning the effects of plant diversity on SOC remain, because plant diversity not only stimulates litter inputs via increased productivity, thus enhancing SOC, but also stimulates microbial respiration, thus reducing SOC. By analysing 1001 paired observations of plant mixtures and corresponding monocultures from 121 publications, we show that both SOC content and stock are on average 5 and 8% higher in species mixtures than in monocultures. These positive mixture effects increase over time and are more pronounced in deeper soils. Microbial biomass carbon, an indicator of SOC release and formation, also increases, but the proportion of microbial biomass carbon in SOC is lower in mixtures. Moreover, these species-mixture effects are consistent across forest, grassland, and cropland systems and are independent of background climates. Our results indicate that converting 50% of global forests from mixtures to monocultures would release an average of 2.70 Pg C from soil annually over a period of 20 years: about 30% of global annual fossil-fuel emissions. Our study highlights the importance of plant diversity preservation for the maintenance of soil carbon sequestration in discussions of global climate change policy.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.017 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".