Soil carbon pools are affected by species identity and productivity in a tree common garden experiment
Bibliographic record
Abstract
The formation and turnover of soil organic carbon (C), the largest terrestrial C pool, is strongly impacted by the ultimate source of that C: leaves, wood, roots, and root exudates. The quantity and quality of these inputs is determined by the identity of the plants involved. Yet substantial uncertainty surrounds the complex relationships among plant traits and soil C, precluding efforts to maximize whole-ecosystem C uptake in nature-based climate mitigation scenarios. In this study, we leveraged a biodiversity-ecosystem function experiment with trees (IDENT) to explore the effects of interspecific variation in plant traits on soil C dynamics in the very early stages of stand development (9 years since planting). Mineral soil C stocks to 5 cm depth were quantified in monospecific plots of 19 tree species planted on a former agricultural field, and analyzed in relation to tree growth and functional traits. We found that tree species identity affected soil bulk density and, to a lesser extent, the carbon content of the topsoil, and thereby total C pools. Among species and across plots, mineral soil C stocks were positively correlated with rates of tree growth and were significantly larger beneath broadleaf trees with “fast” functional traits vs. conifers with more conservative leaf traits, when comparisons were made over equivalent soil depth increments. Thus, plant functional traits mediate interspecific differences in productivity, which in turn influence the magnitude of belowground C stocks. These results highlight important linkages between above- and belowground carbon cycles in the earliest stages of afforestation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".