Resource availability enhances positive tree functional diversity effects on carbon and nitrogen accrual in natural forests
Bibliographic record
Abstract
Forests harbor extensive biodiversity and act as a strong global carbon and nitrogen sink. Although enhancing tree diversity has been shown to mitigate climate change by sequestering more carbon and nitrogen in biomass and soils in manipulative experiments, it is still unknown how varying environmental gradients, such as gradients in resource availability, mediate the effects of tree diversity on carbon and nitrogen accrual in natural forests. Here, we use Canada's National Forest Inventory data to explore how the relationships between tree diversity and the accumulation of carbon and nitrogen in tree biomass and soils vary with resource availability and environmental stressors in natural forests. We find that the positive relationship between tree functional diversity (rather than species richness) and the accumulation of carbon in tree biomass strengthens with increasing light and soil nutrient availability. Moreover, the positive relationship between tree functional diversity and the accumulation of carbon and nitrogen in both organic and mineral soil horizons is more pronounced at sites with greater water and nutrient availabilities. Our results highlight that conserving and promoting functionally diverse forests in resource-rich environments could play a greater role than in resource-poor environments in enhancing carbon and nitrogen sequestration in Canada's forests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".