Data from: Tree species diversity promotes litterfall productivity through crown complementarity in subtropical forests
Bibliographic record
Abstract
1. The role of niche complementarity for driving the positive biodiversity-ecosystem productivity relationship has been widely recognized, but there is scant evidence regarding the role of tree canopy structure on this relationship. Litterfall productivity is proportional to forest net primary productivity in natural forests, and we hypothesized that litterfall productivity would increase with tree species diversity via increased tree crown complementarity. 2. We investigated annual litterfall productivity, species diversity, tree crown architecture, soil moisture content, soil carbon content, and stand age across 28 subtropical forest plots in eastern Zhejiang province, China. Simple linear regression was used to examine bivariate relationships among rarified species richness, crown complementarity, total crown volume, soil moisture content, soil carbon content, stand age, and litterfall productivity. Structural equation modeling was employed to quantify the direct and indirect effects of species richness on litterfall productivity through tree crown complementarity. 3. Litterfall productivity increased with rarefied species richness via increasing crown complementarity rather than total crown volume. Species richness, crown complementarity, and litterfall productivity increased with soil moisture content, while crown complementarity and litterfall productivity increased with soil carbon content. Neither species richness nor crown complementarity increased with stand age, even though litterfall productivity increased with stand age. 4. Synthesis. Our study provides evidence for a strong role of tree crown assembly in shaping ecosystem function in complex natural forests. Our findings suggest that crown spatial complementarity among trees operates mechanistically to drive the positive tree species diversity-litterfall productivity relationship in subtropical forests. We argue that community and/or ecosystem ecology would benefit from more attention to crown variability among coexisting tree species.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".