Diversity-enhanced canopy space occupation and leaf functional diversity jointly promote overyielding in tropical tree communities
Bibliographic record
Abstract
Understanding the mechanisms that drive biodiversity-productivity relationships is critical for guiding forest restoration. Although complementarity among trees in the canopy space has been suggested as a key mechanism for greater productivity in mixed-species tree communities, empirical evidence remains limited. Here, we used data from a tropical tree diversity experiment to disentangle the effects of tree species richness and community functional characteristics (community-weighted mean and functional diversity of leaf traits) on canopy space filling, and how these effects are related to overyielding. We found that canopy space filling was largely explained by species identity effects rather than tree diversity effects. Communities with a high abundance of species with a conservative resource-use strategy were those with most densely packed canopies. Across monocultures and mixtures, a higher canopy space filling translated into an enhanced wood productivity. Importantly, most communities (83 %) produced more wood volume than the average of their constituent species in monoculture (i.e. most communities overyielded). Our results show that overyielding increased with leaf functional diversity and positive net biodiversity effects on canopy space filling, which mainly arose due to a high taxonomic diversity. These findings suggest that both taxonomic diversity-enhanced canopy space filling and canopy leaf diversity are important drivers for overyielding in mixed-species forests. Consequently, restoration initiatives should promote stands with functionally diverse canopies by selecting tree species with large interspecific differences in leaf nutrition, as well as leaf and branch morphology to optimize carbon capture in young forest stands.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".