Enhanced light interception and light use efficiency explain overyielding in young tree communities
Bibliographic record
Abstract
Diverse plant communities are often more productive than mono-specific ones. Several possible mechanisms underlie this phenomenon but their relative importance remains unknown. Here we investigated whether light interception alone or in combination with light use efficiency (LUE) of dominant and subordinate species explained greater productivity of mixtures relative to monocultures (i.e. overyielding) in 108 young experimental tree communities. We found mixed-species communities that intercepted more light than their corresponding monocultures had 84% probability of overyielding. Enhanced LUE, which arose via several pathways, also mattered: the probability of overyielding was 71% when, in a mixture, species with higher 'inherent' LUE (i.e. LUE in monoculture) intercepted more light than species with lower LUE; 94% when dominant species increased their LUE in mixture; and 79% when subordinate species increased their LUE. Our results suggest that greater light interception and greater LUE, generated by inter and intraspecific variation, together drive overyielding in mixed-species 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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".