No complementarity no gain—Net diversity effects on tree productivity occur once complementarity emerges during early stand development
Bibliographic record
Abstract
Although there is compelling evidence that tree diversity has an overall positive effect on forest productivity, there are important divergences among studies on the nature and strength of these diversity effects and their timing during forest stand development. To clarify conflicting results related to stand developmental stage, we explored how diversity effects on productivity change through time in a diversity experiment spanning 11 years. We show that the strength of diversity effects on productivity progressively increases through time, becoming significantly positive after 9 years. Moreover, we demonstrate that the strengthening of diversity effects is driven primarily by gradual increases in complementarity. We also show that mixing species with contrasting resource-acquisition strategies, and the dominance of deciduous, fast-developing species, promote positive diversity effects on productivity. Our results suggest that the canopy closure and subsequent stem exclusion phase are key for promoting niche complementarity in diverse tree communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; both teacher heads agree on what is shown here.
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".