Functional trait variability and identity determine the extent of tree diversity effects on productivity: A global meta-analysis
Bibliographic record
Abstract
Growing evidence has revealed that ecosystem productivity depends more on the functional characteristics of species than on their number. However, just how the extent of tree diversity effects on ecosystem productivity is influenced by functional trait variability and composition has been rarely tested across and within species richness levels. Employing a meta-analysis of data from 59 global scale tree diversity experiments, we examined how functional dispersion and identity determine the outcomes of tree mixture effects on productivity, both across and at given species richness levels. We found that the positive effects of tree mixtures on productivity were strengthened by the increasing multidimensional functional dispersion and the community-weighted mean of leaf nitrogen content both across, and within, two- and four-species mixtures. Our analysis provides mechanistic insights into the potent roles of functional trait attributes in determining the magnitude (and even directionality) of the biodiversity-ecosystem functioning relationship in forest ecosystems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".