Diversity and identity of economics traits determine the extent of tree mixture effects on ecosystem productivity
Bibliographic record
Abstract
Abstract Although both observational and experimental studies have shown that positive tree species diversity–productivity relationships are predominant in global forests, weak or the lack of tree species diversity and productivity relationships also exist. Growing evidence has revealed that ecosystem productivity depends more on the functional characteristics of species than on their number. However, exactly to what extent tree diversity effects on ecosystem productivity are influenced by the variability and composition of functional traits have rarely been tested both across and at given species richness (SR) levels. Here, we employed a meta‐analysis of global‐scale data from 59 tree diversity experiments to examine how the diversity and community‐weighted means (CWMs) of economics traits determine the outcomes of tree mixture effects on productivity across and within SR levels. We found that the positive effects of tree mixtures on productivity were strengthened by the increasing multidimensional functional dispersion (FDis) and the CWM of leaf nitrogen content both across and within two‐ and four‐species mixtures. Moreover, the multidimensional FDis and the CWM of leaf nitrogen content increased the complementarity effect rather than the selection effect. Synthesis . Our findings suggest that both diversity in the leaf economics spectrum and trait concentration on the ‘fast’ end of the spectrum strengthen biodiversity–ecosystem functioning relationships. This study provides mechanistic insights into the potent roles of plant economics traits, especially leaf nitrogen content, in determining the magnitude (and even directionality) of the biodiversity–ecosystem functioning relationships in forest ecosystems.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".