Functional rarity and evenness are key facets of biodiversity to boost multifunctionality
Bibliographic record
Abstract
The functional traits of organisms within multispecies assemblages regulate biodiversity effects on ecosystem functioning. Yet how traits should assemble to boost multiple ecosystem functions simultaneously (multifunctionality) remains poorly explored. In a multibiome litter experiment covering most of the global variation in leaf trait spectra, we showed that three dimensions of functional diversity (dispersion, rarity, and evenness) explained up to 66% of variations in multifunctionality, although the dominant species and their traits remained an important predictor. While high dispersion impeded multifunctionality, increasing the evenness among functionally dissimilar species was a key dimension to promote higher multifunctionality and to reduce the abundance of plant pathogens. Because too-dissimilar species could have negative effects on ecosystems, our results highlight the need for not only diverse but also functionally even assemblages to promote multifunctionality. The effect of functionally rare species strongly shifted from positive to negative depending on their trait differences with the dominant species. Simultaneously managing the dispersion, evenness, and rarity in multispecies assemblages could be used to design assemblages aimed at maximizing multifunctionality independently of the biome, the identity of dominant species, or the range of trait values considered. Functional evenness and rarity offer promise to improve the management of terrestrial ecosystems and to limit plant disease risks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".