Explaining maximum variation in productivity requires phylogenetic diversity and single functional traits
Bibliographic record
Abstract
Many community experiments have shown a positive relationship between plant biodiversity and community productivity, with biodiversity measured in multiple ways based on taxonomy, function, and phylogeny. Whether these different measures of biodiversity and their interactions explain variation in productivity in natural assemblages has rarely been tested. In a removal experiment using natural alpine assemblages in the Tibetan Plateau, we manipulated species richness and functional diversity to examine how different measures of biodiversity predict aboveground biomass production. We combined different biodiversity measures (functional, phylogenetic, richness, evenness) in generalized linear models to determine which combinations provided the most parsimonious explanations of variation in biomass production. Although multivariate functional diversity indices alone consistently explained more variation in productivity than other single measures, phylogenetic diversity and plant height represented the most parsimonious combination. In natural assemblages, single metrics alone cannot fully explain ecosystem function. Instead, a combination of phylogenetic diversity and traits with weak or no phylogenetic signal is required to explain the effects of biodiversity loss on ecosystem function.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".