Phylogenetically diverse grasslands are associated with pairwise interspecific processes that increase biomass
Bibliographic record
Abstract
Biodiversity is an important determinant of primary productivity in experimental ecosystems. We combine two streams of research on understanding the effects of biodiversity on ecosystem function: quantifying phylogenetic diversity as a predictor of biodiversity effects in species-rich systems and the contribution of pairwise interspecific interactions to ecosystem function. We developed a statistical model that partitions the effect of biodiversity into effects due to community phylogenetic diversity and other community properties (e.g., average pairwise interaction, between- and within-functional-group effects, and so forth). The model provides phylogenetically based species-level explanations of differences in ecosystem response for communities with differing species composition. In two well-known grassland experiments, the model approach provides a parsimonious description of the effects of diversity as being due to the joint effect of the average pairwise statistical interaction and to community phylogenetic diversity. Effects associated with functional groupings of species in communities are largely explained by community phylogenetic diversity. The model approach quantifies a direct link between a measure of the evolutionary diversity of species and their interactive contribution to ecosystem function. It proves a useful tool in developing a mechanistic understanding of variation in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".