Drivers of pollen limitation: macroecological interactions between breeding system, rarity, and diversity
Bibliographic record
Abstract
Background: Plant species in biodiversity hotspots suffer more from pollen limitation than those in lower diversity regions, though this pattern is largely restricted to self-incompatible species. It is unknown whether higher pollen limitation is due to increased pollinator sharing or declines in pollinator abundance. Aims: Macroecological examinations of pollen limitation have been challenged by statistical confounds of phylogenetic non-independence and interrelationships between variables. Here, we perform phylogenetically corrected analyses of pollen limitation, examining an ensemble dataset of endemicity, abundance, species diversity, breeding system, floral symmetry, and pollinator richness. Methods: We apply model selection and path analysis to a large dataset of published studies of pollen limitation on 275 plant species distributed worldwide. Results: Plant diversity and breeding system were included in the best model. Even the best model explained only 13% of the among-species variation in pollen limitation, indicating a stochastic component in pollen receipt. Pollinator richness remained a consistent determinant of pollen limitation, influenced by floral symmetry and, to a lesser extent, plant diversity. Conclusions: Our results suggest that many traits examined thus far explain relatively little of the variation in pollen limitation, partly because their effects are subsumed by the roles played by breeding system and plant diversity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".