Global meta-analysis shows that threatened flowering plants have higher pollination deficits
Bibliographic record
Abstract
Most flowering plant species rely on animal pollinators to reproduce, but insufficient pollen receipt, or pollen limitation, commonly occurs and is mediated by plant traits. Pollen limitation could either exacerbate extinction threat or arise as a consequence of population and range declines in threatened plants, leading to the expectation that pollen limitation should be higher in threatened compared to non-threatened plants. To test this, we perform a meta-analysis on a global dataset of pollen limitation from 2633 pollen supplementation experiments, integrating plant threat status and thirteen reproduction and life history traits. Threatened plant species have 26% higher levels of pollen limitation than non-threatened species. This pattern is moderated by plant traits and geographic location: we find higher levels of pollen limitation for threatened compared to non-threatened species for pollinator-dependent plants and for plants found in Asia and temperate zones. Using path analysis, we find that plant traits, study region, and threat status are causally linked to pollen limitation. We suggest that plant traits such as autofertility, which strongly predict pollen limitation, should be considered in global databases on plant threat. Further, preventing pollen limitation through habitat and pollinator management is a promising path to preventing plant extinction. Insufficient pollen reception, pollen limitation, could exacerbate the threat of extinction or be a consequence of decline in threatened plants. Here, the authors conduct a meta-analysis on pollen limitation studies to find that threatened plants show stronger pollen limitation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".