Does pollen limitation limit plant ranges? Evidence and implications
Bibliographic record
Abstract
Sexual reproduction often declines towards range edges, reducing fitness, dispersal and adaptive potential. For plants, sexual reproduction is frequently limited by inadequate pollination. While case studies show that pollen limitation can limit plant distributions, the extent to which pollination commonly declines towards plant range edges is unknown. Here, we use global databases of pollen-supplementation experiments and plant occurrence data to test whether pollen limitation increases towards plant range edges, using a phylogenetically controlled meta-analysis. While there was significant pollen limitation across studies, we found little evidence that pollen limitation increases towards plant range edges. Pollen limitation was not stronger towards the tropics, nor at species' equatorward versus poleward range limits. Meta-analysis results are consistent with results from targeted experiments, in which pollen limitation increased significantly towards only 14% of 14 plant range edges, suggesting that pollination contributes to range limits less often than do other interactions. Together, these results suggest pollination is one of the rich variety of potential ecological factors that can contribute to range limits, rather than a generally important constraint on plant distributions. This article is part of the theme issue 'Species' ranges in the face of changing environments (part I)'.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".