Global synthesis of apple pollination research highlights general pollen limitation and positive contributions of wild bees compared to honeybees
Bibliographic record
Abstract
Abstract Apple is one of the most important pollinator‐dependent fruit crops worldwide. To secure high‐quality yields, it is crucial to know which, and to what extent, pollinating insects contribute to its pollination success as measured by fruit set, fruit weight and seed set. We perform a meta‐analysis of field studies conducted across multiple orchards on insect‐mediated pollination in apple cultivation, using raw data from 29 studies, totalling 532 orchard replicates. We assessed the extent of pollen limitation on different pollination outcomes and assessed the contribution of honeybees, wild bees and bee species richness to apple pollination. Across all studies, we detected strong evidence of pollen limitation for fruit set and seed set, but not for fruit weight. Honeybees were the most abundant flower visitors (average relative visitation of 71.9%) compared to wild bees; but when correcting for their pollination efficiency, the relative pollination contribution of honeybees was lower compared to their relative visitation (vice versa for solitary bees). We conclude that honeybee visitation rate did not influence fruit or seed set; yet increasing honeybee visitation had a small, negative effect on fruit weight. Fruit set was not influenced by wild bee visitation rate, whereas wild bee visitation had a small, but clear positive effect on fruit weight and seed set. Bee species richness had a small, positive effect on seed set; whereas it did not affect fruit set and fruit weight. Syntheses and applications. Our study highlights that pollen limitation is common in this global crop. While managed honeybees are dominant pollinators, a diverse community of wild bees contributes significantly to apple pollination and high‐quality yield. The positive effect of wild bees and species richness on fruit weight and seed set demonstrates that wild bee pollination results in better‐quality fruit production (increased weight & seed set). Therefore, our synthesis highlights the importance of conserving pollinator diversity to maintain pollination services. The absence of a clear effect of honeybee visitation rate on fruit and seed set, coupled with its negative impact on fruit weight, suggests a need for further optimisation of honeybee management to improve the cost‐efficiency of pollination management.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".