Pollination by wild bees yields larger strawberries than pollination by honey bees
Bibliographic record
Abstract
Abstract A diverse array of wild bee species may provide more effective pollination than the widely employed European honey bee ( Apis mellifera L.). High species richness within crop pollinator assemblages has been linked to enhanced fruit and seed yields, but species richness is often confounded with abundance in studies of pollinator communities. We investigated the effects of bee diversity and species identity on pollen deposition and crop yield in the strawberry ( Fragaria × ananassa ) variety Jewel through a field experiment that independently manipulated the species richness and abundance of flower visitors. We used a new pollen deposition measurement technique to determine the pollen contribution of individual bees in an assemblage of flower visits. We compared the performance of wild bee species and managed honey bees, as pollinators of strawberry. We also calculated the influence of species richness, visit frequency and visitor identity on fruit mass, using the fruit that developed from each sampled flower. Species richness of flower visitors did not influence floral pollen loads or strawberry mass. Honey bees and wild bees deposited the same amount of pollen per visited flower. However, strawberries that developed from flowers visited by wild bees were heavier than flowers visited by honey bees. In addition, flowers visited by a combination of wild and honey bees produced strawberries that weighed less than flowers receiving purely WB visits. Synthesis and applications . Our findings show that honey bee pollination results in lower yields than wild bee pollination in a strawberry crop. Consequently, if managed honey bees in strawberry fields displace wild pollinators, growers may obtain suboptimal yields. Management efforts aimed at the maintenance or enhancement of wild pollinator populations may therefore be a cost‐effective way to increase both crop yield and biodiversity on strawberry farms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".