Synthesis of highbush blueberry pollination research reveals region‐specific differences in the contributions of honeybees and wild bees
Bibliographic record
Abstract
Abstract Highbush blueberry production has expanded worldwide in recent decades. To safeguard future yields, it is essential to understand if insect pollination is limiting current blueberry production and which insects contribute to pollination in different production regions. We present a systematic review including a set of meta‐analyses on insect‐mediated pollination in highbush blueberry. We summarize the geographic distribution of research, the abundance of different pollinator taxa and their relative pollination contributions. Using raw data from 21 studies, totalling 496 site replicates, we determine the degree of pollination service and pollen limitation (i.e. combining open pollination levels with experimental bagged and/or hand pollination treatments), as well as the contribution of honeybees and wild bees to pollination (i.e. observational, open pollination). Most studies originate from North America, focusing on only a few cultivars. Honeybees are the dominant pollinator, and wild bees are occasionally abundant. Wild bees are more efficient pollinators on a single‐visit basis compared to honeybees, which increases their relative pollination contribution compared to their relative abundance. Insect‐mediated pollination services increased blueberry fruit set, berry weight and seed set ( R 2 values: 64.8%, 75.9% and 75.2% respectively). We often detected pollen limitation, indicated by an increase in fruit set, berry weight and seed set ( R 2 : 10.1%, 18.2% and 21.5%, respectively), with additional hand pollination. Increasing visitation of honeybees and wild bees contributed to blueberry pollination by increasing fruit set ( R 2 : 5.4% and 3.5%), berry weight ( R 2 : 6.5% and 2.8%) and seed set ( R 2 : 6.4% and 3.8%) respectively. Bee contributions to fruit set and berry weight were variable across regions. Synthesis and application : A diverse community of insects, primarily bees, contributes to highbush blueberry pollination and yield. However, pollination deficits are common. The finding that both honeybees and wild bees enhance pollination highlights the possibility of adopting different management strategies that utilize honeybees, wild bees or both depending on the specific context and region. This further emphasizes the general importance of conserving pollinator health and diversity. Our synthesis highlights data gaps and areas for future research to better understand the pollination contribution of different pollinators to crops that are expanding globally.
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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 | 0.001 | 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".