The pollination ecology of highbush blueberry (Vaccinium corymbosum) in British Columbia
Bibliographic record
Abstract
Agricultural systems often support low beneficial insect diversity because they reduce habitat quality. Agricultural management increases landscape homogeneity resulting in low habitat and resource diversity. Crops that rely on wild pollinators for fruit production or predators and parasitoids for pest control may lose access to these services as the agroecosystem becomes increasingly managed. I used yield data from pollination experiments conducted over four years, along with insect surveys, to better understand the dynamics between insect communities in agroecosystems and their use of the agricultural landscape in highbush blueberry (Vaccinium corymbosum) in the Fraser Valley of southern British Columbia, Canada. Regional land use was identified as being an important component in structuring beneficial insect communities. Semi-natural habitat, such as pasture or fallow, was found to support greater abundances and diversity of all beneficial insects. Land use with greater disturbance, like conventional non-flowering agriculture, reduced pollinator species richness but increased the abundance of generalist predators. The differences between groups in their response to land use types might be driven by variability in access to resources (ex. floral resources or pest insects) in the larger agricultural landscape. However, surrounding landscape composition did not affect blueberry yield deficit, which was instead determined primarily by bumble bee visits and minimum daily temperatures. This finding highlights the importance of weather conducive to pollinator foraging for crop production. Despite the importance of bumble bees for reducing yield deficit, experimental introduction of two managed bumble bee species did not mitigate these deficits. Differences in bumble bee species characteristics associated with reproduction predicted pollen forager recruitment, which when coupled with differences in foraging preferences (blueberry pollen comprised 50% of pollen loads in one species, but less than 20% in the other and in managed honey bees) provides some insight into which managed species is best suited for further commercial development. My results highlight the complexity associated with predicting crop pollination levels and demonstrate how the impact of wild insects on production will vary with surrounding land-use, species characteristics, and abiotic factors. In crops highly reliant on wild pollinators, like highbush blueberry, understanding the needs of beneficial insects may allow farmers to modify practices to improve ecosystem services.
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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 | 0.001 |
| 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".