Large Remaining Forest Habitat Patches Help Preserve Wild Bee Diversity in Cultivated Blueberry Bush
Bibliographic record
Abstract
Global declines in wild and managed bee populations represent a major concern for the agricultural industry. Such declines result, in part, from the loss of natural and semi-natural habitats in and around agricultural ecosystems. However, remaining forest patches in heavily modified landscapes represent nesting habitats that may be crucial to preserving wild bees and their services. Because wild bees are the main pollinators of fruit crops, preserving potential nesting habitats might be particularly important for the crops’ yield and profitability. Here, we assessed whether the abundance and richness of visiting wild bees in blueberry crops relates to the amount of surrounding forest cover and if so, whether those relationships varied with spatial scale. Specifically, we sampled wild bee communities in 18 blueberry fields during the blooming period in Montérégie, Quebec, Canada, where sampling consisted of pan trap triplets and direct observation of flower visitors on blueberry bushes. Then, we quantified the proportion of forest in radii of 0.5 km, 1 km, and 2 km around each field. Wild bee abundance was positively related to the proportion of forest habitat surrounding the crop field, but the relationship for wild bee richness was less clear. Moreover, these relationships were strongest at 1 and 2 km radii of measured land cover. Overall, pollinator diversity was highest when at least 30% of the surrounding landscape consisted of forest patches, representing a total area of at least 1 km2. Our results suggest that preserving large habitat patches in agricultural landscapes can help prevent further decline in wild bee diversity while maximizing pollination services to fruit crops.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".