Floral strips adjacent to rotationally managed crop fields significantly increase nesting density and support pollen foraging of leafcutter bees
Bibliographic record
Abstract
1. Bees in many regions of the world are in decline with strong evidence that agricultural practices are to blame. Although wildflower enhancements adjacent to croplands have proven effective at increasing wild bee abundance and diversity, how these enhancements can improve the diet and nesting density of bees remains poorly known. 2. We placed hollow reeds next to floral strips, control fields, and semi-natural areas in Manitoba from 2019–2021 to determine how pollen utilization and provisioning density of stem-nesting leafcutter bees ( Megachile ) differed between treatments. 3. We found that strip sites significantly increased nesting density of Megachile when compared to the other treatments. Strip sites contained the most pollen species, including 30 % of species sown into the floral strip itself, suggesting that Megachile use the strip for pollen provisioning. 4. At the landscape scale, nesting density of Megachile responded positively to the amount of rewarding agriculture (crops frequently visited by insects for floral rewards). However, the number of offspring per nest responded negatively to rewarding agriculture and local Shannon vegetation diversity, indicating that Megachile create more nests with fewer offspring in areas of large-scale flowering crops or higher vegetation diversity. Possible reasons for this are discussed. 5. Our research supports the idea that floral strips increase beneficial insect abundance and that floral strips supplement pollen foraging of bees.
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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 | 0.000 | 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) | 0.002 | 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".