Bee Preference for Native versus Exotic Plants in Restored Agricultural Hedgerows
Bibliographic record
Abstract
Habitat restoration to promote wild pollinator populations is becoming increasingly common in agricultural lands. Yet, little is known about how wild bees, globally the most important wild pollinators, use resources in restored habitats. We compared bee use of native and exotic plants in two types of restored native plant hedgerows: mature hedgerows (>10 years from establishment) designed for natural enemy enhancement and new hedgerows (≤2 years from establishment) designed to enhance bee populations. Bees were collected from flowers using timed aerial netting and flowering plant cover was estimated by species using cover classes. At mature hedgerow sites, wild bee abundance, richness, and diversity were greater on native plants than exotic plants. At new sites, where native plants were small and had limited floral display, abundance of bees was greater on native plants than exotic plants; but, controlling for floral cover, there was no difference in bee diversity and richness between the two plant types. At both mature and new hedgerows, wild bees preferred to forage from native plants than exotic plants. Honey bees, which were from managed colonies, also preferred native plants at mature hedgerow sites but exhibited no preference at new sites. Our study shows that wild bees, and managed bees in some cases, prefer to forage on native plants in hedgerows over co‐occurring weedy, exotic plants. Semi‐quantitative ranking identified which native plants were most preferred. Hedgerow restoration with native plants may help enhance wild bee abundance and diversity, and maintain honey bee health, in agricultural areas.
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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) | 0.001 | 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".