Complementary crops and landscape features sustain wild bee communities
Bibliographic record
Abstract
Wild bees, which are important for commercial pollination, depend on floral and nesting resources both at farms and in the surrounding landscape. Mass-flowering crops are only in bloom for a few weeks and unable to support bee populations that persist throughout the year. Farm fields and orchards that flower in succession potentially can extend the availability of floral resources for pollinators. However, it is unclear whether the same bee species or genera will forage from one crop to the next, which bees specialize on particular crops, and to what degree inter-crop visitation patterns will be mediated by landscape context. We therefore studied local- and landscape-level drivers of bee diversity and species turnover in apple orchards, blueberry fields, and raspberry fields that bloom sequentially in southern Quebec, Canada. Despite the presence of high bee species turnover, orchards and small fruit fields complemented each other phenologically by supporting two bee genera essential to their pollination: mining bees (Andrena spp.) and bumble bees (Bombus spp.). A number of bee species specialized on apple, blueberry, or raspberry blossoms, suggesting that all three crops could be used to promote regional bee diversity. Bee diversity (rarefied richness, wild bee abundance) was highest across crops in landscapes containing hedgerows, meadows, and suburban areas that provide ancillary nesting and floral resources throughout the spring and summer. Promoting phenological complementarity in floral resources at the farmstead and landscape scales is essential to sustaining diverse wild bee populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".