Distinct pollinator communities persist among co-flowering specialty crops in Indiana
Bibliographic record
Abstract
Diverse bee communities pollinate fruits and vegetables, and the composition of these communities has been described for many specialty crops in major production regions. However, pollinator communities in landscapes dominated by agronomic crops may differ in species composition and the contribution of wild bees. With over 4 million ha of maize and soybean, Indiana presents a novel landscape to compare pollinator communities among specialty crops that differ in their use of managed bees and bloom phenology, with potential implications for the composition of wild bee communities and their contributions to flower visitation. We sampled pollinator communities with flower observations and pan traps in spring-blooming apples and blueberries and summer-blooming tomatoes and watermelons, allowing us to compare communities between specialty crops with overlapping and distinct bloom times. Apples, blueberries, and watermelons were stocked with honey bees, and watermelons additionally had managed bumble bees. Across two years of sampling, we observed 1,651 flower visits by 13 taxa, collected 1,967 bees, and identified 84 taxa from collected specimens. Apples and blueberries hosted the richest pollinator communities (34 and 51 taxa, respectively) followed by watermelons (22 taxa), and tomatoes (19 taxa). While 70% of flower visits in apples and blueberries were attributed to honey bees, wild bees accounted for 82% and 99% of flower visits in watermelons and tomatoes, respectively. Our results indicate that distinct pollinator communities persist among co-flowering specialty crops in a landscape dominated by maize and soybean production and underscore the contribution of wild bees for specialty crop pollination, particularly in watermelons and tomatoes.
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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 | 0.001 | 0.001 |
| 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".