Identifying wild bee visitors of major crops in North America with notes on potential threats from agricultural practices
Bibliographic record
Abstract
Considering the critical importance of insect pollination to food security and documented declines in wild bee populations, it is imperative to develop effective conservation and management strategies that promote the health of wild bee communities associated with agroecosystems. Identifying wild bee visitors of crops, including crop-flower visitors and species that nest within cropping areas, may prove critical to this endeavor as optimal conservation strategies may differ among bee species and/or guilds, regions, and cropping systems. Although lists of bee species that are associated with North American crops are scattered throughout the literature, there is a need for a comprehensive compilation of those species by crop, region, and nesting guild. Here, we searched the literature to compile a list of wild bee species associated with 33 major crops in North America and assessed the overlap in bee species communities among crops and regions. Of the 739 crop-associated bee species retrieved, 405 species (54.8%) were ground nesters, and 438 species (59.3%) were identified as crop-flower visitors of at least one crop. Because of their nesting and foraging behavior, we argue that these species are more likely to be exposed to agricultural pesticide residues than bee species that do not nest in the ground or feed directly on crop flowers. We further compiled lists of wild bees that have been found to be associated with all of the four most surveyed perennial fruit crops (apple, blueberry, cranberry, strawberry) and all of the three most surveyed cucurbit crops (cucumber, Cucurbita , watermelon) in eastern North America. These lists of bee species can be used to focus attention on species needing better protection strategies within agroecosystems, especially for these two important North American crop groups and can also inform the development of multi-species pesticide risk assessment schemes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".