Dietary foundations for pollinators: nutritional profiling of plants for bee health
Bibliographic record
Abstract
Human activities, including urban expansion, intensive farming practices, and the application of pesticides have significantly reshaped bee habitats. Understanding the nutritional content of pollen, the primary source of bees’ proteins and lipids, is important for maintaining their diet and health. In this study, we set out to determine the nutritional composition of pollen from various plant families and genera. Our objectives were to analyze the levels of non-esterified fatty acids (NEFAs), amino acids (AAs), protein-to-lipid (P:L) ratios, and omega-6:3 ratios of 57 pollen species native to North America. These data suggest a potential trade-off between NEFA and AA content within pollen, suggesting that a diverse floral diet may benefit bees more than a single pollen source. The AA profiles showed considerable diversity, with all pollen species providing the essential amino acids (EAAs) required for bee health, except for methionine which was lacking in Rhus glabra pollen. The plant family Asteraceae was especially abundant in EAAs. P:L ratios varied widely further emphasizing bees’ need for access to a diverse array of nutritional profiles. There were no overall nutritional differences between pollen from native and introduced plant species. This study highlights the significance of a diverse array of floral resources to meet the comprehensive nutritional needs of bees, contributing to the support of pollinator populations and the broader ecological system.
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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 | 0.000 |
| 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) | 0.000 | 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".