Food as Medicine: A Review of Plant Secondary Metabolites from Pollen, Nectar, and Resin with Health Benefits for Bees
Bibliographic record
Abstract
Bees rely on pollen and nectar for nutrition, but floral products provide more than just macronutrients; many also contain an array of plant secondary metabolites (PSMs). These compounds are generally thought to serve primarily defensive purposes but also appear to promote longevity and immune function, protect against disease agents, and detoxify toxicants. This review presents a comprehensive overview of PSMs, as well as some fatty acids, with documented health benefits for eusocial bees at ecologically relevant exposure levels and the plant species whose floral products and/or resin are known to contain them. We find medicinal metabolites to be widespread but unevenly distributed across the plant phylogeny, with a few families containing a majority of the species known to produce PSMs with documented health benefits. We discuss the current state of knowledge and identify gaps in our understanding. The existing literature on the health benefits of metabolites, and particularly PSMs, to bees is spread across multiple fields; our hope is that this review will bring these fields closer together and encourage further investigation of the role of metabolites in promoting bee health in ecological contexts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".