Macronutrient composition in pollen affects development and survival in wild bees
Bibliographic record
Abstract
Pollinators rely on diverse floral resources to meet their nutritional needs, as pollen macronutrient composition varies significantly among plant species, influencing bee development and survival. In this study, we collected freshly laid eggs of the small carpenter bee (Ceratina calcarata) from artificial nest substrates and reared them on diets formulated with black poplar (Populus nigra) and dandelion (Taraxacum officinale) pollen, while closely monitoring developmental metrics, lipid content, and survival. We also conducted macronutrient analyses on both pollen types to quantify protein, amino acids, fatty acids, and sugars. While dandelion pollen is abundant and carbohydrate-rich, it contained lower levels of protein, essential amino acids, and several fatty acids in our analyses, a nutrient profile that corresponded with slower larval development in our study. In contrast, black poplar pollen despite being primarily wind-dispersed had higher concentrations of protein, essential amino acids, and fatty acids in our analyses and supported better developmental performance. Bees reared on black poplar pollen exhibited faster maturation, larger head widths, and greater body mass, highlighting the possible role of protein availability in development. Survival analysis further revealed that protein-rich diets supported higher brood viability, whereas bees fed dandelion pollen experienced delayed development and reduced body sizes. Bees on 50:50 mixed diets displayed intermediate development times and improved survival, highlighting the benefits of dietary diversity in mitigating nutritional stress. These findings emphasize the importance of arboreal pollen as a possible nutritional resource and the potential to incorporate tree pollen sources into conservation planning. Given the increasing pressures of habitat loss and climate change, ensuring access to nutritionally diverse pollen sources may be useful for sustaining pollinator populations and promoting ecosystem resilience.
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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 | 0.000 | 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 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".