Pollen defenses negatively impact foraging and fitness in a generalist bee (Bombus impatiens: Apidae)
Bibliographic record
Abstract
Plants may benefit from limiting the community of generalist floral visitors if the species that remain are more effective pollinators and less effective pollenivores. Plants can reduce access to pollen through altered floral cues or morphological structures, but can also reduce consumption through direct pollen defenses. We observed that Eucera (Peponapis) pruinosa, a specialist bee on Cucurbita plants, collected pure loads of pollen while generalist honey bees and bumble bees collected negligible amounts of cucurbit pollen, even though all groups of bees visited these flowers. Cucurbit flowers have no morphological adaptations to limit pollen collection by bees, thus we assessed their potential for physical, nutritional, and chemical pollen traits that might act as defenses to limit pollen loss to generalist pollinators. Bumble bee (Bombus impatiens) microcolonies experienced reduced pollen consumption, mortality, and reproduction as well as increased stress responses when exposed to nutritional and mechanical pollen defenses. These bees also experienced physiological effects of these defenses in the form of hindgut expansion and gut melanization. Chemical defenses alone increased the area of gut melanization in larger bees and induced possible compensatory feeding. Together, these results suggest that generalist bumble bees avoid collecting cucurbit pollen due to the physiological costs of physical and chemical pollen defenses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".