Flower Choice and Learning in Foraging Bumblebees: Effects of Variation in Nectar Volume and Concentration
Bibliographic record
Abstract
Abstract Bees collect food from flowers that differ in morphology, color, and scent. Nectar‐seeking foragers can rapidly associate a flower's cues with its profitability, measured as caloric value or ‘net energy gain,’ and generally develop preferences for more profitable species. If two flower types are equally easy to discover and feed from, differences in profitability will arise from differences in the volume or the sugar concentration of their nectar crops. Although there has been much study of how bees respond to one or the other of these two kinds of nectar variation, few studies have considered both at once. We presented free‐foraging bumblebees with two different types of equally rewarding artificial flowers. After a period of familiarization, we made one type more rewarding than the other by increasing its nectar concentration, volume, or both. Bees responded more rapidly to a change in the reward's sugar concentration than to a change in its volume, even if the profitability differences were approximately equal. Sucrose concentration differences (40% vs. 13%) caused bees to virtually abandon the more dilute flower type, whether both types offered the same volume (2 μ l) or the less concentrated reward offered higher volume (7 μ l vs. 0.85 μ l). When the two types of flower differed only in nectar volume (7 μ l vs. 0.85 μ l), the less rewarding type continued to receive 22% of the visits. We propose three different hypotheses to explain the stronger response of the bees to changes in sugar concentration: (i) their response threshold to sucrose concentration might change; (ii) less time is needed to assess the concentration of a reward than its volume; and (iii) a smaller sample size may be needed for reliable estimation of profitability when flowers differ in concentration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".