Trapline foraging by bumble bees: V. Effects of experience and priority on competitive performance
Bibliographic record
Abstract
Animals collecting resources that are fixed in space but replenish over time, such as floral nectar and pollen, often establish small foraging areas to which they return faithfully. Some repeatedly visit a set of patches in a significantly predictable sequence (so-called “trapline foraging”), which may allow them to focus on more profitable patches in their foraging areas. The functional significance of trapline foraging itself, however, has not been empirically demonstrated, especially in competitive situations. We conducted laboratory experiments with artificial flowers to test whether and how accumulated foraging experience in bumble bees affects their movement patterns and foraging performance in the presence of competition. Experienced bees with prior access to flowers achieved higher rates of nectar intake than did later arrivals because they traveled faster between flowers and returned to flowers at more regular intervals. These behavioral skills improved foraging performance in competitive situations in 2 ways: nectar that accumulated in flowers could be harvested before its replenishment rate slowed down, and nectar could be taken before the arrival of a competitor. In each foraging trip, however, bees traveled more slowly as they followed more repeatable routes. Despite this trade-off between speed and accuracy in traplining, bees constantly upgraded both skills as they gained experience from trip to trip. This upgrading still occurred in the absence of a competitor. Foraging area fidelity thus allowed bumble bees to establish long-term spatial memory required for fast movements and accurate traplining and, in turn, increased their foraging performance in competition with less experienced individuals.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".