Trapline foraging by bumble bees: VI. Behavioral alterations under speed–accuracy trade-offs
Bibliographic record
Abstract
Trapline foraging (repeated sequential visits to a series of feeding locations) has often been observed in animals collecting floral resources. Past experiments have shown that bumble bees cannot always develop accurate (i.e., repeatable) traplines to a sufficient level, despite their economic advantages in many situations. The bees' preference for short flights works against developing accurate traplines when plants or patches are distributed in zigzag fashion. How should bees cope with such situations in nature? We conducted laboratory experiments with artificial flowers to test 2 nonexclusive hypotheses: bees may travel faster to compensate for low traplining accuracy, and when local landmarks are available, bees may be able to develop traplines by remembering external spatial information in addition to the locations of flowers. As predicted, foragers on a zigzag-shaped floral array traveled faster, with lower route repeatability, than those on a triangular lattice where distance and angle could be chosen independently, suggesting that bees trade-off accuracy for speed when it is more feasible. In contrast, bees traveled more slowly with unchanged traplining accuracy when landmarks were added into both arrays, possibly because the landmarks caused information load or visual distraction. Finally, bees on the zigzag array with additional landmarks made a quicker decision to switch from accurate traplining to fast traveling. If landmarks helped the bees to grasp the overall array geometry in our experiments, they may also permit bees in nature to select a distribution of plants or patches that aids accurate traplining.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".