Trapline foraging by bumble bees: IV. Optimization of route geometry in the absence of competition
Bibliographic record
Abstract
Foraging on resources that are fixed in space but that replenish over time, such as floral nectar and pollen, presents animals with the problem of selecting a foraging route. What can flower visitors such as bees do to optimize their foraging routes, that is, reduce return time or route distance? Some repeatedly visit a set of plants in a significantly predictable sequence (so-called “trapline foraging”), which may also enhance their foraging efficiency. A moderate level of optimization and repetition of foraging routes can be reached by following simple movement rules for choosing the distances and turning angles of successive flights, without the use of spatial memory. If pollinators can learn the locations of patches and choose among possible foraging routes or paths, however, even better performance may be achieved. We tested whether and how bumble bees can optimize and repeat their foraging routes in laboratory experiments with artificial flowers that secreted nectar at a constant rate. With increasing experience, foraging routes of bees became more repeatable and efficient than expected from a combination of simple movement rules between successive flowers. We suggest that trapline foraging is a more sophisticated pattern of spatial use than searching and is based on memory. On the other hand, certain spatial configurations of flowers hampered optimization by the bees; bees preferred to choose short distances over straight moves and showed little plasticity in this regard. Developing an efficient trapline, therefore, may require prior selection of a set of plants with an appropriate spatial configuration.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".