Trapline foraging by bumble bees: IV. Optimization of route geometry in the absence of competition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 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 it