Optimal distributions of central-place foragers: honey bee foraging in a mass flowering crop
Bibliographic record
Abstract
Abstract The ideal-free distribution and central-place foraging are important ecological models that can explain the distribution of foraging organisms in their environment. However, this model ignores distance-based foraging costs from a central place (hive, nest), whereas central-place foraging ignores competition. Different foraging currencies and cooperation between foragers also create different optimal distributions of foragers, but are limited to a simple two-patch model. We present a hybrid model of the ideal-free distribution that uses realistic competitive effects although accounting for distance-based foraging, and test it using honey bees (Apis mellifera L.) foraging in canola fields (Brassica napus L.). Our simulations show that foragers maximizing efficiency (energy profits ÷losses) prioritize distance to their aggregation more than those maximizing net-rate (energy profits ÷time), and that social foragers move to more distant patches to maximize group benefits, meaning that social foragers do not approach an ideal-free distribution. Simulated efficiency-maximizers had a hump-shaped relationship of trip times with distance, spending shorter amounts of time in both nearby and far-away patches. Canola fields were far more attractive to simulated foragers than semi-natural areas, suggesting limited foraging on semi-natural lands during the bloom period of canola. Finally, we found that the observed distribution of honey bees in canola fields most closely resembled the optimal distribution of solitary efficiency-maximizers. Our model has both theoretical and practical uses, as it allows us to model central-place forager distributions in complex landscapes as well as providing information on appropriate hive stocking rates for agricultural pollination.
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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) | 0.000 | 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 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".