Bibliographic record
Abstract
Many foraging animals return to feeding sites to harvest replenishing resources, but little is known about efficient tactics for doing this. Can animals with adequate cognitive abilities increase their efficiency by modifying their behavior according to memories of past experience at particular sites? We developed a simulation model of animals harvesting renewable resources from isolated patches in undefended, competitive situations. We compared four foraging tactics: (1) moving stochastically without using any information from past experiences (random searching); (2) moving stochastically, but going longer distances after encountering lower reward (area-restricted searching); (3) repeatedly moving along a fixed route (complete traplining); and (4) traplining, but sampling and shifting to neighboring rewarding patches after encountering low reward (sample-and-shift traplining). Following Possingham, we tracked both the resources actually harvested by a focal forager (i.e., rewards) and the standing crops of resources that accumulated at patches. Complete traplining always produces less variation in elapsed time between visits than random searching or area-restricted searching, which has three benefits: increasing the reward crop harvested, if resource renews nonlinearly; reducing resource standing crop in patches; and reducing variation in reward crop per patch. Moreover, the systematic revisitation schedule produced by complete traplining makes it more competitive, regardless of resource renewal schedule or competitor frequency. By responding to their past experiences, using sample-and-shift traplining, foragers benefit only when many patches are left unvisited in the habitat. Otherwise, the exploratory component of sample-and-shift traplining, which increases the movement distance and the variation in elapsed time between visits, makes it more costly than complete traplining. Thus, traplining will usually be beneficial, but foragers should switch between “impatient” (sample-and-shift traplining) and “tenacious” (complete traplining) traplining, according to temporal changes in surrounding situations.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".