Gaming the system: how predators induce prey to make themselves more vulnerable
Bibliographic record
Abstract
We describe a natural situation that supports predictions of theoretical scenarios in which predators tactically influence the food - safety trade-off faced by prey to increase their vulnerability. By using low-cost ‘false attacks’ or otherwise advertising their presence, predators force prey to spend time in refuges or in other forms of safety-enhancing behavior, during which foraging is impaired or impossible. Prey must compensate by taking extra risks at other times or places to meet their energy requirements, and as a consequence become easier to capture. We used data on the occurrence of over-ocean flocking (OOF) by Pacific dunlins (Calidris alpina pacifica), and on the timing and success of attacks by peregrines. OOF is a safe but energetically expensive alternative to traditional roosting, and largely replaced the latter in Boundary Bay of southwest British Columbia as the presence of wintering peregrines rose during the 1990s. Peregrines appear to use ‘false’ or ‘non-serious’ attacks to shift the occurrence of OOF to a tidal time frame earlier than is ideal for dunlins, thereby creating later hunting opportunities during which dunlins were vulnerable than otherwise would have been the case. The shift increased dunlin mortality substantially. Tactics used by predators such as prominent perching, salient signals and unpredictable appearances, could have evolved because this forces prey to increase their level of caution, rendering them more vulnerable at other times or places.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".