Selfish herd effects depend on prey crypsis
Bibliographic record
Abstract
Determining why some animals form groups while others remain solitary is a longstanding goal in behavioural ecology. Group formation can help mitigate predation risk through a variety of mechanisms, including risk dilution and group vigilance. The ‘selfish herd hypothesis’ proposes that prey can reduce their risk by minimizing the area around which all points in that area are closer to them than to another conspecific (i.e., by minimising their ‘domain of danger’). This hypothesis assumes that an individual’s predation risk is proportional to the size of its domain of danger, however, the relationship between risk and proximity to conspecifics may depend on additional factors. Specifically, approaching conspecifics may be costly for prey that rely on crypsis because group formation increases detectability. Using model prey, we experimentally manipulated prey colouration as well as the domain of danger, then tracked their ‘survival’ under natural field conditions. We found that an individual’s predation risk increased with their domain of danger for conspicuous (red) prey, but decreased with the domain of danger in cryptic (green) prey. Our results are consistent with patterns in natural systems and indicate that the relationship between predation risk and domain of danger depends on additional factors like prey colouration.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".