The Evolution of Warning Signals as Reliable Indicators of Prey Defense
Bibliographic record
Abstract
It is widely argued that defended prey have tended to evolve conspicuous traits because predators more readily learn to avoid defended prey when they are conspicuous. However, a rival theory proposes that defended prey have evolved such characters because it allows them to be distinguished from undefended prey. Here we investigated how the attributes of defended (unprofitable) and undefended (profitable) computer-generated prey species tended to evolve when they were subject to selection by foraging humans. When cryptic forms of defended and undefended species were similar in appearance but their conspicuous forms were not, defended prey became conspicuous while undefended prey remained cryptic. Indeed, in all of our experiments, defended prey invariably evolved any trait that enabled them to be distinguished from undefended prey, even if such traits were cryptic. When conspicuous mutants of defended prey were extremely rare, they frequently overcame their initial disadvantage by chance. When Batesian mimicry of defended species was possible, defended prey evolved unique traits or characteristics that would make undefended prey vulnerable. Overall, our work supports the contention that warning signals are selected for their reliability as indicators of defense rather than to capitalize on any inherent educational biases of predators.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".