The Role of Predators in Maintaining the Geographic Organization of Aposematic Signals
Bibliographic record
Abstract
Selective predation of aposematic signals is expected to promote phenotypic uniformity. But while these signals may be uniform within a population, numerous species display impressive variations in warning signals among adjacent populations. Predators from different localities who learn to avoid distinct signals while performing intense selection on others are thus expected to maintain such a geographic organization. We tested this assumption by placing clay frog models, representing distinct color morphs of the Peruvian poison dart frog Ranitomeya imitator and a nonconspicuous frog, reciprocally between adjacent localities. In each locality, avian predators were able to discriminate between warning signals; the adjacent exotic morph experienced up to four times more attacks than the local one and two times more than the nonconspicuous phenotype. Moreover, predation attempts on the exotic morph quickly decreased to almost nil, suggesting rapid learning. This experiment offers direct evidence for the existence of different predator communities performing localized homogenizing selection on distinct aposematic signals.
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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 | 0.001 |
| 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".