The role of learning by a predator, <i><scp>R</scp>ivulus hartii</i>, in the rare‐morph survival advantage in guppies
Bibliographic record
Abstract
Negative frequency-dependent selection (NFDS), where rare types are favoured by selection, can maintain diversity. However, the ecological processes that mediate NFDS are often not known. Male guppies (Poecilia reticulata) exhibit extreme diversity of colour patterning and, in a previous field experiment, rare morphs had a survival advantage. Here, we test the hypothesis that predators impose NFDS because they are efficient at capturing familiar prey morphs, but are less efficient at capturing unfamiliar morphs. Over a series of trials, we presented Rivulus hartii, a natural predator of guppies, with male guppies with the same colour patterning (A trials); then, for a second series of trials, we presented the rivulus with guppies with a new colour pattern (B trials). The success of rivulus at capturing guppies on the first attack increased over successive A trials. First attack success decreased significantly for the early B trials, and then increased during successive B trials, eventually reaching the same level as in the best A trials. This experiment demonstrates that learning, perhaps through long-term search image formation, plays a role in predation success on familiar vs. unfamiliar prey morphs. These results support the hypothesis that predator learning contributes to the maintenance of the extreme male guppy polymorphism seen in nature.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".