Predator metamorphosis and its consequence for prey risk assessment
Bibliographic record
Abstract
Living with a diverse array of predators provides a significant challenge for prey to learn and retain information about each predator they encounter. Consequently, some prey respond to novel predators because they have previous experience with a perceptually similar predator species, a phenomenon known as generalization of predator recognition. However, it remains unknown whether prey can generalize learned responses across ontogenetic stages of predators. Using wood frog (Lithobates sylvaticus) tadpole prey we conducted two experiments to explore the extent of predator generalization of different life stages of two different predators: (1) predacious diving beetles (Dytiscus sp.) and (2) tiger salamanders (Ambystoma mavortium). In both experiments, we used chemical alarm cues (i.e., injured conspecific cues) to condition tadpoles to recognize the odor of either the larval or adult stage of the predator as risky. One day later, we tested tadpoles with either the larval or adult predator odor to determine whether they generalized their learned responses to the other life stages of the predator. Tadpoles generalized between larval and adult beetle odors but failed to generalize between larval and adult salamander odors. These results suggest that the odor of some predator species changes during metamorphosis to an extent that reduces their recognisability by prey. This ‘predator identity reset’ increases the number of threats to which prey need to attend.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.024 |
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".