Larval amphibians learn to match antipredator response intensity to temporal patterns of risk
Bibliographic record
Abstract
The importance of temporal variability in risk has recently come to the forefront of research examining the behavioral ecology of predator–prey relationships. Temporal variability has been known to drive patterns of behavioral responses associated with foraging, reproduction, and territorial defense of prey animals. However, it is unknown if such behavioral responses are a result of selective depredation, which leads to innate temporal patterns of behavior, or, alternatively, are a result of learning by the prey. Here, we investigated whether larval wood frog (Rana sylvatica) tadpoles can learn to adjust the intensity of their antipredator responses to match the temporal patterns of risk they experience. Tadpoles were exposed to the odor of a predatory salamander paired with injured conspecific cues (salamander present and feeding) during the morning and received the salamander odor alone in the evening (salamander present but not feeding—morning risk treatment), whereas another group received the opposite treatments (evening risk treatment). The 2 groups were treated for 9 days. When subsequently exposed to salamander alone in the evenings, the tadpoles from the evening risk treatment responded with greater antipredator response intensity than the tadpoles from the morning risk treatment. This indicates that tadpoles have the ability to learn the change in predation risk they experience throughout the day and respond to such threats with an intensity reflecting their vulnerability to the 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 | 0.000 | 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) | 0.002 | 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".