Responses of American toad tadpoles to predation cues: behavioural response thresholds, threat-sensitivity and acquired predation recognition
Bibliographic record
Abstract
Abstract Predation is one of the most important selective forces acting on prey animals. To respond adaptively to predation threats and increase their chances of survival, prey animals have to be able to recognize their potential predators. Even though a few studies demonstrated innate predator recognition, the vast majority of animals have to rely on learning to acquire this information. Often aquatic prey animals can learn to recognize predators when they detect conspecific alarm cues associated with cues from a novel predator. In this study, we exposed American toad (Bufo americanus) tadpoles to varying concentrations of chemical alarm cues (cues from injured conspecifics). We identified a concentration of cues which caused an overt antipredator response (supra-threshold concentration) and a lower concentration for which the prey failed to exhibit a response (sub-threshold concentration). In a second experiment, we attempted to condition the tadpoles to recognize the odour of larval dragonflies (Anax sp.) by pairing the dragonfly odour with either the sub-threshold concentration or the supra-threshold concentration of alarm cues. In both cases, the tadpoles learned to recognize the predator based on this single pairing of alarm cues and predator odour. Moreover, the intensity of the learned response was stronger for tadpoles conditioned with the supra-threshold concentration of alarm cues than the sub-threshold concentration. This is the first documented case of this mode of learning in anuran amphibians. Learned recognition of predators has important implications for survival.
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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 | 0.001 |
| 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".