Predator diet cues and the assessment of predation risk by juvenile brook charr: do diet cues enhance survival?
Bibliographic record
Abstract
Recent studies have shown that predator diet cues provide important information that prey animals can use to assess predation risk. Predator-naïve prey animals may even learn to recognize unknown predators when they detect conspecific cues in the predator's diet. We examined the importance of predator diet cues in the responses of juvenile brook charr (Salvelinus fontinalis) to chemical cues of predators. In our first experiment, we showed that charr respond to chemical cues of adult yellow perch (Perca flavescens) that were fed a diet of either brook charr or rainbow trout (Oncorhynchus mykiss), but not to perch fed a diet of brook stickleback (Culaea inconstans). Responses to diet cues from perch fed both charr and trout may indicate that charr are responding to evolutionarily conserved salmonid alarm cues in the predator's diet. In a second experiment, we exposed charr to chemical cues from predatory northern pike (Esox lucius) that were fed charr, trout, or stickleback. Live-predation trials revealed that charr exposed to diet cues from pike fed salmonids exhibited higher survival than charr exposed to diet cues from pike fed a non-salmonid. These results are the first to document a survival benefit for prey exhibiting behavioural responses to predator diet cues.
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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) | 0.001 | 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".