Predator-recognition training enhances survival of brook trout: evidence from laboratory and field-enclosure studies
Bibliographic record
Abstract
In this study we tested whether brook trout (Salvelinus fontinalis) can learn to recognise predators through releaser-induced recognition learning and whether this learning enhances survival of trout during encounters with a predator. In our initial experiment, we exposed hatchery-reared predator-naïve brook trout to chemical stimuli from predatory chain pickerel (Esox niger) paired with alarm signals released by damaged trout, disturbance signals, or distilled water. In subsequent tests 24 h later, when only pickerel odour was presented, trout conditioned with damage-released alarm signals exhibited antipredator behaviour (i.e., decreased movement and altered foraging patterns), in contrast to the other treatments, thereby demonstrating learned recognition of the predator. In our second experiment we showed that trout retained the ability to recognise the predator for at least 10 days. In the next series of experiments we explicitly tested whether training trout to recognise predators confers a survival benefit. During staged encounters with chain pickerel (in both the laboratory and the field), trained fish were better able to evade the predator than nontrained fish. Ours is the first study to demonstrate that fish trained to recognise predators gain a survival benefit during staged encounters with a predator.
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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 | 0.001 |
| 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 | 0.001 |
| 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".