LEARNED RECOGNITION OF HETEROSPECIFIC ALARM CUES ENHANCES SURVIVAL DURING ENCOUNTERS WITH PREDATORS
Bibliographic record
Abstract
Abstract Numerous species of aquatic animals release chemical cues when attacked by a predator. These chemicals serve to warn other conspecifics, and in some cases heterospecifics, of danger, and hence have been termed alarm cues. Responses of animals to alarm cues produced by other species often need to be learned, yet mechanisms of learned recognition of heterospecific cues are not well understood. In this study, we tested whether fathead minnows (Pimephales promelas) could learn to recognize a heterospecific alarm cue when it was combined with conspecific alarm cue in the diet of a predator. We exposed fathead minnows to chemical stimuli collected from rainbow trout, Oncorhynchus mykiss, fed a mixed diet of minnows and brook stickleback, Culaea inconstans, or trout fed a mixed diet of swordtails, Xiphophorous helleri, and stickleback. To test if the minnows had acquired recognition of the heterospecific alarm cues, we exposed them to stickleback alarm cues and introduced an unknown predator, yellow perch (Perca flavescens) or northern pike (Esox lucius). Both perch and pike took longer to initiate an attack on minnows that were previously exposed to trout fed minnows and stickleback than those previously exposed to trout fed swordtails and stickleback. These results demonstrate that minnows can learn to recognize heterospecific alarm cues based on detecting the heterospecific cue in combination with minnow alarm cues in the diet of the predator. Ours is the first study to demonstrate that behavioural responses to heterospecific chemical alarm cues decreases the probability that the prey will be attacked and captured during an encounter 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 | 0.000 | 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".