LEARNED RECOGNITION OF HETEROSPECIFIC ALARM CUES ENHANCES SURVIVAL DURING ENCOUNTERS WITH PREDATORS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it