Effects of group size on the threat-sensitive response to varying concentrations of chemical alarm cues by juvenile convict cichlids
Bibliographic record
Abstract
The threat-sensitive predator avoidance model predicts that prey should balance the intensity of antipredator responses against perceived predation risk, resulting in a graded response pattern. Recent studies have demonstrated considerable interspecific variation in the intensity of threat-sensitive response patterns, ranging from strongly graded to relatively nongraded or "hypersensitive" threat-sensitive response patterns. Here, we test for intraspecific plasticity in threat-sensitive responses by varying group size. We exposed juvenile convict cichlids, Archocentrus nigrofasciatus (Günther, 1867), as individuals or in small (groups of three) or large (groups of six) shoals to a series of dilutions of conspecific chemical alarm cues and a distilled water control. Singleton cichlids exhibited significant reductions in time spent moving and in frequency of foraging attempts (relative to distilled water controls) when exposed to a 12.5% dilution of conspecific alarm cue, with no difference in response intensity at higher stimulus concentrations, suggesting a nongraded (hypersensitive) response pattern. Small shoals exhibited a similar response pattern, but at a higher response threshold (25% dilution of stock alarm cue solution). Large shoals, however, exhibited a graded response pattern. These results suggest that group size influences the trade-off between predator avoidance and other fitness related activities, resulting in flexible threat-sensitive response patterns.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".