Disturbance cues as a source of risk assessment information under natural conditions
Bibliographic record
Abstract
Abstract Disturbance cues are released by stressed or disturbed prey prior to a predator attack and convey useful risk assessment information regarding local threats. While studies have shown that disturbance cues may be important early on within the predation sequence (prior to an attack), their role in predator–prey interactions remains relatively overlooked by ecologists. Critically, experimental studies examining disturbance cues, especially among prey fishes, have been conducted primarily under laboratory or semi‐natural conditions. Here, we tested the prediction that disturbance cues function as sources of risk assessment information in situ. We exposed Trinidadian guppies, in two natural populations differing in predation risk, to a model predator paired with stream water or the disturbance cue collected from guppies from either a high‐ or low‐predation risk population. We found that the predator inspection response of guppies to disturbance cues depends on the level of risk of both the focal and the cue source population. Guppies from both populations exhibited increased latencies to inspect, lower inspection rates and reduced inspecting group sizes towards the model paired with conspecific disturbance cues versus a stream water control. Interestingly, guppies of both populations showed evidence of higher perceived predation risk towards the disturbance cues collected from high‐predation risk donors compared to low‐predation risk donors. Our results support the hypothesis that disturbance cues function as a source of information used by prey fish in the assessment of predation risk and provide the first evidence of disturbance cue function under fully natural conditions.
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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 | 0.000 |
| 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".