Chemical disturbance cues in aquatic systems: a review and prospectus
Bibliographic record
Abstract
Abstract In the natural environment, animals can face potentially dangerous and often regular exposure to major environmental fluctuations such as flash flooding and drought, or the approach of a predator. For many aquatic species, exposure to these ecological disturbances triggers the release of “disturbance cues” – generally characterized as chemicals released when animals are startled but not injured. While the chemistry of such cues remains largely unexplored, they appear to provide early warning information to nearby individuals, potentially leading to behavioral decisions that increase overall fitness, particularly for social species that may coordinate group defense. In the literature, disturbance cues have received little attention relative to other chemical cues, such as damage‐released alarm cues. However, recent advances in the study of disturbance cue communication have led an uptick in research on the subject. Here, we review the existing literature on responses to disturbance cues in aquatic systems. Although the majority of studies involve behavioral responses to a simulated predator approach, we describe various disturbance types across a broad range of taxa. We discuss the ecological implications of disturbance cues, including their role in risk assessment, signaling, learning, and species specificity. We also address several methodological challenges for this developing field of study, as well as the ethical and conservation implications of this research going forward. Future research on disturbance cues should address a number of key unknowns, including questions regarding disturbance cue chemistry, function, and generality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".