Mechanisms underlying the control of responses to predator odours in aquatic prey
Bibliographic record
Abstract
In aquatic systems, chemical cues are a major source of information through which animals are able to assess the current state of their environment to gain information about local predation risk. Prey use chemicals released by predators (including cues from a predator's diet) and other prey (such as alarm cues and disturbance cues) to mediate a range of behavioural, morphological and life-history antipredator defences. Despite the wealth of knowledge on the ecology of antipredator defences, we know surprisingly little about the physiological mechanisms that control the expression of these defensive traits. Here, we summarise the current literature on the mechanisms known to specifically mediate responses to predator odours, including dietary cues. Interestingly, these studies suggest that independent pathways may control predator-specific responses, highlighting the need for greater focus on predator-derived cues when looking at the mechanistic control of responses. Thus, we urge researchers to tease apart the effects of predator-specific cues (i.e. chemicals representing a predator's identity) from those of diet-mediated cues (i.e. chemicals released from a predator's diet), which are known to mediate different ecological endpoints. Finally, we suggest some key areas of research that would greatly benefit from a more mechanistic approach.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".