Predator threat assessment in Daphnia magna: the role of kairomones versus conspecific alarm cues
Bibliographic record
Abstract
Studying the finely tuned mechanism of predation risk assessment allows for a better understanding of how prey organisms make key decisions under different levels of predation pressure. We studied the relative importance of conspecific alarm cues and fish kairomones as initiators of D. magna antipredator defences. By exposing a clone of D. magna to different infochemicals that simulated the presence of an active fish predator, we observed cue-specific responses in terms of altered feeding behaviour, respiration and life-history traits. Results agreed with the hypothesis that D. magna processes information from alarm cues from macerated conspecifics and from predator kairomones to assess the level of predation risk, adjusting the magnitude of their responses to the different levels of threat perceived. Results support the findings of other investigations and further show that single cues (fish kairomones or alarm cues) triggered feeding reduction and increased oxygen consumption, whereas fish kairomones only elicited D. magna life-history responses. Prey-specific alarm cues can thus modify the response of Daphnia to trout kairomones and this combination of both chemical cues appears to be necessary to trigger the full deployment of antipredator responses and avoid unnecessary costs arising from maladaptive responses.
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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 | 0.000 |
| 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) | 0.000 | 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".