Limitation of dogwhelk consumption of mussels by crab cues depends on dogwhelk density and cue type
Bibliographic record
Abstract
Abstract Predator nonconsumptive effects (NCEs) on prey activity are common in nature. Upon sensing predator cues, a common prey response is to reduce feeding to avoid being detected by predators. Using an aquatic system, this study investigated how prey density and predator cue type affect predator NCEs on prey feeding. Prey density was investigated because, as it increases, the individual risk of being preyed upon decreases, which may reduce NCEs if prey can detect conspecifics. Predator cue type was investigated because waterborne cues would trigger weaker NCEs than waterborne and tactile cues combined, as predation risk may be perceived by prey to be stronger in the second case. Specifically, a factorial experiment tested the hypotheses that (i) increasing dogwhelk (prey) density reduces the limitation that crab (predator) chemical cues can have on dogwhelk consumption of mussels and that (ii) chemical and tactile crab cues combined limit dogwhelk feeding more strongly than chemical crab cues alone. The results broadly supported these hypotheses. On the one hand, crab chemical cues limited the per-capita consumption of mussels by dogwhelks at a low dogwhelk density, but such NCEs disappeared at intermediate and high dogwhelk densities. On the other hand, the combination of chemical and tactile cues from crabs caused stronger NCEs, as dogwhelk consumption of mussels was negatively affected at all three dogwhelk densities. The structurally complex mussel beds may provide not only food for dogwhelks but a refuge from crab predation that allows dogwhelk density to limit crab NCEs when mediated by waterborne cues. Overall, this study suggests that prey evaluate conspecific density when assessing predation risk and that the type of cues prey are exposed to can affect their interpretation of risk.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".