Nonconsumptive predator effects on prey demography
Bibliographic record
Abstract
Nonconsumptive effects (NCEs) of predators on prey are widespread in nature. Such effects occur when prey organisms detect cues from nearby predators and exhibit responses to minimize predation risk. Immediate prey responses often include avoidance behavior. The demographic consequences, however, have been considerably less studied. Understanding them is necessary because demography influences a species’ role in its community. Using intertidal predator–prey systems from Atlantic Canada, we investigated predator NCEs on prey recruitment and reproduction. Field experiments showed that dogwhelk (predator) cues limit barnacle (prey) recruitment by inducing barnacle larvae to settle elsewhere. Dogwhelk density intensifies such NCEs. Dogwhelk cues ultimately limit barnacle reproduction once recruits mature into adults. High densities of barnacle recruits (seemingly favored by planktonic food supply) and adults, however, eliminate dogwhelk NCEs, as barnacle recruits and adults attract conspecific larvae seeking settlement. Wave action also eliminates dogwhelk NCEs, presumably by diluting predator cues in the water. Dogwhelk cues also limit the recruitment of mussels (another prey), although less intensely than for barnacles, possibly because mussels have more opportunities to escape predation during their benthic existence. Overall, our research demonstrates that predator cues can significantly limit prey recruitment and reproduction, but that biotic and abiotic factors can neutralize such NCEs.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".