Water motion modulates predator nonconsumptive limitation of prey recruitment
Bibliographic record
Abstract
Abstract Predator chemical cues can elicit behavioral changes in prey to minimize predation risk. Recent field studies have shown that such predator nonconsumptive effects (NCEs) can ultimately affect prey demography. The environment is known to modulate predator consumptive effects on prey demography, but the environmental modulation of predator NCEs on prey demography remains unstudied. We investigated this knowledge gap using an intertidal predator–prey system. Dogwhelk (Nucella lapillus) chemical cues can limit barnacle (Semibalanus balanoides) recruitment by limiting larval settlement. As waves disperse chemicals in coastal environments, we experimentally tested the hypothesis that wave exposure limits dogwhelk NCEs on barnacle recruitment. Shortly before the barnacle recruitment season of 2013 (May–June), we established cages in rocky intertidal habitats in Atlantic Canada under two levels of wave exposure. The cages were used to manipulate the presence and absence of dogwhelks. At the center of each cage, we installed a tile where barnacle pelagic larvae could settle and develop into recruits. Mesh prevented caged dogwhelks from accessing the tiles, but allowed their waterborne cues to reach the tiles. Data collected at the end of the recruitment season indicated that dogwhelk cues limited barnacle recruitment in wave‐sheltered habitats but had no effect on recruitment in wave‐exposed habitats. These findings suggest that predicting predator NCEs on prey demography may require environmental information related to the ability of prey to locate predators.
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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) | 0.001 | 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".