Predator Non‐consumptive Effects on Prey Recruitment
Bibliographic record
Abstract
All photographs by R. A. Scrosati Predators may have nonconsumptive effects (NCEs) on prey populations mediated by chemical cues detected by prey. We experimentally investigated dogwhelk (Nucella lapillus) NCEs on intertidal barnacle (Semibalanus balanoides) recruit density in Nova Scotia, Canada. Under a moderate abundance of coastal phytoplankton (food for barnacle larvae and recruits), barnacle recruitment was moderate and the nearby presence of dogwhelks limited barnacle recruit density at the end of the recruitment season. Under a high phytoplankton abundance, barnacle recruitment was high and neutralized dogwhelk NCEs on barnacle recruit density, likely through the chemical attraction that recruits exert on larvae seeking settlement. Barnacle recruits and predatory dogwhelks on the Atlantic coast of Nova Scotia, Canada. Photograph by R. A. Scrosati. Julius Ellrich and experimental cages used to study the nonconsumptive effects of predatory dogwhelks on barnacle recruitment. These photographs illustrate the article “Predator nonconsumptive effects on prey recruitment weaken with recruit density” by Julius A. Ellrich, Ricardo A. Scrosati, and Markus Molis, published in Ecology 96(3):611–616. http://dx.doi.org/10.1890/14-1856.1
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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) | 0.010 | 0.001 |
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".