Predator nonconsumptive effects on prey recruitment weaken with recruit density, supplementary material
Bibliographic record
Abstract
We investigated the nonconsumptive effects (NCEs) of predatory dogwhelks (Nucella lapillus) on intertidal barnacle (Semibalanus balanoides) recruitment through field experiments on the Gulf of St. Lawrence coast and the Atlantic coast of Nova Scotia, Canada. We studied the recruitment seasons (May-June) of 2011 and 2013. In 2011, the Gulf coast had five times more nearshore phytoplankton (food for barnacle larvae and recruits) during the recruitment season and yielded a 58% higher barnacle recruit density than the Atlantic coast at the end of the recruitment season. In 2013, phytoplankton levels and barnacle recruit density were similar on both coasts and also lower than for the Gulf coast in 2011. Using the comparative-experimental method, the manipulation of dogwhelk presence (without allowing physical contact with prey) revealed that dogwhelk cues limited barnacle recruitment under moderate recruit densities (Atlantic 2011/2013 and Gulf 2013) but had no effect under a high recruit density (Gulf 2011). Barnacle recruits attract settling larvae through chemical cues. Thus, the highest recruit density appears to have neutralized dogwhelk effects. This study suggests that the predation risk perceived by settling larvae may decrease with increasing recruit density and that prey food supply may indirectly influence predator NCEs on prey recruitment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.026 |
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".