Interannual variation in larval survival of snapper (<i>Chrysophrys auratus</i>, Sparidae) is linked to diet breadth and prey availability
Bibliographic record
Abstract
Larval snapper ( Chrysophrys auratus , Sparidae) sampled over 7 years had different diets and feeding strategies between years with lower versus higher larval and 0+ abundances. We analysed stomach contents of snapper larvae from each year to determine diet composition, prey selectivity, prey quality, and trophic niche breadth and compared larval diet with prey availability. Snapper larvae from higher abundance years were specialized foragers selecting for calanoid nauplii at 2–4 mm standard length (SL) and calanoid copepodites and cladocerans at >4 mm SL. These larvae were characterized by either a constant or dome-shaped trophic niche breadth and an increase in prey quality (size of consumed prey) with increasing larval size. Snapper larvae from lower abundance years were generalist foragers characterized by an increase in trophic niche breadth, but not prey quality, with increasing larval size. Changes in foraging strategies were concordant with changes in the prey environment, with low zooplankton densities corresponding with generalist diet (lower larval abundance) years and high zooplankton densities with specialist diet (higher larval abundance) years. These findings suggest that snapper larval survival and juvenile recruitment strength is linked to changes in larval diet that relate to prey abundance and composition.
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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 | 0.001 | 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".