Phenological diversity of a prey species supports life-stage specific foraging opportunity for a mobile consumer
Bibliographic record
Abstract
Abstract Dynamic prey resources influence foraging opportunities for consumers. In coastal food webs, forage fish abundance and seasonal reproduction mediate foraging opportunities for mobile consumers. Recent declines in Chinook salmon productivity have prompted efforts to determine whether poor marine survival is caused by limited feeding opportunities. To establish the importance of phenological diversity in Pacific herring for Chinook salmon, we used genetic stock identification to assign individual herring collected from the guts of juvenile and adult Chinook salmon to populations with distinct spawning phenologies. The majority of herring in the guts of adult Chinook salmon across seasons and geographic areas were dominated by the March–April herring spawn group, but juvenile Chinook salmon diets varied seasonally, with a higher proportion of January–February spawners in summer than in spring. Our results suggest that (1) population diversity of Pacific herring is used by juvenile Chinook salmon and thus contributes to their growth, and (2) stock-specific distribution of Pacific herring extends well beyond documented spawning grounds. Herring population diversity may therefore support foraging opportunities for Chinook salmon during a critical period and highlights the need for future research to quantify seasonal distribution and abundance of phenologically distinct groups of Pacific herring within Salish Sea.
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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) | 0.003 | 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".