Bottom-up and top-down control of small pelagic forage fish: factors affecting age-0 herring in the Strait of Georgia, British Columbia
Bibliographic record
Abstract
Small pelagic fish are key planktivores and prey in marine ecosystems, and their population abundances undergo strong temporal and spatial variability.Top-down (predator controlled) and bottom-up (prey-driven) processes during early life history are important for determining forage fish survival and recruitment.We examined biological and environmental factors hypothesized to influence age-0 Pacific herring Clupea pallasi in the Strait of Georgia (SOG), British Columbia, Canada.Primarily bottom-up processes affected interannual variability in age-0 herring abundance and condition, with some evidence of top-down effects on condition.Age-0 herring abundance increased with increasing adult spawning biomass and peaked when most adults spawned about 20 d prior to the peak spring primary production bloom.This timeline would temporally align first-feeding herring larvae with their prey, such as small copepods.Age-0 herring abundance also increased with increasing juvenile salmon abundance, indicating that conditions favourable for herring were also favourable for their predators and competitors.Age-0 herring condition decreased with increasing spawning biomass, increased when most adults spawned closer to the peak spring bloom, increased with increasing temperatures above 8.2°C, and increased then stabilized with increasing prey zooplankton density.Age-0 herring condition had a dome-shaped relationship with predator abundance, indicating that high predator abundances negatively affected fish condition.Study results suggest that density-dependent processes, such as intraspecific competition, may be important in the SOG.A positive correlation between age-0 herring abundance and subsequent age-3 recruit abundance may provide a leading indicator of low recruitment years. KEY WORDS: Age-0 herring • Abundance • Condition • Bottom-up • Top-down •
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".