Fish population growth in the Gulf of St Lawrence: effects of climate, fishing and predator abundance
Bibliographic record
Abstract
Climate variability, fishing and predation are the main factors affecting fish population dynamics. In this study, the drivers of population growth variability were investigated for 7 fish stocks (2 cod, 4 herring and 1 mackerel stock) in the Gulf of St. Lawrence (NW Atlantic). The annual population growth sensitivity to both recruitment and survival (i.e. how much population growth depends on recruitment and survival) was estimated through elasticity analyses before being linked to harvesting history as well as environmental conditions (climate and predation). Stock-specific generalized additive models showed that population growth was most sensitive to recruitment variability with decreasing fish generation time, increasing water temperature and, in some cases, with predation. The dependence of population growth on recruitment, however, was generally close to 0, albeit higher for pelagic stocks than for demersal ones. This indicates that adult survival was more important than recruitment in shaping population growth and thus population size. As climate mainly acts on recruitment while fishing affects adult survival, management efforts seem more important than climate variability in regulating population growth, especially for long-lived cod. Nevertheless, population growth may become increasingly dependent on recruitment variability with warming waters; therefore, more flexible management strategies should be developed to cope with these oscillations.
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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 | 0.000 | 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) | 0.000 | 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".