Effect of environmental variability on body condition and recruitment success of Atlantic Mackerel (<i>Scomber scombrus</i> L.) in the Gulf of St. Lawrence
Bibliographic record
Abstract
Abstract The objective of this study was to assess the effect of environmental variability on the dynamics of the Atlantic mackerel (Scomber scombrus L.) stock in the Gulf of St. Lawrence (GSL). We first described the dominant modes of physical and biological (zooplankton) variability using Principal Components Analyses of 40 variables. Two principal modes of variability were identified, a long‐term mode (15–20 yr) associated with a warming of the GSL and a second mode describing alternating cold and warm periods at a higher frequency (5–10 yr). A strong link between physical forcing and the dynamics of zooplankton species known to be important for mackerel was shown. Second, a set of Generalized Additive Models (GAM) was developed to explore how these environmental variations could influence mackerel condition (Fulton's K) and recruitment success (Rs). Optimal GAMs including variations in abundance, species composition and phenology of key copepods improved model performance by 40–50% relative to those considering only physical environmental conditions. The results are consistent with the match–mismatch hypothesis and illustrate the key role of zooplankton dynamics in modulating variations in mackerel K and Rs. Finally, this study showed that large variations in Rs could be caused by varying environmental conditions independently of the influence of stock biomass. Our results strongly indicate that the effect of environmental variability should be considered in the implementation of an ecosystem‐based approach to Atlantic mackerel stock management.
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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".