Linking interannual variations of capelin abundance indices in the Gulf of St. Lawrence to environmental proxies of bottom-up regulation of cohort strength
Bibliographic record
Abstract
In this study, we applied a conceptual model relating capelin cohort strength to bottom-up processes using newly developed capelin abundance indices and descriptors of physical and biological oceanographic conditions in the Gulf of St. Lawrence (GSL). The main objective of the analyses was to provide evidence that abundance indices estimated from bottom-trawl surveys in the GSL track some of the variation expected from environmental conditions known to control variations in capelin cohort strength on the eastern Newfoundland shelf. In addition, we sought to improve our understanding of environmental drivers of capelin dynamics and productivity in the GSL over the last 3 decades. Our approach included (1) the development of a capelin larval abundance indices considered as a potential explanatory variable in the conceptual (2) analyses aimed at documenting the links between physical oceanographic conditions, spring bloom and Calanus dynamics, and capelin body condition in the GSL, and (3) multivariate non-linear models relating capelin abundance indices in different sub-regions of the GSL with environmental indices or capelin condition used as proxies of capelin survival potential. Our results showed that the seasonal phenology and June abundance of Calanus hyperboreus was associated with the ice and spring bloom dynamics, while the phenology and abundance of C. finmarchicus was mostly associated with sea-surface temperature (SST) indices in spring and early summer. High capelin body condition (Kn) in June was related to either early ice retreat or associated high C. hyperboreus abundance and early development timing, while high Kn in August-September was observed in years with a late timing of C. finmarchicus population development or a high C. finmarchicus abundance and SST during summer. Variations in capelin abundance indices were generally associated with variations in Kn in June and/or in August-September or their environmental proxies during the first 1-2 years of life in capelin, but not to capelin larval abundance indices. Timing of ice retreat was selected in 5 out of 6 models whereas proxies of Kn in August-September were selected in 3 of 4 models. Our results therefore identified sea ice dynamics (late winter-early spring) or SST (late spring-summer) as potential key drivers of Calanus species dynamics and capelin condition, confirming the bottom-up hypothesis implicitly considered in the predictive model of capelin biomass on the eastern Newfoundland shelf. Our results also confirmed that capelin abundance indices derived from bottom-trawl surveys in the GSL generally track variation in abundance expected from known bottom-up processes regulating capelin’s cohort strength.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".