Reduction in size‐at‐maturity in unprecedentedly strong cohorts of redfish (<i>Sebastes mentella</i> and <i>S. fasciatus</i>) in the Gulf of St. Lawrence and Laurentian Channel
Bibliographic record
Abstract
Life-history traits, such as size-at-maturity, are key parameters to model population dynamics used to inform fisheries management. Fishery-induced evolution, density-dependent effects, and global warming have been shown to affect size- and age-at-maturity, and resulting spawning stock biomass (SSB) in a wide range of commercial fish stocks. Marked changes in redfish biomass and environmental conditions in the Gulf of St. Lawrence and Laurentian Channel over the past decade called for a review and update of size-at-maturity for commercially important deepwater redfish Sebastes mentella and Acadian redfish Sebastes fasciatus stocks. Following a 25-year moratorium, local redfish biomass has recently reached unprecedented levels, co-occurring with an overall warming of bottom water temperatures. Our objectives were (1) to perform a histological assessment of redfish reproduction stages, including the validation and fine-tuning of a robust visual chart to facilitate monitoring of size-at-maturity and SSB in a transforming environment, and (2) to evaluate changes in size-at-maturity in unprecedentedly strong cohorts of redfish, and consequences for stock status assessment and fisheries management. Each specimen was genetically identified to species, and gonad reproduction stages were determined by histology and macroscopic appearances. The present study enabled a robust visual chart for continued and cost-effective monitoring of redfish reproduction stages to be refined and validated, and has shown a large decrease in redfish length when 50% of the individuals are considered mature that led to an increase in estimates of SSB during the 2011-2021 period for S. mentella and S. fasciatus. These changes modified the perception of stock status, thus having significant implications for fisheries management. Given that fishery-induced evolution and community structure changes along with global warming are affecting numerous stocks worldwide, the present study outlines a major and global challenge for scientists and resources managers. As shown by our results, the monitoring and frequent updates of life-history traits in transforming environments are needed to provide reliable science advice for sustainable fisheries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".