Spatiotemporal modelling of Greenland halibut maturation across the Northwest Atlantic
Bibliographic record
Abstract
Abstract Modelling life history trait variation at appropriate spatial and temporal scales is crucial for understanding population dynamics and developing effective fisheries management strategies. However, most efforts to model life history traits ignore spatial correlations and make a priori assumptions about the spatial structuring of populations, potentially clouding the ability to recognize true spatial structure. Here we develop spatiotemporal maturation models for Greenland halibut (Reinhardtius hippoglossoides) in the Northwest Atlantic, a species with large-scale movement patterns that can lead to uncertainty regarding effective stock boundaries. Our analysis using data from three Fisheries and Oceans Canada survey regions, Baffin Bay and Davis Strait in the eastern Canadian Arctic, Newfoundland and Labrador (NL), and the northern Gulf of St. Lawrence (GSL), is the first at such a large spatial scale. We also extend the traditional binary maturity status to a multinomial one that accounts for seasonal changes in maturation. Results show a decreasing temporal trend in size at maturity across the entire area. Spatial results regarding size at maturity provide new insight linking Greenland halibut south of Newfoundland (Northwest Atlantic Fisheries Organization Subdivision 3Ps) to the GSL stock rather than the NL stock. Results also highlight parts of the Davis Strait area, where size at maturity is smaller than in waters both north and south. Multinomial model results identify areas in GSL and Davis Strait that may be important for reproductive development in the summer and fall. Our analyses also reveal constraints on size at maturity that correspond with the well-known positive association between fish length and bottom depth. Broad-scale analyses of high resolution spatial patterns in life history traits, such as those performed here for Greenland halibut maturation, may identify recurrent patterns of association with environmental or habitat characteristics that might not otherwise be evident on a stock- or survey-specific basis.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".