Using Multivariate Autoregressive State-Space Models to Examine Stocks of Greenland Halibut in the North Atlantic
Bibliographic record
Abstract
To manage a fishery effectively, and implement worthwhile fisheries regimes and conservation plans, it is highly important recognizing the stock structure of an exploited species. Greenland halibut is managed in the North Atlantic as four separated offshore stocks. Here I work with three of those stocks, the Northeast Canada - West Greenland (NWAS), the East Greenland, Iceland and Faroes waters (WNS) and the Barents Sea (NAS) to examine if the existing management boundaries should be maintained or should be reconsidered. For that I have combined abundance time-series from bottom trawl surveys from 4 different countries from 1996 to 2019, and I have mathematically formulated 13 different hypotheses about the population structure of Greenland halibut with Multivariate Autoregressive State-Space (MARSS) models. These hypotheses were based on the literature and biology of the GHL, at two different depth zones (shallow < 400 m; deep > 400 m) and for 3 length ranges (9-29 cm, juveniles; 30-60 cm maturing; > 61cm adults). Finally, for each hypotheses, I run the models with different levels of parameters complexity, and with and without covariates (NAO index and commercial catches) to evaluate relationships with climate and commercial catches. The best fit model included five different trajectories without the effect of the covariates: (1) One overall trajectory for NWAS; (2) East Greenland north; (3) East Greenland south (juveniles, maturing, and adults deep) - West Iceland (juveniles, and maturing deep); (4) West Iceland adults deep - West Iceland (juveniles, maturing and adults shallow) - East Iceland (juveniles, maturing and adults shallow) - East Iceland adults deep; and (5) East Iceland (juveniles and maturing deep) – All NAS. The results of the best fit model suggest that the assessment of Greenland halibut in the North Atlantic should be treated carefully, flagging out the WNS, which seems to be a mix of different populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".