Can Seasonal Forecasts of Ocean Conditions Aid Fishery Managers? Experiences from 10 Years of J-SCOPE
Bibliographic record
Abstract
Multiple stressors co-occurring in coastal waters are of increasing concern to local fisheries. Many economically, culturally, or ecologically important species (e.g., oysters, crabs, pteropods) in the Pacific Northwest are already directly affected by ocean acidification (OA), warming, and hypoxia. Additional indirect economic impacts on the finfish industry are possible due to losses of prey species. Because of strong seasonal and interannual variations in ocean conditions, capability for predicting degrees of acidification and hypoxia, as well as relevant indices of impact for species of interest, could be of considerable benefit to managers. Over the past 10 years, we have developed a seasonal ocean prediction system, JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE), for the coastal waters of the Pacific Northwest. The goal has been to provide seasonal (six-month) predictions of ocean conditions that are testable and relevant to management decisions regarding fisheries, protected species, and ecosystem health. The results of this work include publicly available seasonal forecasts of OA variables, hypoxia, temperature, and ecological indicators that are tailored for decision-makers involved in federal, international, state, and tribal fisheries. We codesigned J-SCOPE model products with state and tribal managers, and now federal managers at the Pacific Fishery Management Council receive J-SCOPE forecasts of OA and hypoxia within their annual Ecosystem Status Reports. US and Canadian managers of Pacific hake (Merluccius productus) are now briefed on J-SCOPE-driven forecasts of hake distribution. Most recently, new ocean acidification indices specific to Dungeness crab (Metacarcinus magister) have been co-produced with state and tribal managers. In each of these cases, the team has also investigated the sources of skill in forecasting ocean conditions to assess applicability of the forecasts to the variables, depths, and seasons relevant to these high-value fisheries. Observations from NOAA’s Pacific Marine Environmental Laboratory and other regional partners have provided critical validation of model performance throughout the model development process. We offer a retrospective look at the first 10 years of forecasting to provide perspective on its successes and limitations, and the potential global applicability of seasonal forecasting to inform flexible management responses to rapidly changing climate and ocean conditions.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".