Fish harvesting advice under climate change: A risk-equivalent empirical approach
Bibliographic record
Abstract
The rate of climate change (CC) has accelerated to the point where it now affects the mid- to long-term sustainability of fishing strategies. Therefore, it is important to consider practical and effective ways to incorporate CC into fisheries advice so that the advice can be considered conditioned to CC. We developed a model to characterise the empirical relationship between a variable affected by climate and fish production. We then used model projections as a foundation for a risk analysis of CC effects on harvesting of Greenland halibut Reinhardtius hippoglossoides in the Gulf of St Lawrence, Canada. The risk-based approach quantified a) the relative change in risk of a status quo fishing strategy under various CC scenarios, and b) the change in fishery exploitation rates required to achieve a management objective over a specified time period at a level of risk considered acceptable (risk equivalent fishery exploitation advice). This empirical approach can be used to develop risk-based advice for any other external variable that affects stock production in addition to climate-related variables and it can be applied in most situations where there is an index of stock biomass and fisheries catch. Shifting the focus from process-based understanding of the responses of fish stocks to CC to quantification of how CC-contributed uncertainty can alter the risks associated with different fishing strategies and/or management options, can ensure timely delivery of robust scientific advice for fisheries under non-stationary environmental 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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".