Incorporating Climate Change Impacts Within Harvest Strategies: An Overview of Approaches
Bibliographic record
Abstract
ABSTRACT Ensuring that harvest strategies are robust to climate change is a top priority for many fisheries jurisdictions globally. This is because climate change is altering ecosystem structure and the productivity of marine species. We outline a range of approaches for incorporating climate change impacts within harvest strategies, including how a harvest strategy is specified and changes to monitoring requirements. Approaches evaluated include the use of extended stock assessments, multi‐species and ecosystem models, revised management reference points, implementing regime shifts in model parameters, the provision of climate‐sensitive catch advice, projections under alternative climate change scenarios and expanded use of management strategy evaluation. We evaluate the utility of these approaches against cost, data needs and uncertainty criteria; highlight key learnings from a range of global jurisdictions and demonstrate the broad array of options available outside of direct incorporation of climate variables within stock assessments. We identify approaches that have been successfully implemented and show that the most complex responses are not always the most successful. While there is no one‐size‐fits‐all way to incorporate climate change within harvest strategies, we outline the need for flexible management arrangements. We also provide examples of approaches that have been successfully implemented, demonstrating that many of the most data‐intensive responses will only be applicable in a few cases, necessitating the application of cheaper, less data‐intensive approaches that are associated with greater uncertainty.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".