The status of climate change adaptation in fisheries management: Policy, legislation and implementation
Bibliographic record
Abstract
Abstract Climate change is altering ecosystems and fisheries throughout the world's oceans, demanding climate‐adaptive governance for conserving and managing living marine resources. While in some regions fisheries management systems address wider ecosystem dynamics within management frameworks and decision‐making, which may facilitate resilience to climate change, there remains a shortfall in terms of directly incorporating climate change adaptation into fisheries management legislation and implementation. This review assesses the current state of implementation of climate change adaptation into fisheries management policies and legislation across 11 national case studies, based on government documents and the primary literature. The overarching goal is to understand the key elements and gaps in existing fisheries management policies and legislation in the context of climate change. Given recent reforms of fisheries management policies and/or legislation across the nations examined, political recognition of the need to address climate change adaptation in fisheries management appears to be increasing; albeit formal mandates of climate‐adaptation objectives in fisheries management are largely missing. Based on our review, recommendations for achieving climate‐adaptive fisheries management regimes are developed. Overall, this study will help to inform and broaden the scope of management approaches and tools to accelerate the move towards adaptive fisheries management that accounts for climate change impacts on fish stocks, fisheries and the societies that depend upon them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".