Review of climate change impacts on marine fisheries in the UK and Ireland
Bibliographic record
Abstract
ABSTRACT Commercial fishing is an important socio‐economic activity in coastal regions of the UK and Ireland. Ocean–atmospheric changes caused by greenhouse gas emissions are likely to affect future fish and shellfish production, and lead to increasing challenges in ensuring long‐term sustainable fisheries management. The paper reviews existing knowledge and understanding of the exposure of marine ecosystems to ocean‐atmospheric changes, the consequences of these changes for marine fisheries in the UK and Ireland, and the adaptability of the UK and Irish fisheries sector. Ocean warming is resulting in shifts in the distribution of exploited species and is affecting the productivity of fish stocks and underlying marine ecosystems. In addition, some studies suggest that ocean acidification may have large potential impacts on fisheries resources, in particular shell‐forming invertebrates. These changes may lead to loss of productivity, but also the opening of new fishing opportunities, depending on the interactions between climate impacts, fishing grounds and fleet types. They will also affect fishing regulations, the price of fish products and operating costs, which in turn will affect the economic performance of the UK and Irish fleets. Key knowledge gaps exist in our understanding of the implications of climate and ocean chemistry changes for marine fisheries in the UK and Ireland, particularly on the social and economic responses of the fishing sectors to climate change. However, these gaps should not delay climate change mitigation and adaptation policy actions, particularly those measures that clearly have other ‘co‐benefits’. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".