Estimating the ecological, economic and social impacts of ocean acidification and warming on <scp>UK</scp> fisheries
Bibliographic record
Abstract
Abstract Assessments of the combined ecological impacts of ocean acidification and warming ( OAW ) and their social and economic consequences can help develop adaptive and responsive management strategies in the most sensitive regions. Here, available observational and experimental data, theoretical, and modelling approaches are combined to project and quantify potential effects of OAW on the future fisheries catches and resulting revenues and employment in the UK under different CO 2 emission scenarios. Across all scenarios, based on the limited available experimental results considered, the bivalve species investigated were more affected by OAW than the fish species considered, compared with ocean warming alone. Projected standing stock biomasses decrease between 10 and 60%. These impacts translate into an overall fish and shellfish catch decrease of between 10 and 30% by 2020 across all areas except for the Scotland >10 m fleet. This latter fleet shows average positive impacts until 2050, declining afterwards. The main driver of the projected decreases is temperature rise (0.5–3.3 °C), which exacerbate the impact of decreases in primary production (10–30%) in UK fishing waters. The inclusion of the effect of ocean acidification on the carbon uptake of primary producers had very little impact on the projections of potential fish and shellfish catches (<1%). The <10 m fleet is likely to be the most impacted by‐catch decreases in the short term (2020–50), whereas the effects will be experienced more strongly by the >10 m fleet by the end of the century in all countries. Overall, losses in revenue are estimated to range between 1 and 21% in the short term (2020–50) with England and Scotland being the most negatively impacted in absolute terms, and Wales and North Ireland in relative terms. Losses in total employment (fisheries and associated industries) may reach approximately 3–20% during 2020–50 with the >10 m fleet and associated industries bearing the majority of the losses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".