Climate-induced changes in ocean productivity and food-web functioning are projected to markedly affect European fisheries catch
Bibliographic record
Abstract
In European waters, climate-induced changes in ocean conditions will alter marine ecosystems, leading to potential repercussions for the European fisheries and the status of exploited species. Here, we used a new version of the EcoTroph model, forced by a regional high-resolution coupled hydrodynamic-ecosystem model, to investigate the effects of climate change on biomass and catch in 15 areas (ICES divisions) of the European Atlantic shelf ecosystems. Based on the projected changes in temperature, zooplankton and benthic secondary producers, we modeled the changes in biomass and catch at each trophic level by the end of the 21st century. We projected that total biomass and catch for the whole Atlantic European seas would decrease by 11.5 and 10.0%, respectively, by 2090-2099 relative to 2013-2017 under a ‘no mitigation’ greenhouse gases emissions scenario (RCP8.5). The projected decrease in catch is 310000 or 240000 t by 2090-2099 under a high (RCP8.5) or a moderate (RCP4.5) emissions scenario, respectively. Some areas, such as the Celtic Sea, would be more affected than others, while the climate impact on the bentho-demersal biomass and catches would be more pronounced, especially toward the higher trophic levels. Our study suggests that climate change may strongly impact European fisheries, with ecological consequences and potential socio-economic repercussions. Future studies using alternative climate and ecosystem models would allow the exploration of uncertainties in projected biomass and catch. While fisheries management is required to adapt to these changes, the projected impacts on catch cannot be avoided without aggressive mitigation of greenhouse gas emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".