Multiple Models of European Marine Fish Stocks: Regional Winners and Losers in a Future Climate
Bibliographic record
Abstract
Climate change continues to alter the productivity of commercially and culturally important fisheries with major consequences for food security and coastal economies. We provide the first, multi-model projections of changes in the distribution and productivity of 18 key fish stocks across seven European regional seas spanning the Mediterranean to the Arctic, using 11 state-of-the-art bio-ecological models. Our projections indicate species- and region-specific changes in abundance and distributions of these stocks by the mid- to late 21st century. The varied responses are caused by differences in species' physiology, regional food web dynamics, and physical habitat characteristics. Important drivers include not only warming of Europe's seas (from 1°C to 3°C in RCP 4.5, and 2°C to 4°C in RCP 8.5 by 2100) and changes in primary productivity but also oxygen-limited fish growth, changes in pH, and benthic dissolved organic carbon. Warming and altered levels of secondary production are projected to lead to declines in some stocks (Norwegian and Barents Sea herring) and increases in others (Bay of Biscay anchovy). While some temperate and cold-water stocks are projected to decline markedly in some regions (e.g., North Sea, Western Mediterranean), the immigration of species from the south and/or increase in productivity of warm-water species may offer new opportunities for fisheries. Species-level changes will likely have ecosystem-level consequences that have yet to be fully assessed, and responses in some sub-areas may be more pronounced due to local processes not captured in projections. Projections are consistent despite differences in model structures, and the results of our multi-model analysis align with other modelling exercises while delving into details often overlooked at the species or spatial level. This represents a novel approach to projecting the impacts of climate change on fisheries, which should be considered in future efforts to support climate-ready management strategies for marine fish stocks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".