Projecting contributions of marine protected areas to rebuild fish stocks under climate change
Bibliographic record
Abstract
Abstract No-take marine protected areas (No-take MPAs) are considered as a major tool for conserving marine biodiversity and ecosystem services. No-take MPAs can also contribute to climate adaptation for exploited fish stocks. Meanwhile, many fish stocks in the world are overfished and management institutions are developing plans to rebuild them. Understanding the potential effects of no-take MPAs on fish stocks under climate change can help develop strategies for climate-resilient stock rebuilding. Here, using a linked climate-fish-fishing model, we undertake simulation experiments to examine the effects of no-take MPAs on biomass and potential catches of 231 exploited fish and invertebrate species in eight marine ecoregions in the Northeast Atlantic under climate change. The simulations include different levels of fishing, no-take MPAs coverage, atmospheric global warming levels, and account for the expected displacement of fishing to the area around the no-take MPAs. Average individual stock biomass is projected to decrease by 5–15% per degree Celsius atmospheric warming. Having 30% of the distribution of over-exploited fish stocks under no-take MPAs together with conservation-focused fisheries management of these stocks are projected to offset the negative impacts on their biomass under 2.6–2.9 °C global warming. Meanwhile, potential catches increase when a portion of the over-exploited fish stocks is protected from fishing as higher biomass in the no-take MPAs spills-over to the surrounding areas. Our findings highlight that no-take MPAs, combined with reducing fishing intensity, can help rebuild over-exploited fish biomass and benefit their dependent fisheries in the Northeast Atlantic under projected climate change in the 21st century.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".