Transform high seas management to build climate resilience in marine seafood supply
Bibliographic record
Abstract
Abstract Climate change is projected to redistribute fisheries resources, resulting in tropical regions suffering decreases in seafood production. While sustainably managing marine ecosystems contributes to building climate resilience, these solutions require transformation of ocean governance. Recent studies and international initiatives suggest that conserving high seas biodiversity and fish stocks will have ecological and economic benefits; however, implications for seafood security under climate change have not been examined. Here, we apply global‐scale mechanistic species distribution models to 30 major straddling fish stocks to show that transforming high seas fisheries governance could increase resilience to climate change impacts. By closing the high seas to fishing or cooperatively managing its fisheries, we project that catches in exclusive economic zones (EEZ s) would likely increase by around 10% by 2050 relative to 2000 under climate change (representative concentration pathway 4.5 and 8.5), compensating for the expected losses (around −6%) from ‘business‐as‐usual’. Specifically, high seas closure increases the resilience of fish stocks, as indicated by a mean species abundance index, by 30% in EEZ s. We suggest that improving high seas fisheries governance would increase the resilience of coastal countries to climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".