Projecting pathways to achieve sustainable ocean targets for food security, climate resilience, and biodiversity conservation
Bibliographic record
Abstract
The ocean's capacity to sustainably, equitably, and safely produce food is declining due to ongoing biodiversity losses driven by direct exploitation, climate change, and other human-induced stressors such as plastic pollution. Solutions such as fisheries management, aquaculture, and marine protected areas have been proposed to reverse these declines. However, recent analyses of global fisheries and aquaculture trends highlight intensifying trade-offs and inequities within and across seafood production, climate action, and biodiversity conservation. In this study, we apply multiple integrated models—encompassing climate, biodiversity, seafood production, and economic impacts—at both global and regional scales to assess how portfolios of proposed solutions could achieve sustainable, equitable seafood security and biodiversity conservation targets under climate change. The models incorporate scenarios for both direct and indirect drivers, including changing ocean conditions, demographic shifts, seafood demand and prices, fisheries management, and aquaculture development strategies. Our projections indicate that urgent, unprecedented actions on multiple fronts to reduce fishing effort, develop sustainable aquaculture, expand marine protected areas, and advance climate mitigation, are required to meet sustainable, equitable seafood security goals. Climate change disproportionately impacts the capacity to produce nutrient-rich seafood for local consumption and rebuild depleted biomass, especially for low- and middle-income countries in the tropics and vulnerable communities in extra-tropical regions. We show that conservation- and vulnerable communities- oriented ocean management strategies and keeping global warming below 1.5°C are necessary conditions to achieve seafood and biodiversity targets. Our findings underscore the co-benefits and trade-offs in the pathways towards achieving food, climate and biodiversity ocean targets. We highlight how scenarios and models with participatory approaches can facilitate the co-development of portfolios of sustainable and equitable solution options for ocean-based food systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".