Data for: Projecting global mariculture production and adaptation pathways under climate change
Bibliographic record
Abstract
The sustainability of global seafood supply to meet increasing demand is facing several challenges, including increasing consumption levels due to a growing human population, fisheries resources over-exploitation and climate change. While growth in seafood production from capture fisheries is limited, global mariculture production is expanding. However, climate change poses risks to the potential seafood production from mariculture. Here, we apply a global mariculture production model that accounts for changing ocean conditions, suitable marine area for farming, fishmeal and fish oil production, farmed species dietary demand, farmed fish price, and global seafood demand to project mariculture production under two climate and socioeconomic scenarios. We include 85 farmed marine fish and molluscs species, representing about 70% of all mariculture production in 2015. Results show positive global mariculture production changes by the mid and end of the 21st century relative to the 2000s under the SSP1-2.6 scenario with an increase of 33%±6 and 17%±5 respectively. However, under the SSP5-8.5 scenario, an increase of 8%±5 is projected, which peaks by the mid-century and declines by 16%±5 towards the end of the 21st century. More than 25% of mariculture-producing nations are projected to lose 40-90% of their current mariculture production potential under SSP5-8.5 by mid-century. Projected impacts are mainly due to the direct ocean warming effects on farmed species and suitable marine areas, and the indirect impacts of changing availability of forage fishes supplies to produce aquafeed. Fishmeal replacement with alternative protein can lower climate impacts on a subset of finfish production. However, such adaptation measures do not apply to regions dominated by non-feed-based farming (i.e., molluscs) and regions losing substantial marine areas suitable for mariculture. Our study highlights the importance of strong mitigation efforts and the need for different climate adaptation options tailored to the diversity of mariculture systems, to support climate-resilient mariculture development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.017 |
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".