Rebuilding fish biomass for the world's marine ecoregions under climate change
Bibliographic record
Abstract
Rebuilding overexploited marine populations is an important step to achieve the United Nations' Sustainable Development Goal 14-Life Below Water. Mitigating major human pressures is required to achieve rebuilding goals. Climate change is one such key pressure, impacting fish and invertebrate populations by changing their biomass and biogeography. Here, combining projection from a dynamic bioclimate envelope model with published estimates of status of exploited populations from a catch-based analysis, we analyze the effects of different global warming and fishing levels on biomass rebuilding for the exploited species in 226 marine ecoregions of the world. Fifty three percent (121) of the marine ecoregions have significant (at 5% level) relationship between biomass and global warming level. Without climate change and under a target fishing mortality rate relative to the level required for maximum sustainable yield of 0.75, we project biomass rebuilding of 1.7-2.7 times (interquartile range) of current (average 2014-2018) levels across marine ecoregions. When global warming level is at 1.5 and 2.6°C, respectively, such biomass rebuilding drops to 1.4-2.0 and 1.1-1.5 times of current levels, with 10% and 25% of the ecoregions showing no biomass rebuilding, respectively. Marine ecoregions where biomass rebuilding is largely impacted by climate change are in West Africa, the Indo-Pacific, the central and south Pacific, and the Eastern Tropical Pacific. Coastal communities in these ecoregions are highly dependent on fisheries for livelihoods and nutrition security. Lowering the targeted fishing level and keeping global warming below 1.5°C are projected to enable more climate-sensitive ecoregions to rebuild biomass. However, our findings also underscore the need to resolve trade-offs between climate-resilient biomass rebuilding and the high near-term demand for seafood to support the well-being of coastal communities across the tropics.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".