Micronutrient supply from global marine fisheries under climate change and overfishing
Bibliographic record
Abstract
Fish are rich in bioavailable micronutrients, such as zinc and iron, deficiencies of which are a global food security concern. 1 , 2 Global marine fisheries yields are threatened by climate change and overfishing, 3 , 4 yet understanding of how these stressors affect the nutrients available from fisheries is lacking. 5 , 6 Here, using global assessments of micronutrient content 2 and fisheries catch data, 7 we investigate how the vulnerability status of marine fish species 8 , 9 may translate into vulnerability of micronutrient availability at scales of both individual species and entire fishery assemblages for 157 countries. We further quantify the micronutrient evenness of catches to identify countries where interventions can optimize micronutrient supply. Our global analysis, including >800 marine fish species, reveals that, at a species level, micronutrient availability and vulnerability to both climate change and overfishing varies greatly, with tropical species displaying a positive co-tolerance, indicating greater persistence to both stressors at a community level. 10 Global fisheries catches had relatively low nutritional vulnerability to fishing. Catches with higher species richness tend to be nutrient dense and evenly distributed but are more vulnerable to climate change, with 40% of countries displaying high vulnerability. Countries with high prevalence of inadequate micronutrient intake tend to have the most nutrient-dense catches, but these same fisheries are highly vulnerable to climate change, with relatively lower capacity to adapt. 11 Our analysis highlights the need to consolidate fisheries, climate, and food policies to secure the sustainable contribution of fish-derived micronutrients to food and nutrition security.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".