Quantifying the Nutritional and Socio‐Ecological Dimensions of Indian Ocean Fisheries
Bibliographic record
Abstract
ABSTRACT Seafood from marine fisheries, such as finfishes and invertebrates, is an important source of nutrients for billions of people globally. Seafood species vary in their micronutrient concentration, their economic value, and their vulnerability to exploitation and climate change. However, fisheries management has rarely considered the nutritional quality of fisheries catches and their relation to economic, conservation and climate vulnerability dimensions. Here, we quantified and analysed the micronutrient supply and average micronutrient concentration of taxa exploited by fisheries in the Indian Ocean. We also assessed associations among taxon‐specific micronutrient concentrations, ex‐vessel prices, fishing vulnerability and climate vulnerability. We found that small pelagic finfishes, despite contributing little to the overall catch weight, were particularly rich in micronutrients, were resilient and low priced, highlighting their utility in food and nutritional security. In contrast, taxa such as tunas and cephalopods were less nutrient‐dense, more vulnerable and had higher ex‐vessel prices. Results also showed differences in catch micronutrient concentrations between countries within the Indian Ocean Rim (IOR) and Distant Water Fishing (DWF) countries. IOR country catches were dominated by taxa richer in calcium, omega‐3 fatty acids and iron but with higher climate vulnerability. DWF catches, which accounted for only 2% of the Indian Ocean's total micronutrient supplies, were relatively richer in selenium, more vulnerable to fishing and had higher ex‐vessel prices. Our results highlight the trade‐offs and synergies among nutritional, economic, conservation and climate resilience dimensions of Indian Ocean fisheries, providing key insights for nutrition‐sensitive fisheries management strategies aimed at balancing multiple priorities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".