Managing nutrition-biodiversity trade-offs on coral reefs
Bibliographic record
Abstract
Coral reefs support an incredible abundance and diversity of fish species, with reef-associated fisheries providing important sources of income, food, and dietary micronutrients to millions of people across the tropics. However, the rapid degradation of the world's coral reefs and the decline in their biodiversity may limit their capacity to supply nutritious and affordable seafood while meeting conservation goals for sustainability. Here, we conduct a global-scale analysis of how the nutritional quality of reef fish assemblages (nutritional contribution to the recommended daily intake of calcium, iron, and zinc contained in an average 100 g fish on the reef) relates to key environmental, socioeconomic, and ecological conditions, including two key metrics of fish biodiversity. Our global analysis of more than 1,600 tropical reefs reveals that fish trophic composition is a more important driver of micronutrient concentrations than socioeconomic and environmental conditions. Specifically, micronutrient density increases as the relative biomass of herbivores and detritivores increases at lower overall biomass or under high human pressure. This suggests that the provision of essential micronutrients can be maintained or even increase where fish biomass decreases, reinforcing the need for policies that ensure sustainable fishing, and that these micronutrients are retained locally for nutrition. Furthermore, we found a negative association between micronutrient density and two metrics of fish biodiversity, revealing an important nutrition-biodiversity trade-off. Protecting reefs with high levels of biodiversity maintains key ecosystem functions, whereas sustainable fisheries management in locations with high micronutrient density could sustain the essential supply of micronutrients to coastal human communities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".