Potential yield and food provisioning gains from rebuilding the world’s coral reef fish stocks
Bibliographic record
Abstract
Many coral reefs have fish stocks that are depleted below the level at which sustainable production is maximized. Lower production means that millions of people are losing out on potential food, income, and livelihoods. Rebuilding these stocks to maximize sustainable production can contribute toward ending hunger and malnutrition but requires active and effective fisheries management. Yet, for fish stock recovery plans to be implemented, recovery benefits, targets, and timeframes need to be quantified. Here, using 1,211 individual reef sites and 23 jurisdictions identified globally as being below maximum sustainable production levels, we show that reefs have the potential to increase sustainable yields by nearly 50% if allowed to recover toward their maximum production levels. For individual jurisdictions, this recovery represents from 20,000 up to 162 million additional sustainable servings of reef fish per year in comparison to current sustainable production, meeting recommended seafood intake for up to 1.4 million additional people a year. However, such growth and food provisioning will require fish stocks to double their standing biomass (increase by a median of 32 t/km 2 ). Recovery timeframes range from 6.4 y under the most stringent scenario (a moratorium) to 49.7 y under the maximum harvest scenario that results in recovery. We find that locations with the greatest potential for sustainable gains in yield are among those with the greatest food and micronutrient deficiencies, underscoring both the challenges and opportunities in recovering fish assemblages to achieve their maximum sustainable potential.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".