Trophic distribution of nutrient production in coral reef fisheries
Bibliographic record
Abstract
Coral reef fisheries supply nutritious catch to tropical coastal communities, where the quality of reef seafood is determined by both the rate of biomass production and nutritional value of reef fishes. Yet our understanding of reef fisheries typically uses targets of total reef fish biomass rather than individual growth (i.e. biomass production) and nutrient content (i.e. nutritional value of reef fish), limiting the ability of management to sustain the productivity of nutritious catches. Here, we use modelled growth coefficients and nutrient concentrations to develop a new metric of nutrient productivity of coral reef fishes. We then evaluate this metric with underwater visual surveys of reef fish assemblages from four tropical countries to examine nutrient productivity of reef fish food webs. Species' growth coefficients were associated with nutrients that vary with body size (calcium, iron, selenium and zinc), but not total nutrient density. When integrated with fish abundance data, we find that herbivorous species typically dominate standing biomass, biomass turnover and nutrient production on coral reefs. Such bottom-heavy trophic distributions of nutrients were consistent across gradients of fishing pressure and benthic composition. We conclude that management restrictions that promote sustainability of herbivores and other low trophic-level species can sustain biomass and nutrient production from reef fisheries that is critical to the food security of over 500 million people in 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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".