Conserving the beauty of the world’s reef fish assemblages
Bibliographic record
Abstract
On shallow rocky and coral reefs, cultural and recreational values, like aesthetics, are critical aspects of Nature's Contributions to People (NCP) that support human well-being and provide billions of dollars in tourism revenue. Quantifying the aesthetic value of reef ecosystems and uncovering the conditions that enhance it could support NCP-based management. Here, we combine a global dataset of reef fish surveys, species-level aesthetic values, and causal modeling to assess the global status and drivers of reef fish assemblage aesthetic value. We find that aesthetic value is inherently linked to species richness, displaying a latitudinal gradient with peaks in the tropics, but varies strongly with the presence of exceptionally beautiful or less-beautiful species. Sea surface temperature, primary productivity, human gravity, and protection status are the strongest drivers of assemblage-level aesthetic value. Protection against human impacts consistently enhances aesthetic value by boosting taxonomic and phylogenetic diversity, and this effect is greatest in species-rich, tropical ecoregions. Economic development has little influence, indicating that low-income countries are not constrained from maintaining beautiful fish assemblages. Our results therefore suggest that marine protected areas (MPAs) can support multiple NCPs simultaneously, particularly in developing tropical countries. While we highlight the effectiveness of MPAs, given the low level of marine protection globally and the sensitivity of aesthetic value to environmental conditions, the beauty of the world's reefs appears severely threatened. Aesthetic value should be immediately integrated into reef conservation and management plans.
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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 | 0.001 | 0.002 |
| 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) | 0.003 | 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".