A global map of human pressures on tropical coral reefs
Bibliographic record
Abstract
Abstract As human activities on the world's oceans intensify, mapping human pressure is essential to develop appropriate conservation strategies and prioritize investments with limited resources. Here, we map six human (nonclimatic) pressures on coral reefs using the latest quantitative data on fishing, water pollution (nitrogen and sediments), coastal population, industrial development, and tourism. Using a percentile approach to rank different stressors, we identify the top‐ranked local pressure and estimate a cumulative pressure index for 54,596 global coral reef pixels at 0.05° (∼5 km) resolution. We find that coral reefs are exposed to multiple intense local pressures: fishing and water pollution (nutrients and sediments) are the most common top‐ranked pressures worldwide (in 30.8% and 32.3% of reef cells, respectively), although each pressure was ranked as a top pressure in some locations. We also find that local pressures are similar inside and outside a proposed global portfolio of coral reef climate refugia, suggesting that even potential climate refugia have high levels of local human pressure that require effective management. Our findings and datasets provide the best available information that can ensure local pressures are effectively managed across the world's coral reefs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".