A global map of species at risk of extinction due to natural hazards
Bibliographic record
Abstract
An often-overlooked question of the biodiversity crisis is how natural hazards contribute to species extinction risk. To address this issue, we explored how four natural hazards, earthquakes, hurricanes, tsunamis, and volcanoes, overlapped with the distribution ranges of amphibians, birds, mammals, and reptiles that have either narrow distributions or populations with few mature individuals. To assess which species are at risk from these natural hazards, we combined the frequency and magnitude of each natural hazard to estimate their impact. We considered species at risk if they overlapped with regions where any of the four natural hazards historically occurred (n = 3,722). Those species with at least a quarter of their range subjected to a high relative impact were considered at high risk (n = 2,001) of extinction due to natural hazards. In total, 834 reptiles, 617 amphibians, 302 birds, and 248 mammals were at high risk and they were mainly distributed on islands and in the tropics. Hurricanes (n = 983) and earthquakes (n = 868) affected most species, while tsunamis (n = 272), and volcanoes (n = 171) affected considerably fewer. The region with the highest number of species at high risk was the Pacific Ring of Fire, especially due to volcanoes, earthquakes, and tsunamis, while hurricane-related high-risk species were concentrated in the Caribbean Sea, Gulf of Mexico, and northwestern Pacific Ocean. Our study provides important information regarding the species at risk due to natural hazards and can help guide conservation attention and efforts to safeguard their survival.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.004 | 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".