Predicting Species Extinction Threats Using Occurrence Data From the Global Biodiversity Information Facility
Bibliographic record
Abstract
Global biodiversity continues to decline, underscoring the urgency for systematic conservation planning. Effective action requires reliable information on species distributions and the pressures they face. This study uses occurrence data from the Global Biodiversity Information Facility (GBIF) to construct extinction threat indicators for over 600,000 terrestrial and marine species, drawing on secondary data for range size, formal protection, and species-specific pressures. For terrestrial species, additional indicators include population density and sensitivity to human encroachment, while marine species are assessed using commercial fishing intensity, Exclusive Economic Zone (EEZ) jurisdiction, and coastal population pressure. An ordered logit model is estimated using 87,731 species already evaluated by the International Union for Conservation of Nature (IUCN), linking threat levels to IUCN’s five risk categories: Least Concern, Near Threatened, Vulnerable, Endangered, and Critically Endangered. The resulting parameters are then applied to 512,675 additional species in the GBIF dataset without IUCN ratings, projecting their extinction probabilities. The findings reveal a much larger pool of species at elevated risk than previously recognized and generate substantially revised maps of global conservation hotspots and priority areas. The approach is transparent, adaptable, and capable of integrating new records as GBIF coverage expands, offering a practical tool for monitoring biodiversity threats worldwide.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 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) | 0.001 | 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".