The importance of Indigenous Peoples’ lands for the conservation of terrestrial vertebrates
Bibliographic record
Abstract
Abstract Indigenous Peoples’ lands cover over one-quarter of the Earth’s surface, a significant proportion of which is still free from industrial-level human impacts. As a result, Indigenous Peoples’ lands are crucial for the long-term persistence of Earth’s biodiversity and ecosystem services. Yet, information on species composition within Indigenous Peoples’ lands globally remains unknown. Here, we provide the first comprehensive analysis of terrestrial vertebrate composition across mapped Indigenous lands by using distribution range data for 20,328 IUCN-assessed mammal, bird and amphibian species. We estimate that 12,521 species (62%) have ≥10% of their ranges in Indigenous Peoples’ lands, and 3,314 species (16%) have >half of their ranges within these lands. For threatened species assessed, 1,878 (41.5% of all threatened of all threatened mammals, birds and amphibians) occur in Indigenous Peoples’ lands. We also find that 3,989 species (of which 418 are threatened) have ≥10% of their range in Indigenous Peoples’ lands that have low human pressure. Our results are conservative because not all known Indigenous lands are mapped, and this analysis shows how important Indigenous Peoples’ lands are for the successful implementation of international conservation and sustainable development agendas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".