Quantifying conservation outcomes in Indigenous peoples’ lands across the tropics
Bibliographic record
Abstract
Biodiversity loss and climate change represent some of the biggest challenges humanity currently faces, with habitat loss as the biggest proximate driver. Area-based conservation is a key conservation policy and recent international conservation targets aimed for at least 30% of terrestrial, inland water, and of coastal and marine areas to be effectively conserved and managed \nthrough protected areas and other effective area-based conservation measures, recognising Indigenous and traditional territories, by 2030. While the effectiveness of protected areas (PAs) in achieving conservation outcomes has received much research attention in recent years, there still remains a gap in a global-scale understanding for Indigenous lands (ILs). Focusing on tropical forests, as globally important biomes for biodiversity and climate change mitigation, this thesis quantifies three metrics of conservation outcomes on ILs, PAs, the spatial overlap of protected areas and Indigenous lands (PIAs), and non-protected areas across the tropical Americas, Africa, and Asia. In Chapter 2, I examined deforestation and forest degradation rates from 2011-2019 using propensity score matching and generalised linear mixed models. I found that deforestation was \nreduced by 16.8-25.9% and degradation reduced by 9.1-18.4% on ILs compared to non-protected areas across tropical regions, while differences compared to PAs varied between regions. In Chapter 3, I sought to investigate forest integrity using the Forest Landscape Integrity Index which incorporates observed pressures, inferred pressures, and lost connectivity, and long-term human land-use intensity using the Anthromes dataset. Across tropical regions, forest integrity was highest \nand land-use intensity the least in PIAs, but varied in ILs between regions compared to non-protected areas. In Chapter 4, I assessed 11,872 forest-dependent vertebrate species’ Area of Habitat and compared species richness, extinction vulnerability, and range-size rarity inside and outside Indigenous peoples’ lands. At least 76.8% of tropical amphibians, birds, mammals, and reptiles had range overlaps with ILs, with an average range overlap of ~25%. Most countries in the Americas \nhad higher species richness in ILs than outside, whereas most countries in Asia had lower extinction vulnerability scores in ILs, and more countries in Africa and Asia had slightly higher range-size rarity in ILs. Taken together, the thesis reveals the contributions that Indigenous peoples’ lands make towards tropical conservation, in terms of reducing habitat loss, maintaining habitat quality, and providing vital habitat for forest-dependent vertebrate diversity. Supporting and including \nIndigenous peoples in conservation target-setting and planning is not only socially just, it is likely vital to the success of achieving the Kunming-Montreal targets.
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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 | 0.001 | 0.001 |
| 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".