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Record W7033796893

Quantifying conservation outcomes in Indigenous peoples’ lands across the tropics

2023· dissertation· en· W7033796893 on OpenAlexaboutno aff

Bibliographic record

VenueWhite Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsnot available
FundersGrantham Centre for Sustainable Futures
KeywordsDeforestation (computer science)BiomeTropicsBiodiversityIndigenousClimate changeHabitat destructionHabitat
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.062
GPT teacher head0.323
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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