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Record W4414198992 · doi:10.24124/2025/30575

The role of the matrix in shaping extinction risk and conservation opportunities for terrestrial mammals

2025· dissertation· en· W4414198992 on OpenAlexaboutno aff
Juan Pablo Ramírez‐Delgado

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsExtinction (optical mineralogy)Extinction debtHabitatHabitat fragmentationHabitat destructionFragmentation (computing)Species richnessHabitat conservation

Abstract

fetched live from OpenAlex

,The global biodiversity crisis, driven primarily by habitat loss and fragmentation, has traditionally led conservation efforts to focus almost exclusively on primary habitats. However, the ecological importance of the matrix, defined as areas surrounding primary habitat, has remained largely overlooked. In this dissertation, I used global-scale spatial analyses coupled with statistical modeling to (i) quantify how matrix condition influences the effects of habitat fragmentation on extinction risk for terrestrial mammals, (ii) compare the predictive performance of alternative habitat intactness models (patch-matrix, continuum, and hybrid models) for assessing extinction risk in terrestrial mammals, and (iii) map global patterns of terrestrial mammal species richness within the matrix to identify conservation opportunities beyond primary habitats. My findings demonstrate that matrix condition plays a key mediating role in the relationship between habitat fragmentation and extinction risk, with greater predictive power than habitat loss or habitat amount alone. Moreover, I found that the predictive importance of fragmentation increases as matrix condition deteriorates, suggesting that managing or restoring the matrix represents a strategic conservation action to mitigate the negative effects of fragmentation on biodiversity. Additionally, the hybrid habitat intactness model—which integrates discrete habitat patches with continuous gradients of habitat quality—consistently outperforms traditional patch-matrix and continuum models, regardless of species’ habitat specialization. Notably, the magnitude of the relationship between habitat intactness and extinction risk was greater when using the hybrid model, highlighting that integrating discrete and continuous habitat representations can improve extinction risk analyses and provide valuable insights for conservation. My results further reveal that hotspots of species richness within the matrix occupy only about 1% of Earth's terrestrial surface, yet could support more than half of all terrestrial mammal species. Matrix areas identified as having high conservation potential—based on overlapping richness hotspots—are primarily concentrated in tropical strongholds such as the Amazon Basin, Colombian Tropical Andes, Brazilian Atlantic Forest, and Albertine Rift. Importantly, many of these matrix areas face intense human pressures and remain inadequately represented within existing protected areas and other area-based conservation measures, underscoring their value as strategic opportunities for biodiversity conservation. Collectively, my results highlight an urgent need for a paradigm shift in conservation strategies that explicitly recognize, manage, and restore matrix areas as integral components of global biodiversity conservation. Integrating the matrix into conservation planning closely aligns with international biodiversity frameworks, particularly Target 2 of the Kunming-Montreal Global Biodiversity Framework, which calls for restoring at least 30% of degraded terrestrial ecosystems to enhance ecological integrity and connectivity. Such integration could substantially improve biodiversity outcomes, ecosystem resilience, and landscape connectivity, ultimately making critical contributions toward reversing global biodiversity declines.

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)0.005
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.258
Teacher spread0.229 · 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
Published2025
Admission routes1
Has abstractyes

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