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Record W4412704230 · doi:10.1073/pnas.2410937122

From science to impact: Conserving ecological connectivity in large conservation landscapes

2025· review· en· W4412704230 on OpenAlexaff
Robin Naidoo, Cody M. Aylward, Wendy Elliott, Annika T. H. Keeley, Margaret F. Kinnaird, Michael Knight, Cristian Remus Papp, Kanchan Thapa, Rafael Antelo

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiodiversityEcosystem servicesEnvironmental resource managementLeverage (statistics)GeographyEcologyLandscape connectivityEcosystemEnvironmental planningEnvironmental scienceComputer scienceBiologyPopulation

Abstract

fetched live from OpenAlex

Implementing ecological connectivity conservation in large landscapes requires cutting-edge science combined with consideration of ecological, socioeconomic, and cultural factors that collectively shape the outcomes of conservation efforts. We outline a theory of change (ToC) for connectivity conservation to improve the ecological condition of landscapes and biodiversity and the ecosystem services upon which humans depend. We review connectivity conservation efforts on four continents in large landscapes that span gradients of latitude, fragmentation, biodiversity value, socioeconomic characteristics, and the richness of data used to assess connectivity and target action. We share the substantial but variable progress made in each landscape and outline specific challenges to achieving conservation goals. Opportunities and challenges in public and private sectors can further leverage the potential of large-scale connectivity conservation to reduce isolation and improve gene flow in functional landscapes worldwide.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.240
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.362
Teacher spread0.317 · 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 teacher head, 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

Citations11
Published2025
Admission routes1
Has abstractyes

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Same venueProceedings of the National Academy of SciencesSame topicWildlife-Road Interactions and ConservationFrench-language works237,207