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Record W4416110761 · doi:10.1111/conl.13154

Connectivity of Forest Patches via Wooded Corridors Increases Biodiversity at Low, but Not High, Forest Amounts

2025· article· en· W4416110761 on OpenAlexafffundabout
Lindsay Daly, Joe Gabriel, Adrianne Hajdasz, Amanda E. Martin, Greg W. Mitchell, Adam C. Smith, Lenore Fahrig

Bibliographic record

VenueConservation Letters · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsEnvironment and Climate Change CanadaCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change CanadaNature Conservancy of Canada
KeywordsBiodiversityForest restorationIntact forest landscapeForest structureForest ecologyForest farmingSecondary forestBiodiversity conservation

Abstract

fetched live from OpenAlex

ABSTRACT To determine whether we can reduce the impacts of forest loss on biodiversity by altering forest pattern, we need to estimate the effects of forest pattern independent of forest amount. We evaluated the independent and interactive effects of forest amount, fragmentation, and connectivity (wooded corridors) on diversity of forest‐associated plants, small mammals, and birds. We selected 70 forest sites in eastern Ontario, Canada with low correlations between these landscape predictors. We found positive effects of forest amount, neutral or positive effects of forest fragmentation, and an interaction effect between connectivity and forest amount. In landscapes with low forest amount, biodiversity increased with connectivity, while at high forest amount, biodiversity decreased with connectivity. Thus, forest patches should be protected regardless of size, and conservation actions aimed at improving connectivity by adding wooded corridors should be prioritized in areas where forest is scarce, for example agricultural and urban areas.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.076
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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

Citations3
Published2025
Admission routes3
Has abstractyes

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