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Record W2166088672 · doi:10.2980/21-2-3692

Liana distribution in response to urbanization in temperate forests

2014· article· en· W2166088672 on OpenAlexafffundvenueabout
Marie‐Hélène Brice, Alexandre Bergeron, Stéphanie Pellerin

Bibliographic record

VenueEcoscience · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMediterranean and Iberian flora and fauna
Canadian institutionsUniversité de MontréalMcGill UniversityEspace pour la vie
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLianaTemperate rainforestUrbanizationTemperate forestTemperate climateEcologyGeographyAgroforestryEnvironmental scienceBiologyEcosystem

Abstract

fetched live from OpenAlex

Urbanization results in ecosystem fragmentation, habitat loss, and altered environmental conditions that usually favour pioneer and ruderal species. The objective of this study was to evaluate the impact of urban conditions on liana abundance in temperate forests. Fieldwork was conducted in 50 forests of the metropolitan Montréal area (Quebec, Canada) and focused on the 6 most common lianas of the study area, Celastrus scandens, Menispermum canadense, Parthenocissus quinquefolia, Solanum dulcamara, Toxicodendron radicans, and Vitis riparia. Potential drivers of liana distribution at the landscape scale (e.g., surrounding land use, urban heat island) were quantified based on satellite images and land use maps. At the forest scale, we investigated biotic and abiotic variables in 429 sampling plots. We found that at the landscape scale, lianas benefited from urbanization, mainly through warm microclimates created by urban heat islands (UHI) as lianas are not well adapted to cold climates. At the forest scale, lianas were more abundant in disturbed forests and in edge habitats than in less disturbed forest and core habitats. Their fast growth rate enables them to quickly take advantage of high light availability on disturbed sites. Our results suggest that urbanization and ongoing climate changes will lead to an increase in liana abundance in temperate forests.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.135

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.211
Teacher spread0.198 · 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

Citations14
Published2014
Admission routes4
Has abstractyes

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