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Diversity of canopy and understorey spiders in north‐temperate hardwood forests

2008· article· en· W1990241388 on OpenAlexaffabout
Maxim Larrivée, Christopher M. Buddle

Bibliographic record

VenueAgricultural and Forest Entomology · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsUnderstoryCanopyBeechBiologyEcologyTree canopyHardwoodSpecies diversityMapleTemperate rainforestYellow birchOrdinationAceraceae

Abstract

fetched live from OpenAlex

Abstract 1 We characterized and compared diversity patterns of canopy and understorey spiders (Arachnida: Araneae) on sugar maple ( Acer saccharum Marsh.) and American beech ( Fagus grandifolia Ehrh.) in hardwood forests of southern Québec, Canada. 2 We sampled canopies of 45 sugar maple and 45 American beech trees and associated understorey saplings in mature protected forests near Montréal. Samples were obtained by beating the crown foliage at various heights and by beating saplings around each tree. 3 Eighty‐two species were identified from 13 669 individuals. Forty‐eight species and 3860 individuals and 72 species and 9809 individuals were collected from the canopy and the understorey, respectively. 4 Multivariate analyses (NMDS ordination and NPMANOVA) showed the composition of canopy and understorey assemblages differed significantly, and canopy assemblages differed between tree species. Rank‐abundance distribution models fitted to the canopy and understorey data indicated that different mechanisms structure the assemblages in both habitats. Three abundant spider species were significantly more common in the canopy; ten species were collected significantly more often in the understorey. 5 The forest canopy was shown to be an important reservoir for spider diversity in north‐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)0.000
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 score0.155
Threshold uncertainty score0.857

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.024
GPT teacher head0.186
Teacher spread0.163 · 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

Citations65
Published2008
Admission routes2
Has abstractyes

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