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Record W2096358120 · doi:10.4039/tce.2012.111

Seasonal patterns in the structure of epigeic beetle (Coleoptera) assemblages in two subarctic habitats in Nunavut, Canada

2013· article· en· W2096358120 on OpenAlexaffabout
Crystal M. Ernst, Christopher M. Buddle

Bibliographic record

VenueThe Canadian Entomologist · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsMcGill University
Fundersnot available
KeywordsEpigealEcologyHabitatSubarctic climateFaunaBiomass (ecology)BiologyGeography

Abstract

fetched live from OpenAlex

Abstract Seasonal patterns in the taxonomic and functional structure of epigeic Coleoptera assemblages in wet and mesic habitats were studied in Kugluktuk, Nunavut, Canada. Using pan and pitfall traps, 2638 beetles were collected between 21 June and 13 August 2010. Fifty species (including 17 new territory records) in 11 families were identified. The biomass of each specimen was estimated, and each was assigned to a functional group. Species composition differed between habitats throughout the active season and there was a rapid compositional turnover even though species diversity was similar in both habitats and among sampling periods. The functional beetle assemblages in the two habitats were different, and both assemblages experienced seasonal turnover in function; this effect was more pronounced in the mesic habitats. The beetle fauna in both habitats was predominantly entomophagous. We also examined the influence of seasonal weather patterns on assemblage structure: there is a significant relationship between mean daily temperature and assemblage structure. This relationship indicates that changes in weather (or longer-term changes in climate) could affect the diversity and ecological function of insects in this system. Given the significance of insects in the north, this could result in important changes to northern ecology.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.988

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.018
GPT teacher head0.245
Teacher spread0.228 · 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.

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

Citations21
Published2013
Admission routes2
Has abstractyes

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