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Record W3111165119 · doi:10.1111/afe.12429

The structure of temperate yellowjacket communities is affected by land development and loss of forest cover

2020· article· en· W3111165119 on OpenAlexafffund
J.L. Maclean, Lara van Akker, Brian H. Van Hezewijk

Bibliographic record

VenueAgricultural and Forest Entomology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersNatural Resources Canada
KeywordsBiologyEcologyGeneralist and specialist speciesAbundance (ecology)Species richnessDisturbance (geology)Temperate rainforestEcosystemHabitat

Abstract

fetched live from OpenAlex

Abstract As anthropogenic disturbance continues to encroach on natural areas, it is imperative to establish how this disturbance affects species assemblages. Yellowjackets are important predators of a wide range of arthropods, acting as natural population control in many ecosystems. This study seeks to explore how Vespinae community structure shifts with increasing land development in a temperate North American environment. Yellowjackets were sampled from May to September 2019 using heptyl butyrate and acetic acid plus isobutanol traps. Sampling sites represented a gradient of developed landscapes, from fully forested to entirely developed. Seven species from the genera Dolichovespula and Vespula were trapped during the study. Yellowjacket community structure was found to be significantly affected by the amount of land development. These results were driven by the replacement of Vespula consobrina with Vespula germanica in urban landscapes. A high level of development, greater than 75%, reduced species abundance relative to the fully forested landscapes, primarily due to the loss of Vespula pensylvanica and V. consobrina . Our results highlight that the replacement of forested areas with urban development causes a shift in yellowjacket community composition, favouring generalist scavengers (e.g. V. germanica ) and threatening the abundance of forest specialists such as V. consobrina .

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 score0.005
Threshold uncertainty score0.261

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 studies0.0000.001
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 head0.006
GPT teacher head0.189
Teacher spread0.183 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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