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Record W2811503651 · doi:10.1080/11956860.2018.1564484

Predatory hymenopteran assemblages in boreal Alaska: associations with forest composition and post-fire succession

2019· article· en· W2811503651 on OpenAlexvenueno aff
Alexandria Wenninger, Teresa N. Hollingsworth, Diane Wagner

Bibliographic record

VenueEcoscience · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsEcologyEcological successionSpecies richnessChronosequenceTaigaBiologyDominance (genetics)DeciduousAbundance (ecology)

Abstract

fetched live from OpenAlex

Changes to the fire regime in boreal Alaska are shifting the ratio of coniferous to deciduous dominance on the landscape. The increase in aspen and birch may have important effects on predatory hymenopteran assemblages by providing a source of extrafloral nectar and increasing prey availability. Furthermore, fire-induced changes in successional age alter habitat structure and microclimate in ways that may favor ants. This study is the first to characterize the influence of fire-related vegetation changes on boreal predatory hymenopteran assemblages. We compare the abundance, species richness, and composition of predatory hymenopteran assemblages among forests at different stages of succession and of varying post-fire tree species compositions. Ant assemblages were weakly related to forest composition, but ants were significantly more abundant and speciose in early-successional forests than in mid-late successional forests. In contrast, macropterous wasp morphospecies richness and abundance, and micropterous wasp abundance, were positively related to the basal area of aspen, but were not related to successional stage. The results suggest that shifts in boreal vegetation related to climate warming will result in changes to the predaceous insect community, with ants responding positively to disturbance and wasps responding positively to an increase in the representation of aspen on the landscape.

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 score0.116
Threshold uncertainty score0.343

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 science0.0000.000
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 head0.014
GPT teacher head0.204
Teacher spread0.190 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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