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Record W4214491202 · doi:10.1139/as-2021-0027

A pioneering pest: the winter moth (<i>Operophtera brumata</i>) is expanding its outbreak range into Low Arctic shrub tundra

2022· article· en· W4214491202 on OpenAlexvenueno aff
Ole Petter Laksforsmo Vindstad, Jane Uhd Jepsen, Helge Molvig, Rolf A. Ims

Bibliographic record

VenueArctic Science · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersNatureNorges Forskningsråd
KeywordsTundraTaigaShrubEcologyArcticBorealVegetation (pathology)LarchBiology

Abstract

fetched live from OpenAlex

Climate warming allows generalist boreal consumers to expand into Arctic ecosystems. We present experimental and observational field data showing that a generalist boreal insect pest—the winter moth (Operophtera brumata Linnaeus, 1758)—is expanding its outbreak range out of the northern boreal mountain birch forest in northeast Fennoscandia and into the adjacent Low Arctic shrub tundra. This is the first documented example of an outbreaking boreal insect pest expanding into a tundra ecosystem. The expansion has coincided with a long-term advancing trend in the expected hatching date of moth eggs in spring for the study region. We show that the winter moth can complete development on Low Arctic willows and that the density of winter moth larvae in willow thickets is unrelated to the amount of mountain birch (the main host plant in northern boreal forest) in the thickets. However, we also demonstrate that larval densities on willows show a regional-scale spatial decline when moving away from the birch forest and into the shrub tundra. Continued monitoring is needed to establish whether the outbreaks will spread farther into the tundra. The expansion of outbreaking boreal pests into the tundra could alter conventional expectations of increasing vegetation productivity and shrubification in tundra ecosystems.

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 categoriesScience and technology studies, Insufficient 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 score0.075
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.001

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 head0.030
GPT teacher head0.249
Teacher spread0.219 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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