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Record W4408031024 · doi:10.1139/as-2024-0076

Limited short-term impact of lemming grazing on vascular plants under experimentally reduced predation in the High Arctic

2025· article· en· W4408031024 on OpenAlexafffundvenueabout
Gilles Gauthier, Guillaume Slevan-Tremblay, Dominique Fauteux, Esther Lévesque

Bibliographic record

VenueArctic Science · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité du Québec à Trois-RivièresCanadian Museum of NatureUniversité LavalCenter for Northern Studies
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPredationGrazingTerm (time)ArcticEcologyThe arcticBiologyEnvironmental scienceOceanographyPhysicsGeology

Abstract

fetched live from OpenAlex

Population fluctuations of lemmings in the High Arctic appear to be driven by predator–prey interactions. However, lemming grazing can sometimes have a strong impact on the vegetation during population peaks, suggesting a possible role of plant–herbivore interactions. We use a large-scale experiment where predators were excluded to investigate whether predator reduction could have cascading effects on the vegetation through an increase in lemming densities in the Canadian Arctic. Morphological traits and biomass of Salix arctica and the biomass of Poaceae and Juncaceae were sampled inside and outside lemming exclosures. We detected signs of lemming grazing on the number of buds and catkins of S. arctica at snowmelt, and stem length, stem growth, and number of leaves during the summer but the impact was relatively small. We did not detect an impact of grazing on plant biomass during the summer. We also found limited evidence that the impact of grazing was higher in the predator exclosure even though lemming density increased up to two-fold. Our results suggest that the short-term impact of lemmings on vascular plants is relatively small and that an experimental increase in lemming density did not have a cascading effect on the plants consumed by these herbivores in the Canadian Arctic.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.997

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 studies0.0000.000
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 head0.051
GPT teacher head0.314
Teacher spread0.262 · 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

Citations3
Published2025
Admission routes4
Has abstractyes

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