A pioneering pest: the winter moth (<i>Operophtera brumata</i>) is expanding its outbreak range into Low Arctic shrub tundra
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".