Responses of insect pests, pathogens, and invasive plant species to climate change in the forests of northeastern North America: What can we predict?This article is one of a selection of papers from NE Forests 2100: A Synthesis of Climate Change Impacts on Forests of the Northeastern US and Eastern Canada.
Bibliographic record
Abstract
Climate models project that by 2100, the northeastern US and eastern Canada will warm by approximately 3–5 °C, with increased winter precipitation. These changes will affect trees directly and also indirectly through effects on “nuisance” species, such as insect pests, pathogens, and invasive plants. We review how basic ecological principles can be used to predict nuisance species’ responses to climate change and how this is likely to impact northeastern forests. We then examine in detail the potential responses of two pest species (hemlock woolly adelgid ( Adelges tsugae Annand) and forest tent caterpillar ( Malacosoma disstria Hubner)), two pathogens (armillaria root rot ( Armillaria spp.) and beech bark disease ( Cryptococcus fagisuga Lind. + Neonectria spp.)), and two invasive plant species (glossy buckthorn ( Frangula alnus Mill.) and oriental bittersweet ( Celastrus orbiculatus Thunb.)). Several of these species are likely to have stronger or more widespread effects on forest composition and structure under the projected climate. However, uncertainty pervades our predictions because we lack adequate data on the species and because some species depend on complex, incompletely understood, unstable relationships. While targeted research will increase our confidence in making predictions, some uncertainty will always persist. Therefore, we encourage policies that allow for this uncertainty by considering a wide range of possible scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".