Climate, Host Abundance and Spread: Unravelling the Drivers of Forest Pest Distributions in North America
Bibliographic record
Abstract
ABSTRACT Aim Forest pathogens, insect pests and parasitic plants are among the most important disturbance agents in forested ecosystems. Understanding where pests occur and where they might occur in the future will be important for understanding their impacts on host trees, and planning for future pest outbreaks. Location North America. Taxon Insect pests, pathogens and parasitic plants of forest trees. Methods Here, we develop and implement a framework to predict the contemporary distributions of 26 pest species that accounts for climate, host abundance and, for non‐native species, their spread on the landscape. Results We show that pest distributions can be predicted primarily by climatic variables. The abundance of individual host trees had only minor explanatory power, but the summed total of host abundance frequently had greater importance—suggesting forest composition and the relative frequency of hosts and non‐hosts place strong limits on pest distributions. Non‐native pests were strongly impacted by the distance from their original discovery location in North America, which tended to interact with climate variables—suggesting most non‐native pests are not yet at equilibrium with their potential climatic ranges in North America. Main Conclusions This work helps to clarify the generalised controls on pest distributions and provide a framework for predicting pest distributions in future climates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".