Can climate variability and landscape position predict white pine blister rust incidence, mortality due to the disease, and regeneration in whitebark pine?
Bibliographic record
Abstract
Invasive forest pathogens, like Cronartium ribicola (Fisch), the fungus that causes white pine blister rust, threaten native tree species. Federally listed whitebark pine ( Pinus albicaulis Engelmann) is highly susceptible and faces extensive mortality due to this disease. Understanding infection conditions and disease incidence variability is crucial for management and restoration efforts. We surveyed whitebark pine stands in Glacier National Park, Montana, USA from 2020 to 2022, examining the impact of landform and climate on tree mortality, rust incidence, cone production, and regeneration. Our analysis revealed that tree diameter, elevation, and aspect significantly influenced mortality, rust incidence, and cone production, while climatic factors such as spring solar radiation, humidity, and late summer snowpack also played key roles. Regeneration was primarily affected by elevation, geographic location, and humidity. Although landform variables similarly predicted disease incidence in this and other studies, climatic drivers varied by region, emphasizing the need to consider region-specific landform and climate for effective management. Our study highlights the importance of protecting large trees, which harbor genetic diversity crucial for recovery and adaptation to disturbance and climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".