Genomic Exploration of Climate-driven Evolution and Evolutionary Convergence in Forest Pathogens
Bibliographic record
Abstract
Climate significantly influences the distribution, composition, and diversity of fungal communities, impacting the growth, spread, and virulence of fungal forest pathogens. This study employs advanced landscape genomics methods to explore the genomic adaptations of three major fungal pathogens: Those responsible for Dutch elm disease, dothistroma needle blight, and Swiss needle cast. Our findings reveal that precipitation and humidity are primary drivers of adaptation in these species. We use these insights to forecast potential adaptations under future climate scenarios (genomic offset) and identify specific genes and pathways associated with climate responses in each pathogen. Notably, we detect a convergence in moisture adaptation across these distantly related species, particularly in genes related to the cytoskeleton and transporters. This study enhances our understanding of fungal pathogen evolution in response to climate change, offering crucial insights for forest disease management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".