Climate change and forest diseases: using today’s knowledge to address future challenges
Bibliographic record
Abstract
The health of the earth’s forests and urban green spaces is increasingly challenged by the outcomes of human activities, including global climate change. As climate changes, the role and impact of diseases on trees in both forest ecosystems and in urban settings will also change. Knowledge of relationships between climate variables and diseases affecting forest and urban trees is reviewed, with specific emphasis on those affecting foliage, shoots, and stems. Evidence that forest diseases are already responding to the earth’s changing climate is examined (e.g., Dothistroma needle blight in northern British Columbia) as are predicted scenarios for future changes in impact on forests by other tree diseases. Outbreaks of tree diseases caused by native and alien pathogens are predicted to become more frequent and intense – this and other general predictions about the effects of climate change on forest and tree diseases are discussed. Despite the uncertainty that accompanies such predictions it is imperative that researchers, forest and urban tree managers, and policy makers work together to develop and implement management strategies that enhance the resilience of the worlds’ forests and urbanized trees. Strategies discussed include monitoring, forecasting, planning, and mitigation.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".