Implication of global climate change on the distribution and activity of Phytophthora ramorum
Bibliographic record
Abstract
Global climate change is predicted to alter the distribution and activity of several forest pathogens. Boland et al. (2004) suggested that climate change might affect pathogen establishment, rate of disease progress, and the duration of epidemics, each in a potentially different way. In some cases, climate changes may favor the onset of disease and potentially accelerate the displacement of a tree species from portions of its current geographic range. In other instances, climate changes may be detrimental to the development of disease. Boland et al. (2004) qualitatively predicted that climate change would have a strong positive net effect on four of 18 tree pathogens in Ontario, Canada and would have a negative net effect on another four pathogens. Phytophthora ramorum is an alien invasive pathogen, likely present in the United States since the mid-1990s. The pathogen is the cause of Sudden Oak Death (SOD) and several other diseases. Infected tanoaks (Lithocarpus spp.) and oaks (Quercus spp.) are found in 14 western counties of California and one county in Oregon. Previous work has suggested that the distribution of the pathogen is affected by regional climate patterns. The purpose of the study was to quantify the potential change in occurrence of climatically suitable habitat for P. ramorum under future climate scenarios. All analyses were conducted with the ecological niche model, CLIMEX. Biological parameters describing the response of the pathogen to temperature and moisture were taken from Venette and Cohen (2006). Baseline and future climate projections, downscaled to a 10minute resolution, were obtained from worldclim.org. Baseline data represented the period from 1961-1990. Climate projections were based on the Canadian General Circulation Model-1 (CGCM1) from the Canadian Centre for Climate Modeling and Analysis under emissions scenario b2 (assumes slowed population growth and reduced greenhouse gas emissions). Climate projections were available for the years 2020, 2050, and 2080. For each year, CLIMEX provided several indices of climatic suitability for the presence of the species. The Ecoclimatic index provides a measure of overall habitat suitability. The Ecoclimatic Index for the baseline climate data gave a qualitatively satisfactory fit to observed occurrences of the pathogen in California. The pathogen was observed more often in areas that were predicted to be favorable or very favorable than in areas predicted to be marginal or unsuitable. Because the model parameters were not estimated directly or indirectly from field observations, the field observations provide a completely independent validation of the model. The baseline model predicts that climatically favorable or very favorable habitat in the contiguous US should currently extend along the west coast from approximately Monterey, CA to Puget Sound, WA. Large areas of climatically suitable habitat also occur in the eastern half of the United States. Based on the predictions from CGCM1, we predict that the area that is favorable or very favorable will decrease substantially in the eastern US, but will increase in WA, OR, and CA. By 2050, favorable habitat will extend from Los Angeles, CA to Puget Sound, WA. Inland progression of climatically favorable habitat, even by 2080, is predicted to be modest. In the eastern US, only fragmented pockets of favorable or very favorable habitat are predicted to occur in far western North Carolina, in the northeast quarter of West Virginia, and a small region from northern New Jersey to the southern half of Massachusetts.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".