A Spatial Model for Predicting Effects of Climate Change on Swiss Needle Cast Disease Severity in Pacific Northwest Forests

Swiss needle cast disease of Douglas-fir (Pseudotsuga menziesii) is caused by the ascomycete fungus Phaeocryptopus gaeumannii. Symptoms of the disease are foliage chlorosis and premature needle abscission due to occlusion of stomata by the ascocarps of the pathogen, resulting in impaired needle gas exchange. Severe defoliation and growth losses of 20 to 50 percent due to Swiss needle cast have been reported for about 150,000 ha of Douglas-fir plantations in western Oregon since 1996. Because the physiological effects of the disease (impaired CO₂ uptake and photosynthesis) are quantitatively related to the abundance of the pathogen (proportion of stomata occluded by ascocarps), pathogen abundance is directly related to disease and is a suitable response variable for assessing effects of climatic factors on disease. Climate factors most highly correlated with pathogen abundance are winter temperature and spring leaf wetness, and a model for prediction of disease severity based on these factors accounts for 77 percent and 78 percent of the variation in one- and two-year-old needles, respectively, for western Oregon sites. A trend of temperatures increasing by 0.2 to 0.4 °C during the winter months and spring precipitation increasing by 1.6 to 2.6 cm per decade since 1966 suggests that regional climate trends are influencing the current distribution and severity of Swiss needle cast disease. Forecasts of climate change in the Pacific Northwest region predict continued increases in temperatures during winter months of about 0.4 °C per decade through 2050, suggesting that the severity and distribution of Swiss needle cast is likely to increase in the coming decades as a result of climate change, with significant consequences for Pacific Northwest forests. A climate-based disease prediction model is being developed as an online, interactive tool that can be used to guide further research, conduct extended model validations, perform climate change scenario analyses, and eventually to provide short- and long-term disease risk predictions and management cost/benefit analyses. The model will be useful for prediction of disease development trends under different climate change scenarios and temporal scales.