Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/4/2005
Publication Date: 7/18/2006
Citation: Stone, J.K., Coop, L.B., Manter, D.K. 2006. A spatial model for predicting effects of climate change on Swiss needle cast disease severity in the Pacific Northwest. Meeting Abstract. http://www.forestencyclopedia.net Interpretive Summary:
Technical Abstract: Swiss needle cast disease of Douglas-fir 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 20-50% 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 having the greatest influence on pathogen abundance are winter temperatures and spring leaf wetness, and a model for prediction of disease severity based on these factors has been developed for western Oregon. A trend of increasing temperatures during the winter months of 0.2-0.4 °C and increasing spring precipitation of 0.7-1.5 cm per decade since 1970 suggests that regional climate trends are influencing the 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 will 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 for use as an online, interactive tool that can be used to guide further research, conduct extended validations, perform climate change scenario analyses, and eventually to provide short and long term disease risk predictions and control cost/benefit analyses. The model will be useful for visualization of disease development trends under different climate change scenarios and temporal scales.