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Approaches to predicting potential impacts of climate change on forest disease: an example with Armillaria root diseaseAuthor(s): Ned B. Klopfenstein; Mee-Sook Kim; John W. Hanna; Bryce A. Richardson; John E. Lundquist
Source: Res. Pap. RMRS-RP-76. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 10 p.
Publication Series: Research Paper (RP)
Station: Rocky Mountain Research Station
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DescriptionPredicting climate change influences on forest diseases will foster forest management practices that minimize adverse impacts of diseases. Precise locations of accurately identified pathogens and hosts must be documented and spatially referenced to determine which climatic factors influence species distribution. With this information, bioclimatic models can predict the occurrence and distribution of suitable climate space for host and pathogen species under projected climate scenarios. Predictive capacity is extremely limited for forest pathogens because distribution data are usually lacking. Using Armillaria root disease as an example, predictive approaches using available data are presented.
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CitationKlopfenstein, Ned B.; Kim, Mee-Sook; Hanna, John W.; Richardson, Bryce A.; Lundquist, John E. 2009. Approaches to predicting potential impacts of climate change on forest disease: an example with Armillaria root disease. Res. Pap. RMRS-RP-76. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 10 p.
Keywordsclimate change, forest diseases, Armillaria, bioclimatic models, forest pathogens, global warming, MaxEnt
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