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    Author(s): Václavík Tomáš; Alan Kanaskie; Ellen Goheen; Janet Ohmann; Everett Hansen; Ross Meentemeyer
    Date: 2010
    Source: In: Frankel, Susan J.; Kliejunas, John T.; Palmieri, Katharine M. 2010. Proceedings of the Sudden Oak Death Fourth Science Symposium. Gen. Tech. Rep. PSW-GTR-229. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station. pp. 126-132
    Publication Series: General Technical Report (GTR)
    PDF: Download Publication  (0 B)

    Description

    Phytophthora ramorum was first discovered in forests of southwestern Oregon in 2001. Despite intense eradication efforts, disease continues to spread from initially infested sites because of the late discovery of disease outbreaks and incomplete detection. Here we present two GIS predictive models of sudden oak death (SOD) establishment and spread risk that can be used to target monitoring and eradication activities in western Oregon. Model predictions were based on three primary parameters: weather and climate variability, host vegetation susceptibility and distribution, and dispersal (force of infection). First, a heuristic model using multi-criteria evaluation (MCE) method was developed to identify the areas at potential risk. We mapped and ranked host susceptibility using new geospatial vegetation data available from the U.S. Department of Agriculture, Forest Service (USDA FS)/Oregon State University (OSU) Landscape, Ecology, Modeling, Mapping, and Analysis project (LEMMA). Precipitation and temperature conditions derived from PRISM climate database were parameterized in accordance to their epidemiological importance in the SOD disease system. The final appraisal scores were calculated and summarized to represent a cumulative spread risk index, standardized into five risk categories from very low risk to very high risk. Second, we used the machine-learning method, maximum entropy (MAXENT) to predict the current distribution of SOD infections. Here, probability of infection was calibrated based on the correlation between 500 field observations of disease occurrence and several predictor variables including climate variability, host susceptibility and abundance, topographic variables, and a dispersal constraint. The dispersal constraint estimates the force of infection at all locations and thus predicts the actual or current distribution of the pathogen rather than its potential distribution. Numerous forests across the western region of Oregon appear to be susceptible to SOD. Areas at greatest risk of disease spread are concentrated in the southwest region of Oregon where the highest densities of susceptible host species exist, in particular tanoak (Lithocarpus densiflorus). These models provide a better picture of threatened forest resources across the state and are being actively used to prioritize early detection and eradication efforts.

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    Citation

    Vaclavik, Tomas; Kanaskie, Alan; Goheen, Ellen; Ohmann, Janet; Hansen, Everett; Meentemeyer, Ross. 2010. Mapping the Risk of Sudden Oak Death in Oregon: Prioritizing Locations for Early Detection and Eradication. In: Frankel, Susan J.; Kliejunas, John T.; Palmieri, Katharine M. 2010. Proceedings of the Sudden Oak Death Fourth Science Symposium. Gen. Tech. Rep. PSW-GTR-229. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station. pp. 126-132

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