Mapping the risk of establishment and spread of sudden oak death in California

Abstract

Sudden oak death, caused by the recently described pathogen Phytophthora ramorum, is an emerging forest disease that has reached epidemic levels in coastal forests of central California. We present a rule-based model of P. ramorum establishment and spread risk in California plant communities. The model, which is being used as a management tool to target threatened forests for early-detection monitoring and protection, incorporates the effects of spatial and temporal variability of multiple variables on pathogen persistence. Model predictions are based on current knowledge of host susceptibility, pathogen reproduction, and pathogen transmission with particular regard to host species distribution and climate suitability. Maps of host species distributions and monthly weather conditions were spatially analyzed in a GIS and parameterized to encode the magnitude and direction of each variable's effect on disease establishment and spread. Spread risk predictions were computed for each month of the pathogen's general reproductive season and averaged to generate a cumulative risk map (Fig. 6a and b). The model identifies an alarming number of uninfected forest ecosystems in California at considerable risk of infection by Phytophthora ramorum. This includes, in particular, a broad band of high risk north of Sonoma County to the Oregon border, a narrow band of high risk south of central Monterey County south to central San Luis Obispo County, and scattered areas of moderate and high risk in the Sierra Nevada foothills in Butte and Yuba counties. Model performance was evaluated by comparing spread risk predictions to field observations of disease presence and absence. Model predictions of spread risk were consistent with disease severity observed in the field, with modeled risk significantly higher at currently infested locations than at uninfested locations ( P < 0.01, n = 323). Based on what is known about the ecology and epidemiology of sudden oak death, this model provides a simple and effective management tool for identifying emergent infections before they become established.