Ecological Applications, 18(2), 2008, pp. 377–390 Ó 2008 by the Ecological Society of America EARLY DETECTION OF EMERGING FOREST DISEASE USING DISPERSAL ESTIMATION AND ECOLOGICAL NICHE MODELING ROSS K. MEENTEMEYER, 1,5 BRIAN L. ANACKER, 1,2 WALTER MARK, 3 AND DAVID M. RIZZO 4 1 Department of Geography and Earth Sciences, University of North Carolina, 9201 University City Boulevard, Charlotte, North Carolina 28223 USA 2 Department of Environmental Science and Policy, University of California, 1 Shields Avenue, Davis, California 95616 USA 3 Department of Natural Resources Management, California Polytechnic State University, San Luis Obispo, California 93407 USA 4 Department of Plant Pathology, University of California, 1 Shields Avenue, Davis, California 95616 USA Abstract. Distinguishing the manner in which dispersal limitation and niche requirements control the spread of invasive pathogens is important for prediction and early detection of disease outbreaks. Here, we use niche modeling augmented by dispersal estimation to examine the degree to which local habitat conditions vs. force of infection predict invasion of Phytophthora ramorum, the causal agent of the emerging infectious tree disease sudden oak death. We sampled 890 field plots for the presence of P. ramorum over a three-year period (2003–2005) across a range of host and abiotic conditions with variable proximities to known infections in California, USA. We developed and validated generalized linear models of invasion probability to analyze the relative predictive power of 12 niche variables and a negative exponential dispersal kernel estimated by likelihood profiling. Models were developed incrementally each year (2003, 2003–2004, 2003–2005) to examine annual variability in model parameters and to create realistic scenarios for using models to predict future infections and to guide early-detection sampling. Overall, 78 new infections were observed up to 33.5 km from the nearest known site of infection, with slightly increasing rates of prevalence across time windows (2003, 6.5%; 2003–2004, 7.1%; 2003–2005, 9.6%). The pathogen was not detected in many field plots that contained susceptible host vegetation. The generalized linear modeling indicated that the probability of invasion is limited by both dispersal and niche constraints. Probability of invasion was positively related to precipitation and temperature in the wet season and the presence of the inoculum-producing foliar host Umbellularia californica and decreased exponentially with distance to inoculum sources. Models that incorporated niche and dispersal parameters best predicted the locations of new infections, with accuracies ranging from 0.86 to 0.90, suggesting that the modeling approach can be used to forecast locations of disease spread. Application of the combined niche plus dispersal models in a geographic information system predicted the presence of P. ramorum across ;8228 km 2 of California’s 84 785 km 2 (9.7%) of land area with susceptible host species. This research illustrates how probabilistic modeling can be used to analyze the relative roles of niche and dispersal limitation in controlling the distribution of invasive pathogens. Key words: dispersal kernel; early detection; ecological niche modeling; emerging infectious disease; invasive species; landscape epidemiology; Phytophthora ramorum; sudden oak death. INTRODUCTION Globalization and extensive land use changes have facilitated the transfer, establishment, and spread of invasive pathogens of plants and animals, resulting in worldwide increases of emerging infectious diseases (EIDs) (Patz et al. 2004, Foley et al. 2005). Direct detrimental impacts of EIDs include major biodiversity loss, significant alterations of natural community struc- ture, and reduced agricultural production (Vitousek et al. 1997, Hansen 1999, Pimentel et al. 2000, Baskin 2002, Pimentel 2002). Despite prevention efforts, path- ogen introductions frequently occur and subsequent invasions can spread rapidly across extensive areas before damaging impacts are recognized (Mack et al. 2000). Rapid discovery of outbreaks substantially increases efficacy of control treatments, which are typically only effective when populations are small and isolated. Thus, early detection of disease outbreaks is critical for increasing the likelihood of eradication or control (Moody and Mack 1988, Zamora and Thill 1999, Simberloff 2003). As the number of EIDs continues to grow, effective methods for predicting locations most at risk of invasion are increasingly needed. Like other types of biological invasions, early detection of disease outbreaks in natural environments can be challenging, especially when founder populations are rare or spatially heterogeneous (Moody and Mack 1988, D’Antonio et al. 2004). One approach is to model the ‘‘realized ecological niche’’ of an invader based on Manuscript received 13 July 2007; revised 17 September 2007; accepted 24 September 2007. Corresponding Editor: S. K. Collinge. 5 E-mail: rkmeente@uncc.edu 377