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Landscape-Scale Prediction of Hemlock Woolly Adelgid, Adelges tsugae (Homoptera: Adelgidae), Infestation in the Southern Appalachian Mountains

Abstract After causing substantial mortality in the northeastern and mid-Atlantic United States, the hemlock woolly adelgid, Adelges tsugae Annand (Homoptera: Adelgidae), has recently invaded the southern Appalachian region. Although general estimates of regional spread exist, the landscape-level dynamics of A. tsugae invasion are poorly understood—particularly factors predicting where the pest is likely to first infest a landscape. We examined first-year infestation locations from Great Smoky Mountains National Park and the Blue Ridge Parkway to identify possible factors. For 84 infested and 67 uninfested sites, we calculated values for a suite of variables using a geographic information system. After identifying significant variables, we applied four statistical techniques—discriminant analysis, k-nearest neighbor analysis, logistic regression, and decision trees—to derive classification functions separating the infested and uninfested groups. We used the resulting functions to generate maps of A. tsugae infestation risk in the Great Smoky Mountains. Three proximity variables (distance to the closest stream, trail, and road) appeared in all four classification functions, which performed well in terms of error rate. Discriminant analysis was the most accurate and efficient technique, but logistic regression best balanced accuracy, efficiency, and ease of use. Our results suggest that roads, major trails, and riparian corridors provide connectivity enabling long-distance dispersal of A. tsugae, probably by humans or birds. The derived classification functions can yield A. tsugae infestation risk maps for elsewhere in the southern Appalachian region, allowing forest managers to better target control efforts.