Predicting density of Ixodes pacificus nymphs in dense woodlands in Mendocino County, California, based on geographic information systems and remote sensing versus field-derived data

Ixodes pacificus nymphs are the primary vectors to humans of Borrelia burgdorferi, the etiologic agent of Lyme disease, in California. We used a supervised classification model, based on remote sensing (RS) data from multi-seasonal Landsat TM 5 images, to identify the key habitat in Mendocino County where humans are exposed to I. pacificus nymphs (woodlands carpeted with leaf litter). The model, based on the normalized difference vegetation index (NDVI), brightness, and wetness, separated the nymphal risk habitat (52.6% of the county) from other habitat types with > 93% user and producer accuracies. Next, we determined the density of questing nymphs in 62 woodland-leaf areas located throughout Mendocino County and created forward-stepwise regression models explaining the variation in nymphal density based on traits attainable by a lay-person in the field (e.g., tree species present, deer signs; r(2) = 0.43, P < 0.0001), or geographic information systems (GIS)/RS-based environmental data (r(2) = 0.50, P < 0.0001). The GIS/RS model, using July NDVI, November greenness, a coastal influence category, May solar insolation, November hours of sunlight, and dominant hydrologic grouping as input variables, was 22% more accurate in predicting nymphal density at 16 validation sites (r(2) = 0.72) than the field-derived data model (r(2) = 0.50). The habitat classification and GIS/RS models were combined to create a continuous nymphal density surface for the entirety of Mendocino County. This risk surface showed that 11.9% of the county was classified as habitat posing at least moderate risk of human exposure to nymphs (> 6.4 nymphs per 100 m(2)). Furthermore, high-risk areas (> 10.5 nymphs per 100 m(2); 1.7% of the county) tended to cluster in the central interior and most heavily populated region of Mendocino County, but were rare in the proximity of coastal population centers.