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An understanding of the spatial distribution of the black-legged tick, Ixodes scapularis, is a fundamental component in assessing human risk for
Lyme disease in much of the United States. Although a county-level vector distribution map exists for the United States, its accuracy is limited by arbitrary categories of its reported presence. It is unknown whether reported positive areas can support established populations and whether negative areas are suitable for established populations. The steadily increasing range of I. scapularis in the United States suggests that all suitable habitats are not currently occupied. Therefore, we developed a spatially predictive logistic model for I. scapularis in the 48 conterminous states to improve the previous vector distribution map. We used ground-observed environmental data to predict the probability of established I. scapularis populations. The autologistic analysis showed that maximum, minimum, and mean temperatures as well as vapor pressure significantly contribute to population maintenance with an accuracy of 95% (p < 0.0001). A cutoff probability for habitat suitability was assessed by sensitivity analysis and was used to reclassify the previous distribution map. The spatially modeled relationship between I. scapularis presence and large-scale environmental data provides a robust suitability model that reveals essential environmental determinants of habitat suitability, predicts emerging areas of
Lyme disease risk, and generates the future pattern of I. scapularis across the United States.