Modeling the Geographic Distribution of Ixodes scapularis and Ixodes pacificus (Acari: Ixodidae) in the Contiguous United States

Abstract

In addition to serving as vectors of several other human pathogens, the black-legged tick, Ixodes scapularis Say, and western black-legged tick, Ixodes pacificus Cooley and Kohls, are the primary vectors of the spirochete (Borrelia burgdorferi) that causes Lyme disease, the most common vector-borne disease in the United States. Over the past two decades, the geographic range of I. pacificus has changed modestly while, in contrast, the I. scapularis range has expanded substantially, which likely contributes to the concurrent expansion in the distribution of human Lyme disease cases in the Northeastern, North-Central and Mid-Atlantic states. Identifying counties that contain suitable habitat for these ticks that have not yet reported established vector populations can aid in targeting limited vector surveillance resources to areas where tick invasion and potential human risk are likely to occur. We used county-level vector distribution information and ensemble modeling to map the potential distribution of I. scapularis and I. pacificus in the contiguous United States as a function of climate, elevation, and forest cover. Results show that I. pacificus is currently present within much of the range classified by our model as suitable for establishment. In contrast, environmental conditions are suitable for I. scapularis to continue expanding its range into northwestern Minnesota, central and northern Michigan, within the Ohio River Valley, and inland from the southeastern and Gulf coasts. Overall, our ensemble models show suitable habitat for I. scapularis in 441 eastern counties and for I. pacificus in 11 western counties where surveillance records have not yet supported classification of the counties as established.

Keywords: Ixodes pacificus; Ixodes scapularis; Lyme disease; bioclimatic modeling; habitat suitability.

Fig. 1

Map depicting counties on the leading edge of Ixodes spp. expansion and counties where the ticks are established but not predicted as suitable by the ensemble model. Counties in black are those that have established Ixodes spp. populations (Eisen et al. 2016). Counties in red are those that do not have an established Ixodes spp. population but are predicted to have suitable habitat by two or more ensemble model members. Counties in grey are those that have established Ixodes spp. populations but were predicted as unsuitable by three or four ensemble model members. Counties with black dots have reported (but not established) Ixodes spp. populations.

Fig. 2

Response curves for the climate, elevation, or land cover predictor variables selected by the optimized Ixodes scapularis models. The x-axis represents the range of each predictor in the training dataset, and the y-axis represents the associated probability of suitable habitat (0= not suitable, 1 =maximum suitability).

Fig. 3

Maps depicting Ixodes scapularis surveillance records and results of the habitat suitability modeling. (A) County-level classification of I. scapularis surveillance records based on (Eisen et al. 2016). (B) Map of ensemble model consensus habitat suitability scores. Scores indicate the number of the four optimized models that classified a given county as having climate, elevation, and land cover conditions suitable for the establishment of I. scapularis. (Right column) Maps depicting the predicted distribution of I. scapularis by each of the individual optimized models: BRT, GLM, MARS, and Maxent, respectively. BRT=boosted regression tree; GLM=generalized linear model; MARS=multivariate adaptive regression spline; Maxent=maximum entropy.

Fig. 4

Response curves for the climate, elevation, or land cover predictor variables selected by the optimized Ixodes pacificus models. The x-axis represents the range of each predictor in the training dataset, and the y-axis represents the associated probability of suitable habitat (0= not suitable, 1=maximum suitability).

Fig. 5

Maps depicting Ixodes pacificus surveillance records and results of the habitat suitability modeling. (A) County-level classification of I. pacificus surveillance records based on (Eisen et al. 2016). (B) Map of ensemble model consensus habitat suitability scores. Scores indicate the number of the four optimized models that classified a given county as having climate, elevation, and land cover conditions suitable for the establishment of I. pacificus. (Right column) Maps depicting the predicted distribution of I. pacificus by each of the individual optimized models: GLM, MARS, Maxent, and RF, respectively. GLM=generalized linear model; MARS=multivariate adaptive regression spline; Maxent=maximum entropy; RF=random forest.