Modeling the Present and Future Geographic Distribution of the Lone Star Tick, Amblyomma americanum (Ixodida: Ixodidae), in the Continental United States

Abstract

Abstract. The Lone star tick (Amblyomma americanum L.) is the primary vector for pathogens of significant public health importance in North America, yet relatively little is known about its current and potential future distribution. Building on a published summary of tick collection records, we used an ensemble modeling approach to predict the present-day and future distribution of climatically suitable habitat for establishment of the Lone star tick within the continental United States. Of the nine climatic predictor variables included in our five present-day models, average vapor pressure in July was by far the most important determinant of suitable habitat. The present-day ensemble model predicted an essentially contiguous distribution of suitable habitat extending to the Atlantic coast east of the 100th western meridian and south of the 40th northern parallel, but excluding a high elevation region associated with the Appalachian Mountains. Future ensemble predictions for 2061-2080 forecasted a stable western range limit, northward expansion of suitable habitat into the Upper Midwest and western Pennsylvania, and range contraction along portions of the Gulf coast and the lower Mississippi river valley. These findings are informative for raising awareness of A. americanumtransmitted pathogens in areas where the Lone Star tick has recently or may become established.

Modeling the Geographic Distribution ofIxodes scapularisandIxodes pacificus(Acari: Ixodidae) in the Contiguous United States

“…with reasonable accuracy based on current vector surveillance data, the expansion of tick populations into new areas depends on either dispersal of a gravid female, male and female adults, or several immature ticks that survive until reproduction (Springer et al 2015). Ticks are generally dispersed by the movement of hosts (Madhav et al 2004, Khatchikian and Prusinski 2012) and therefore, Ixodes spp.…”

“…Derivation methods and data sources for the candidate variables have been described previously in Springer et al (2015) and are briefly summarized below and in the Supp. File (online only).…”

“…We included the mean number of GDDs > 10°C for each month as well as cumulative GDDs > 10°C from the start of the year (Eisen et al 2006, Moore et al 2014). Because GDDs are not a direct output of WorldClim, they were estimated using WorldClim temperature data and calibrated using daily data from Daymet (Thorton et al 2012), as described in Springer et al (2015). We also tested monthly average values of vapor pressure (a measure of humidity) and the average number of days per month with snow cover (based on values of snow water equivalent >0mm) using data for the period 1980-2000 from Daymet.…”

“…We assessed over-fitting by looking to see if the difference between the training AUC and mean of the testing AUC values exceeded 0.05 or if there were large differences between the training and testing sets in percent correctly classified, percent deviance explained, and sensitivity and specificity of training and testing models for each algorithm. Based on these assessments, we optimized the BRT model using a tree complexity of two, a learning rate of 0.005, and 5,000 trees (Elith et al 2008, Springer et al 2015). All other models used the default SAHM parameters.…”

“…distribution data (Eisen et al 2016) and statistical ensemble modeling (Araujo and New 2007) to map the potential distribution of the tick vectors of Lyme disease spirochetes and other human pathogens in the United States. An ensemble modeling approach is used to address uncertainty in individual modeling algorithms (Buisson et al 2010, Springer et al 2015). …”