Tick-Borne Pathogens

Ecology of vectors and hosts

Ticks transmit a variety of disease-causing pathogens that are maintained in complex ecological cycles involving wildlife hosts and environmental conditions. The geographic distributions of ticks and their pathogens are influenced by climate, vegetation, and landscape characteristics, but these relationships vary across species and environmental settings. Our research has demonstrated that the environmental drivers of tick-borne pathogen distributions vary geographically, with climate playing a dominant role in the eastern United States and forest cover and fragmentation becoming more important near western range boundaries (Yabsley et al. 2005, Wimberly et al. 2008b). We also documented the effects of forest cover and flooding on pathogen distributions in the Mississippi Alluvial Valley (Manangan et al. 2007). Using Bayesian hierarchical models, we developed spatial predictions of tick-borne pathogen distributions that accounted for geographic variation in environmental relationships and spatial autocorrelation (Wimberly et al. 2008a). These studies demonstrated the importance of incorporating spatially heterogeneous environmental relationships into models of tick-borne disease risk.

Ehrlichia chaffeensis range map
Map of the regional distribution of Ehrlichia chaffeensis based on a hierarchical Bayesian modeling incorporating spatially heterogeneous environmental predictors and spatial autocorrelation (Wimberly et al. 2008b).

More recently, we collaborated with Anni Yang (University of Oklahoma) to investigate how urban landscapes and microenvironments influence tick abundance. In a study of 13 parks in the Oklahoma City metropolitan area, we found that the abundance of lone star ticks (Amblyomma americanum) was influenced by both local habitat characteristics and broader landscape conditions (Marquez et al. 2025). Important predictors included woody vegetation, solar radiation, humidity, and soil characteristics at the local level and forest cover at the landscale scale. These findings highlight the highlight the potential for using environmental variables to predict potential areas of human exposure in urban green spaces.

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