Abstract

Background: Understanding the geographic distribution of Rickettsia montanensis infections in Dermacentor variabilis is important for tick-borne disease management in the United States, as both a tick-borne agent of interest and a potential confounder in surveillance of other rickettsial diseases. Two previous studies modeled niche suitability for D. variabilis with and without R. montanensis, from 2002 to 2012, indicating that the D. variabilis niche overestimates the infected niche. This study updates these, adding data since 2012. Methods: Newer surveillance and testing data were used to update Species Distribution Models (SDMs) of D. variabilis, and R. montanensis-infected D. variabilis, in the United States. Using random forest models, found to perform best in previous work, we updated the SDMs and compared them with prior results. Warren's I niche overlap metric was used to compare between predicted suitability for all ticks and "R. montanensis-positive niche" models across datasets. Results: Warren's I indicated <2% change in predicted niche, and there was no change in order of importance of environmental predictors, for D. variabilis or R. montanensis-positive niche. The updated D. variabilis niche model overpredicted suitability compared with the updated R. montanensis-positive niche in key peripheral parts of the range, but slightly underpredicted through the northern and midwestern parts of the range. This reinforces previous findings of a more constrained R. montanensis-positive niche than predicted by D. variabilis records alone. Conclusions: The consistency of predicted niche suitability for D. variabilis in the United States, with the addition of nearly a decade of new data, corroborates this is a species with generalist habitat requirements. Yet a slight shift in updated niche distribution, even of low suitability, included more southern areas, pointing to a need for continued and extended monitoring and surveillance. This further underscores the importance of revisiting vector and vector-borne disease distribution maps.

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