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Newer Surveillance Data Extends our Understanding of the Niche of Rickettsia montanensis (Rickettsiales: Rickettsiaceae) Infection of the American Dog Tick (Acari: Ixodidae) in the United States.

Catherine A LippiHolly D GaffRobyn M NadolnySadie Jane Ryan
Published in: Vector borne and zoonotic diseases (Larchmont, N.Y.) (2023)
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.
Keyphrases
  • climate change
  • electronic health record
  • big data
  • machine learning
  • deep learning
  • artificial intelligence