Tick maps on the virtual globe: First results using the example of Dermacentor reticulatus

Area of Science:

• Zoology
• Ecology
• Geographic Information Systems (GIS)

Background:

• Digital maps on virtual globes are increasingly vital for geographical knowledge.
• Google Earth is a widely used platform for educational and scientific visualization.
• Understanding tick distribution is crucial for public health due to their role as disease vectors.

Purpose of the Study:

• To develop and present a novel Google Earth application showcasing the worldwide distribution of the marsh tick, *Dermacentor reticulatus*.
• To integrate diverse data sources, including expert maps and species distribution models, for a comprehensive view of tick distribution.
• To provide an interactive tool for scientists, students, and the public to explore tick ecology and geographical spread.

Main Methods:

• Compilation of digitized expert maps and creation of a species distribution model (SDM) for *Dermacentor reticulatus* habitat suitability.
• Utilized a random forest model incorporating tick observations, bioclimatic variables, altitude, and land cover data.
• Developed a Google Earth application using Keyhole Markup Language (KML) to display various distribution maps and supplementary information.

Main Results:

• The Google Earth application features multiple map layers: historical expert, current expert, SDM habitat suitability, combined distribution, and rasterized tick locations.
• A comparison of historical and current expert maps reveals the expansion of *Dermacentor reticulatus* populations.
• Urban distribution maps for several European and Russian cities highlight tick presence in accessible public and recreational areas.

Conclusions:

• The developed Google Earth application offers a dynamic and accessible platform for visualizing *Dermacentor reticulatus* distribution.
• This tool facilitates the study of tick spread, ecological dynamics, and potential public health implications.
• The integration of expert knowledge and predictive modeling provides a robust representation of tick distribution for diverse user groups.