Grading Habitats for Ticks by Mapping a Suitability Index based on Remotely Sensed Data and Meta® population dataset in Aosta Valley, NW Italy.

Abstract

Ticks represent a reservoir of zoonotic pathogens, and their numbers are increasing largely in wildlife. This work is aimed at producing maps of suitable habitats for ticks in Aosta Valley, Italy based on multitemporal EO data and veterinary datasets (tick species and distribution in wild hosts). EO data were processed in Google Earth Engine considering the following inputs: A) Growing Degree Ticks (GDT), B) NDVI from MOD09GA, C) NDVI entropy, D) distance from water bodies, E) topography, F) rainfalls from CHIRPS as monthly composites along the 2020, 2021 and 2022 years. Ticks were collected from hunted, injured, and found-dead wild animals ( Sus scrofa, Capreolus capreolus, Rupicapra rupicapra, Cervus elaphus); they were labeled at species level using taxonomic keys. Between September 2020 and December 2022, a total of 90 ticks were collected from 89 wild animals. Ixodes ricinus was the most prevalent tick species, followed by Dermacentor marginatus and Dermacentor spp. Molecular analyses demonstrated the presence of Anaplasma spp., B. burgdorferi sensu lato and Rickettsia spp. pathogens in infected ticks. To assess human population potential exposure to tick Meta® population dataset was used. In conclusion this study shows the potentialities of Remote sensing improving the technological transfer to the veterinarian sector.