Abstract
Ticks represent a reservoir of zoonotic pathogens, and their numbers are increasing largely in wildlife. The development of Earth Observation (EO) missions and GIS-Remote sensing techniques have contributed to the enhancement of epidemiological data analysis capabilities providing new and potential tools for the surveillance of animal diseases (Estrada-Peña, 2002). Moreover, the correctly geomatic-based use and development of remote sensing and EO Data in veterinarian ordinary workflows is still under-exploited.
In this regard, to strengthen the use of free EO data in ordinary veterinarian analysis, this work aimed identifying the tick species most involved, their distribution in wild hosts and develop suitability habitat maps (SHM) in Aosta Valley (NW Italy) based on multitemporal EO data processed in Google Earth Engine providing also a risk assessment (Orusa et al., 2023). SHM were realized considering the following inputs: A) Growing Degree Ticks (GDT) computed from Land Surface Temperature from MOD11A1 by assigning a threshold, B) NDVI from MOD09GA involving the pixels satisfying the GDT threshold, C) NDVI entropy, D) water surfaces distance, E) terrain features, F) Precipitation retrieved from WORLDCLIM/V1/BIO.
Clouds, shadows and defective pixels have been masked out in the EO data considered and monthly composites within the years 2020-2022 created. Since the data of the ticks have not been punctually geo-referenced, not allowing a multivariate regression analysis of the variables involved in the suitability map, to define the weight of each one, on the basis of the existing literature, the variables considered have been assigned the same weight and have been normalized. An ISODATA unsupervised classification–clustering algorithm was performed on B), and the separability of each class obtained was checked by computing the Jeffries–Matusita distances to compute than C).
Ticks were collected from hunted, injured, and found dead wild animals (Sus scrofa, Capreolus capreolus, Rupicapra rupicapra, Cervus elaphus), and they were identified to species level using taxonomic keys (Accorsi et al., 2022). Between September 2020 and December 2022, a total of 90 ticks were collected from 89 wild animals. Ixodes ricinus (93.4%) was the most prelevant tick species, followed by Dermacentor marginatus (5.5%) and Dermacentor spp (1.1%). In roe deer, Ixodes ricinus was the most frequent species founded, as Dermacentor spp., while in wild boar Dermacentor marginatus was the dominant species. Molecular analyses demonstrated the infection of ticks with Anaplasma spp., B. burgdorferi sensu lato, Coxiella burnetii, Rickettsia spp.
Finally, to assess population potential exposure to tick presence during the period investigated, the Meta® population dataset was considered by performing zonal statistics per each suitability class.
In conclusion this study shows the potentialities of Remote sensing and how the use of different information layers can allow a technological transfer to the veterinary sector according to a real One Health perspective.
References
Estrada-Peña, A. (2002). Increasing habitat suitability in the United States for the tick that transmits Lyme disease: a remote sensing approach. Environmental health perspectives, 110(7), 635-640.
Orusa, T., Viani, A., Moyo, B., Cammareri, D., & Borgogno-Mondino, E. (2023). Risk Assessment of Rising Temperatures Using Landsat 4–9 LST Time Series and Meta® Population Dataset: An Application in Aosta Valley, NW Italy. Remote Sensing, 15(9), 2348.
Accorsi, A., Schiavetti, I., Listorti, V., Dellepiane, M., Masotti, C., Ercolini, C., ... & Razzuoli, E. (2022). Hard Ticks (Ixodidae) from Wildlife in Liguria, Northwest Italy: Tick Species Diversity and Tick-Host Associations. Insects, 13(2), 199.