Analisi ecologica dei flebotomi vettori della leishmaniosi canina in Italia nord-orientale attraverso l'utilizzo di Sistemi Informativi Geografici (GIS) e Modelli di Distribuzione di Specie
Since the 1990s, new foci of Canine Leishmaniosis have being detected in northern Italy, previously regarded as non-endemic. The disease has increased gradually in the regions of the Alps and entomological surveys demonstrated the established presence of two vectors of the disease, P. perniciosus and P. neglectus, in several localities of the area. The changing pattern of the disease is mainly due to movement of infected dogs from endemic areas, to the increase in human and animal travels and goods trade and to climatic changes, critical to the establishment of the vectors.
The study describes the results of the 12-years entomological survey in Veneto, Friuli Venezia Giulia and Trentino Alto Adige Regions, analysed through the use of GIS tools and Species Distribution Models (SDMs), based on remote sensing environmental data. Several climatic and environmental features were explored in regard to the entomological data, in order to better understand the ecology of the vectors and the epidemiology of the disease, to create a tool in support of surveillance activities.
Sandfly trapping was conducted from 2001 to 2012 in 175 sites, using sticky traps (n=114 sites), CDC light traps (n=53) and CO2 traps (n=66). GPS coordinates were acquired for all sites and potential risk factors (altitude, number and species of domestic bait animals, structural characteristics of site and level of urbanization) were identified and registered. The presence/absence of sandflies were compared with the risk factors considered and environmental variables, such as MODIS data (Normalized Difference Vegetation Index and Land Surface Temperature), a land cover map (Corine Land Cover 2006), a Digital Elevation Model (GTOPO30) and a bioclimatic variable taken from the database WorldClim (BIO 18, precipitation of Warmest Quarter). The environmental features resulted more relevant, were used to built a predictive model of presence of P. perniciosus and sandflies in Veneto, Friuli Venezia Giulia and Trentino Alto Adige Regions, using the software MaxEnt (Maximum Entropy Modeling System).
Overall, 6.144 sandflies were collected and identified and P. perniciosus was the most abundant species (3.797, 61,8%), with density values comparable to endemic areas of southern and central Italy. The ecological analyses of risk factors identified the altitudinal range between 100 and 300 m as the optimal environment for sandflies. The analyses of eco-climatic variables showed that the species P. perniciosus and the sandflies prefer hilly areas, characterized by temperate climate, high vegetation cover and moderate rainfall.
The SDMs developed showed a high predictive power and demonstrated to be realistic, since areas highly suitable for sandflies overlap with Canine Leishmaniosis foci in the study area.
Visualisation of patterns of distribution of vector species in ecological space, using SDMs, was a useful tool for the understanding of the ecological requirements of the sandfly vectors. The used approach may be considered a new resource for the proper identification of the surveillance actions for the control of Canine Leishmaniosis
