Use of remote sensing and geographical information systems to identify environmental features that influence the distribution of paramphistomosis in sheep from the southern Italian Apennines

Abstract

A geographic information system (GIS) was constructed using remote sensing (RS) and landscape feature data together with Calicophoron daubneyi positive survey records from 197 georeferenced ovine farms with animals pasturing in a 3971 km 2 area of the southern Italian Apennines. The objective was to study the spatial distribution of this rumen fluke, identify environmental features that influence its distribution, and develop a preliminary risk assessment model. The GIS for the study area was constructed utilizing the following environmental variables: normalized difference vegetation index (NDVI), land cover, elevation, slope, aspect, and total length of rivers. These variables were then calculated for “buffer zones” consisting of the areas included in a circle of 3 km diameter centered on 197 farms. The environmental data obtained from GIS and RS and from data taken by the veterinarians on the field (stocking rate and presence of streams, springs and brooks on pasture) were analyzed by univariate (Spearman and ANOVA) and multivariate (discriminant) statistical analyses using the farm coprological status (positive/negative) as the dependent variable. Sheep on 32 of the 197 (16.2%) farms, were positive for C. daubneyi, with an average intensity of 52 epg. A multivariate stepwise discriminant analysis model was developed that included moors and heathland, sclerophyllous and coniferous forest vegetation, autumn–winter NDVI and presence of streams, springs and brooks on pasture. The variables entered in the model were also correlated with C. daubneyi positive farms in the univariate tests and are consistent with the environmental requirements of C. daubneyi and its snail intermediate host.