Global change is expected to have complex effects on the distribution and transmission patterns of zoonotic parasites. Modelling habitat suitability for parasites with complex life cycles is essential to further our understanding of how disease systems respond to environmental changes, and to make spatial predictions of their future distributions. However, the limited availability of high quality occurrence data with high spatial resolution often constrains these investigations. Using 449 reliable occurrence records for Echinococcus multilocularis from across Europe published over the last 35 years, we modelled habitat suitability for this parasite, the aetiological agent of alveolar echinococcosis, in order to describe its environmental niche, predict its current and future distribution under three global change scenarios, and quantify the probability of occurrence for each European country. Using a machine learning approach, we developed large-scale (25 × 25 km) species distribution models based on seven sets of predictors, each set representing a distinct biological hypothesis supported by current knowledge of the autecology of the parasite. The best-supported hypothesis included climatic, orographic and land-use/land-cover variables such as the temperature of the coldest quarter, forest cover, urban cover and the precipitation seasonality. Future projections suggested the appearance of highly suitable areas for E. multilocularis towards northern latitudes and in the whole Alpine region under all scenarios, while decreases in habitat suitability were predicted for central Europe.Our spatially explicit predictions of habitat suitability shed light on the complex responses of parasites to ongoing global changes.

Surveillance of Echinococcus multilocularis at the edge of its range is hindered by fragmented distributional patterns and low prevalence in definitive hosts. Thus, tests with adequate levels of sensitivity are especially important for discriminating between infected and non-infected areas. In this study we reassessed the prevalence of E. multilocularis at the southern border of its distribution in Province of Bolzano (Alto Adige, northeastern Alps, Italy), to improve surveillance in wildlife and provide more accurate estimates of exposure risk. We compared the diagnostic test currently implemented for surveillance based on coproscopy and multiplex PCR (CMPCR) to a real-time quantitative PCR (qPCR) in 235 fox faeces collected in 2019 and 2020. The performances of the two tests were estimated using a scraping technique (SFCT) applied to the small intestines of a subsample (n = 123) of the same foxes as the reference standard. True prevalence was calculated and the sample size required by each faecal test for the detection of the parasite was then estimated. True prevalence of E. multilocularis in foxes (14.3%) was markedly higher than reported in the last decade, which was never more than 5% from 2012 to 2018 in the same area. In addition, qPCR showed a much higher sensitivity (83%) compared to CMPCR (21%) and agreement with the reference standard was far higher for qPCR (0.816) than CMPCR (0.298) meaning that for the latter protocol, a smaller sample size would be required to detect the disease. Alto Adige should be considered a highly endemic area. Routine surveillance on definitive hosts at the edges of the E. multilocularis distribution should be applied to smaller geographic areas, and rapid, sensitive diagnostic tools using directly host faeces, such as qPCR, should be adopted.

The assessment of red fox population density is considered relevant to the surveillance of zoonotic agents vectored by this species. However, density is difficult to estimate reliably, since the ecological plasticity and elusive behavior of this carnivore hinder classic methods of inference. In this study, red fox population density was estimated using a non-invasive molecular spatial capture-recapture (SCR) approach in two study areas: one in a known hotspot of the zoonotic cestode Echinococcus multilocularis, and another naïve to the parasite. Parasitological investigations on collected samples confirmed the presence of the parasite exclusively in the former area; the SCR results indicated a higher fox population density in the control area than in the hotspot, suggesting either that the relationship between fox density and parasite prevalence is not linear and/or the existence of other latent factors supporting the parasitic cycle in the known focus. In addition, fox spotlight count data for the two study areas were used to estimate the index of kilometric abundance (IKA). Although this method is cheaper and less time-consuming than SCR, IKA values were the highest in the areas with the lower molecular SCR density estimates, confirming that IKA should be regarded as a relative index only.