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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.

Information on the population dynamics of a reservoir species have been increasingly adopted to understand and eventually predict the dispersal patterns of infectious diseases throughout an area. Although potentially relevant, to date there are no studies which have investigated the genetic structure of the red fox population in relation to infectious disease dynamics. Therefore, we genetically and spatially characterised the red fox population in the area stretching between the Eastern and Dinaric Alps, which has been affected by both distemper and rabies at different time intervals. Red foxes collected from north-eastern Italy, Austria, Slovenia and Croatia between 2006-2012, were studied using a set of 21 microsatellite markers. We confirmed a weak genetic differentiation within the fox population using Bayesian clustering analyses, and we were able to differentiate the fox population into geographically segregated groups. Our finding might be due to the presence of geographical barriers that have likely influenced the distribution of the fox population, limiting in turn gene flow and spread of infectious diseases. Focusing on the Italian red fox population, we observed interesting variations in the prevalence of both diseases among distinct fox clusters, with the previously identified Italy 1 and Italy 2 rabies as well as distemper viruses preferentially affecting different sub-groups identified in the study. Knowledge of the regional-scale population structure can improve understanding of the epidemiology and spread of diseases. Our study paves the way for an integrated approach for disease control coupling pathogen, host and environmental data to inform targeted control programs in the future.

The rock partridge, Alectoris graeca, is a polytypic species declining in Italy mostly due to anthropogenic causes, including the massive releases of the closely related allochthonous chukar partridge Alectoris chukar which produced the formation of hybrids. Molecular approaches are fundamental for the identification of evolutionary units in the perspective of conservation and management, and to correctly select individuals to be used in restocking campaigns. We analyzed a Cytochrome oxidase I (COI) fragment of contemporary and historical A. graeca and A. chukar samples, using duplicated analyses to confirm results and nuclear DNA microsatellites to exclude possible sample cross-contamination. In two contemporary specimens of A. graeca, collected from an anthropogenic hybrid zone, we found evidence of the presence of mtDNA heteroplasmy possibly associated to paternal leakage and suggesting hybridization with captive-bred exotic A. chukar. These results underline significant limitations in the reliability of mtDNA barcoding-based species identification and could have relevant evolutionary and ecological implications that should be accounted for when interpreting data aimed to support conservation actions.