Toxoplasma gondii in the Eurasian kestrel (Falco tinnunculus) in northern Italy

Background Identifying factors that sustain parasite transmission is important for understanding their spread and emergence, including how changes in biodiversity may affect parasite prevalence and spread. Toxoplasma gondii is a protozoan parasite infecting humans and animals. Birds can acquire T. gondii infection through ingestion either of oocysts from the ground or of tissue cysts present in infected prey and are therefore suitable indicators of the presence of T. gondii in the natural environment. Methods The aim of the study included the evaluation of T. gondii seroprevalence in clinically healthy Eurasian kestrels (Falco tinnunculus) using a modified agglutination test. Birds were captured in a small area of Parma (northern Italy) for two consecutive years (2016–2017), sex and age determined and serological study carried out. Food sources for the birds were also evaluated, in particular rodent and grasshopper population estimates in the study area. The biomass of rodents and grasshoppers per hectare was estimated in order to directly compare food availability. Statistical analyses were performed in order to evaluate factors influencing the probability of kestrels being T. gondii-seropositive using R 3.4.4 fitting linear mixed-effect models with the ‘glmer’ function of the package lme4, ‘lsmean’ in package lsmean for pair-wise post-hoc comparisons using differences of least square means (DLSM) and the ‘betareg’ function of the package betareg for beta regression. Results Seroprevalence for T. gondii was 33.3% (49/147) in 2016, while in 2017 seroprevalence decreased to 14.3% (13/91). An increase in the probability of kestrels being T. gondii-seropositive was associated with a higher rodent biomass in the environment, suggesting a positive feedback of the biotic factors driving infection risk. Conclusions These results underline the need for multidisciplinary studies aimed at better understanding pathogen-host relationships and for predictions in disease ecology.