Schmallenberg virus (SBV) is a Culicoides ‐borne Orthobunyavirus causing congenital malformations and reproductive losses in ruminants, with substantial economic and livestock health impacts across Europe. While outbreaks have been linked to specific climatic and environmental conditions, the drivers of SBV transmission in endemic regions remain poorly defined. It is unclear to what extent spatial variation in SBV seroprevalence reflects environmental risk factors in temperate regions with intensively managed livestock systems such as those in Germany. Spatially explicit generalised additive models (GAMs) and predictive risk mapping are, hence, applied to investigate whether landscape, climate or host availability influence SBV exposure risk in sheep flocks across five German federal states. Serological data were obtained from 70 sheep flocks ( n = 2723 animals; autumn 2017 to spring 2018) and 69 environmental variables were used in the spatial risk analysis. Environmental heterogeneity showed limited explanatory power for SBV seroprevalence. The final GAM explained 50.6% of deviance and identified cattle density as the strongest positive predictor (odds ratio OR = 1.01, p < 0.001), while nature reserve coverage (OR = 0.13, p = 0.015) and summer temperature during the wettest quarter (OR = 0.95, p = 0.021) were negatively associated. No spatial clustering was detected, and the predicted risk surface revealed only modest regional variation. These findings suggest that farm‐level factors and cattle‐associated vector habitats are more relevant to SBV transmission than broader climatic or land use gradients in ecologically uniform settings. The diffuse spatial pattern underscores a general vulnerability of German ruminants to Culicoides ‐borne viruses and supports the need for targeted surveillance and farm‐focused vector control. This modelling framework may assist in future risk assessments for emerging arboviruses under changing climate and agricultural conditions.
Kiene et al. (2026) studied this question.