Infectious diseases are influenced by interactions between host and pathogens in variable environments and are rarely homogenous across the landscape. Areas with elevated pathogen burden and transmission may indicate a disruption to steady-state disease dynamics. However, isolating processes that result in increases in infection prevalence and intensity remains a challenge. Here we elucidate the contribution of host species, and pathogen clade in disease hotspots. We examined broad-scale patterns of infection of O. ophidiicola, the pathogen that causes snake fungal disease, in 21 species of snakes across 10 countries in Europe. Disease hotspots were evident across several regions in Europe, and our analyses revealed significant differences in infection based on host species and pathogen clade. Over 80% of positive detections were from host species in the Natrix genus, indicating potential higher susceptibility in this group. The presence of O. ophidiicola genotypes that have been associated with more severe disease in North America, also resulted in high rates of infection compared to genotypes only described from Europe. Elevated infection prevalence was best explained by an interaction between host and pathogen identity which was not uniform across all species. More broadly, these findings present important mechanisms underlying disease hotspots across a disease endemic region.

Forest fires, due to climate change, are a growing threat to human life, health, and property, especially in temperate climates. Unfortunately, the impact of individual factors on forest fires varies, depending on the geographical region and its natural and socio-economic conditions. The latter are rarely introduced into fire warning systems, which significantly reduces their effectiveness. Therefore, the main goal of this study was to quantify the impact of a wide range of anthropogenic factors on forest fires, using Poland as a representative example of a Central European country. Data were analyzed in relation to districts for the period 2007–2017, using correlation analysis and regression modeling applying global and local/mixed regression methods. It was found that almost all of the 28 variables taken for analysis significantly determined the density of forest fires, but the greatest role was played by the length of the border between forests and built-up areas, and road density. In addition, the impact of most of the analyzed variables on forest fires varied over the study area, so implementing non-stationarity in geographically weighted regression models significantly improved the goodness-of-fit compared to global models.

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