Rodents are key reservoirs of zoonotic pathogens and play an important role in disease transmission to humans. Importantly, anthropogenic land-use change has been found to increase the abundance of synanthropic rodents, particularly rodent reservoirs of zoonotic disease. Anthropogenic environments also affect the microbiome of synanthropic wildlife, influencing wildlife health and potentially introducing novel pathogens. Our objective was to characterize the microbiome and investigate the prevalence of zoonotic bacterial pathogens in synanthropic rodents in native and anthropogenic environments to better understand their role in pathogen maintenance and transmission. We sampled wild Peromyscus mice in agricultural and undeveloped landscapes and forest and synanthropic habitat in Minnesota, USA and conducted 16S amplicon sequencing using long-read Nanopore sequencing technology on fecal samples to characterize the rodent microbiome. We compared community composition and diversity between habitats and screened for the presence of putative pathogenic bacteria species. Microbiome community composition differed significantly between agricultural and undeveloped landscapes and forest and synanthropic habitat while microbiome richness, diversity, and evenness were lower in undeveloped-forest habitat compared to all other habitats. We detected overall low abundance and diversity of putative pathogenic bacteria, though the greatest number of pathogenic bacteria were detected in the agricultural-forest habitat. Our findings show that rodent microbiome community composition differs across landscapes and habitat types but suggest that landscape-level anthropogenic factors may be most important to predict zoonotic pathogen abundance. Ultimately, understanding how anthropogenic land-use change and synanthropy affect rodent microbiomes and pathogen prevalence is important to managing transmission of rodent-borne zoonotic diseases to humans.

Hunting can fundamentally alter wildlife population dynamics, but the consequences of hunting on pathogen transmission and evolution remain poorly understood. Here we present a study that leverages a unique landscape-scale experiment coupled with pathogen transmission tracing, network simulation and phylodynamics to provide insights into how hunting shapes viral dynamics in puma (Puma concolor). We show that removing hunting pressure enhances the role of males in transmission, increases the viral population growth rate and the role of evolutionary forces on the pathogen (higher purifying and diversifying selection) compared with when hunting was reinstated. Changes in transmission could be linked to short term social changes as male population increases. These findings are supported through comparison with a region with stable hunting management over the same time period. This study shows that routine wildlife management can have profound impacts on pathogen transmission and evolution not previously considered.

Environment drives the host movements that shape pathogen transmission through three mediating processes: host density, host mobility, and contact. These processes combine with pathogen life-history to give rise to an “epidemiological landscape” that determines spatial patterns of pathogen transmission. Yet despite its central role in transmission, strategies for predicting the epidemiological landscape from real-world data remain limited. Here, we develop the epidemiological landscape as an interface between movement ecology and spatial epidemiology. We propose a movement-pathogen pace-of-life heuristic for prioritizing the landscape’s central processes by positing that spatial dynamics for fast pace-of-life pathogens are best-approximated by the spatial ecology of host contacts; spatial dynamics for slower pace-of-life pathogens are best approximated by host densities; and spatial dynamics for pathogens with environmental reservoirs reflect a convolution of those densities with the spatial configuration of environmental reservoir sites. We then identify mechanisms that underpin the epidemiological landscape and match each mechanism to emerging tools from movement ecology. Finally, we outline workflows for describing the epidemiological landscape and using it to predict subsequent patterns of pathogen transmission. Our framework links transmission to environmental context, providing a scaffold for mechanistically understanding how environmental context can generate and shift existing patterns in spatial epidemiology.