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AbstractThe inherently variable nature of epidemics renders predictions of when and where infection is expected to occur challenging. Differences in pathogen strain composition, diversity, fitness, and spatial distribution are generally ignored in epidemiological modeling and are rarely studied in natural populations, yet they may be important drivers of epidemic trajectories. To examine how these factors are linked to epidemics in natural host populations, we collected epidemiological and genetic data from 15 populations of the powdery mildew fungus, Podosphaera plantaginis , on Plantago lanceolata in the Åland Islands, Finland. In each population, we tracked spatiotemporal disease progression throughout one epidemic season and coupled our survey of infection with intensive field sampling of the pathogen. We found that strain composition varied greatly among populations in the landscape. Within populations, strain composition was driven by the sequence of strain activity: early-active strains reached higher abundances, leading to consistent strain compositions over time. Co-occurring strains also varied in their contribution to the growth of the local epidemic, and these fitness inequalities were linked to epidemic dynamics: a higher proportion of hosts became infected in populations containing strains that were more similar in fitness. Epidemic trajectories in the populations were also linked to strain diversity and spatial dynamics: higher infection rates occurred in populations containing higher strain diversity, while spatially clustered epidemics experienced lower infection rates. Together, our results suggest that spatial and/or temporal variation in the strain composition, diversity, and fitness of pathogen populations are important factors generating variation in epidemiological trajectories among infected host populations.

Variation in individual-level disease transmission is well documented, but the underlying causes of this variation are challenging to disentangle in natural epidemics. In general, within-host replication is critical in determining the extent to which infected hosts shed transmission propagules, but which factors cause variation in this relationship are poorly understood. Here, using a plant host, Plantago lanceolata, and the powdery mildew fungus Podosphaera plantaginis, we quantify how the distinct stages of within-host spread (autoinfection), spore release, and successful transmission to new hosts (alloinfection) are influenced by host genotype, pathogen genotype, and the coinfection status of the host. We find that within-host spread alone fails to predict transmission rates, as this relationship is modified by genetic variation in hosts and pathogens. Their contributions change throughout the course of the epidemic. Host genotype and coinfection had particularly pronounced effects on the dynamics of spore release from infected hosts. Confidently predicting disease spread from local levels of individual transmission, therefore, requires a more nuanced understanding of genotype-specific infection outcomes. This knowledge is key to better understanding the drivers of epidemiological dynamics and the resulting evolutionary trajectories of infectious disease.

The ecological and evolutionary dynamics of species are influenced by spatiotemporal variation in population size. Unfortunately, we are usually limited in our ability to investigate the numerical dynamics of natural populations across large spatial scales and over long periods of time. Here we combine mechanistic and statistical approaches to reconstruct continuous-time infection dynamics of an obligate fungal pathogen on the basis of discrete-time occurrence data. The pathogen, Podosphaera plantaginis, infects its host plant, Plantago lanceolata, in a metapopulation setting where the presence of the pathogen has been recorded annually for 6 years in approximately 4,000 host populations across an area of 50 km x 70 km in Finland. The dynamics are driven by strong seasonality, with a high extinction rate during winter and epidemic expansion in summer for local pathogen populations. We are able to identify with our model the regions in the study area where overwintering has been most successful. These overwintering sites represent foci that initiate local epidemics during the growing season. There is striking heterogeneity at the regional scale in both the overwintering success of the pathogen and the encounter intensity between the host and the pathogen. Such heterogeneity has profound implications for the coevolutionary dynamics of the interaction.