When should a pathogen strike? Navigating the landscape of birth pulses and mating season

Abstract

Susceptibility to pathogens often differs across host life stages, which in turn depends on seasonal host phenology. For many species, juveniles are highly susceptible relative to adults, which given constrained birth periods can lead to seasonal variation in the average host susceptibility. Similarly, for pathogens with complex lifecycles, or seasonally dependent development, peak transmission may be temporally constrained. Maximum pathogen transmission may therefore coincide with a greater abundance of either juvenile or adult hosts. For instance, the birthing season presents a highly susceptible pool of hosts while the mating season presents a higher rate of contact between potentially less susceptible hosts. When these two transmission opportunities are separated in time, it is unclear whether pathogens should evolve a single, intermediate transmission timing or undergo disruptive selection to exploit different host life stages. To investigate how pathogens might navigate this complex landscape, we developed an age-structured model that allows us to test the impacts of host life history on pathogen prevalence and evolution, inspired by a fungal plant pathogen. Parameterizing the model for Silene latifolia, we find that disease prevalence is highest when seed germination coincides with flowering, and that separating the two reduces disease. Using invasion analysis, we find that at the estimated S. latifolia parameters, a single evolutionarily stable transmission timing closely tracks germination time, with infectious flowering centered about seven weeks after the median germination day. When juveniles mature faster or are much more susceptible than adults, or under parameters for Dianthus pavonius, a pathogen genotype that transmits to spring-germinating juveniles and one that transmits later in the year can instead each invade the other and coexist as a polymorphic population when germination is split between spring and summer cohorts. Our model may help explain observed phenological differences seen in wild populations of infected plants.