Long-term disease dynamics in lakes: causes and consequences of chytrid infections in Daphnia populations

Understanding the drivers and consequences of disease epidemics is an important frontier in ecology. However, long-term data on hosts, their parasites, and the corresponding environmental conditions necessary to explore these interactions are often unavailable. We examined the dynamics of Daphnia pulicaria, a keystone zooplankter in lake ecosystems, to explore the long-term causes and consequences of infection by a chytridiomycete parasitoid (Polycaryum laeve). After quantifying host-pathogen dynamics from vouchered samples collected over 15 years, we used autoregressive models to evaluate (1) hypothesized drivers of infection, including host density, water temperature, dissolved oxygen, host-food availability, and lake mixing; and (2) the effects of epidemics on host populations. Infection was present in most years but varied widely in prevalence, from,1% to 34%, with seasonal peaks in early spring and late fall. Within years, lake strati. cation strongly inhibited P. laeve transmission, such that epidemics occurred primarily during periods of water mixing. Development of the thermocline likely reduced transmission by spatially separating susceptible hosts from infectious zoospores. Among years, ice duration and cumulative snowfall correlated negatively with infection prevalence, likely because of reductions in spring phytoplankton and D. pulicaria density in years with extended winters. Epidemics also influenced dynamics of the host population. Infected D. pulicaria rarely (< 1%) contained eggs, and P. laeve prevalence was positively correlated with sexual reproduction in D. pulicaria. Analyses of D. pulicaria density-dependent population dynamics predicted that, in the absence of P. laeve infection, host abundance would be 11-50% higher than what was observed. By underscoring the importance of complex physical processes in controlling host-parasite interactions and of epidemic disease in influencing host populations, our results highlight the value of long-term data for understanding wildlife disease dynamics.