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Host species that can independently maintain a pathogen in a host community and contribute to infection in other species are important targets for disease management. However, the potential of host species to maintain a pathogen is not fixed over time, and an important challenge is understanding how within- and across-season variability in host maintenance potential affects pathogen persistence over longer time scales relevant for disease management (e.g., years). Here, we sought to understand the causes and consequences of seasonal infection dynamics in leopard frogs (Rana sphenocephala and Rana pipiens) infected with the fungal pathogen Batrachochytrium dendrobatidis (Bd). We addressed three questions broadly applicable to seasonal host-parasite systems. First, to what degree are observed seasonal patterns in infection driven by temperature-dependent infection processes compared to seasonal host demographic processes? Second, how does seasonal variation in maintenance potential affect long-term pathogen persistence in multi-host communities? Third, does high deterministic maintenance potential relate to the long-term stochastic persistence of pathogens in host populations with seasonal infection dynamics? To answer these questions, we used field data collected over 3 years on >1400 amphibians across four geographic locations, laboratory and mesocosm experiments, and a novel mathematical model. We found that the mechanisms that drive seasonal prevalence were different from those driving seasonal infection intensity. Seasonal variation in Bd prevalence was driven primarily by changes in host contact rates associated with breeding migrations to and from aquatic habitat. In contrast, seasonal changes in infection intensity were driven by temperature-induced changes in Bd growth rate. Using our model, we found that the maintenance potential of leopard frogs varied significantly throughout the year and that seasonal troughs in infection prevalence made it unlikely that leopard frogs were responsible for long-term Bd persistence in these seasonal amphibian communities, highlighting the importance of alternative pathogen reservoirs for Bd persistence. Our results have broad implications for management in seasonal host-pathogen systems, showing that seasonal changes in host and pathogen vital rates, rather than the depletion of susceptible hosts, can lead to troughs in pathogen prevalence and stochastic pathogen extirpation., (© 2022 The Ecological Society of America.)

Accurate estimates of seasonal infection risk can be used by animal health officials to predict future disease risk and improve understanding of the mechanisms driving disease dynamics. It can be difficult to estimate seasonal infection risk in wildlife disease systems because surveillance assays typically target antibodies (serosurveillance), which are not necessarily indicative of current infection, and serosurveillance sampling is often opportunistic. Recently developed methods estimate past time of infection from serosurveillance data using quantitative serological assays that indicate the amount of antibodies in a serology sample. However, current methods do not account for common opportunistic and uneven sampling associated with serosurveillance data. We extended the framework of survival analysis to improve estimates of seasonal infection risk from serosurveillance data across population and regional scales. We found that accounting for the right-censored nature of quantitative serology samples greatly improved estimates of seasonal infection risk, even when sampling was uneven in time. Survival analysis can also be used to account for common challenges when estimating infection risk from serology data, such as biases induced by host demography and continually elevated antibodies following infection. The framework developed herein is widely applicable for estimating seasonal infection risk from serosurveillance data in humans, wildlife, and livestock., (© 2019 by the Ecological Society of America.)

Few hosts have many parasites while many hosts have few parasites. This axiom of macroparasite aggregation is so pervasive it is considered a general law in disease ecology, with important implications for the dynamics of host-parasite systems. Because of these dynamical implications, a significant amount of work has explored both the various mechanisms leading to parasite aggregation patterns and how to infer mechanism from these patterns. However, as many disease mechanisms can produce similar aggregation patterns, it is not clear whether aggregation itself provides any additional information about mechanism. Here we apply a "constraint-based" approach developed in macroecology that allows us to explore whether parasite aggregation contains any additional information beyond what is provided by mean parasite load. We tested two constraint-based null models, both of which were constrained on the total number of parasites P and hosts H found in a sample, using data from 842 observed amphibian host-trematode parasite distributions. We found that constraint-based models captured ~85% of the observed variation in host-parasite distributions, suggesting that the constraints P and H contain much of the information about the shape of the host-parasite distribution. However, we also found that extending the constraint-based null models can identify the potential role of known aggregating mechanisms (such as host heterogeneity) and disaggregating mechanisms (such as parasite-induced host mortality) in constraining host-parasite distributions. Thus, by providing robust null models, constraint-based approaches can help guide investigations aimed at detecting biological processes that directly affect parasite aggregation above and beyond those that indirectly affect aggregation through P and H., (© 2016 by the Ecological Society of America.)