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Models of outbreaks in forest-defoliating insects are typically built from a priori considerations and tested only with long time series of abundances. We instead present a model built from experimental data on the gypsy moth and its nuclear polyhedrosis virus, which has been extensively tested with epidemic data. These data have identified key details of the gypsy moth-virus interaction that are missing from earlier models, including seasonality in host reproduction, delays between host infection and death, and heterogeneity among hosts in their susceptibility to the virus. Allowing for these details produces models in which annual epidemics are followed by bouts of reproduction among surviving hosts and leads to quite different conclusions than earlier models. First, these models suggest that pathogen-driven outbreaks in forest defoliators occur partly because newly hatched insect larvae have higher average susceptibility than do older larvae. Second, the models show that a combination of seasonality and delays between infection and death can lead to unstable cycles in the absence of a stabilizing mechanism; these cycles, however, are stabilized by the levels of heterogeneity in susceptibility that we have observed in our experimental data. Moreover, our experimental estimates of virus transmission rates and levels of heterogeneity in susceptibility in gypsy moth populations give model dynamics that closely approximate the dynamics of real gypsy moth populations. Although we built our models from data for gypsy moth, our models are, nevertheless, quite general. Our conclusions are therefore likely to be true, not just for other defoliator-pathogen interactions, but for many host-pathogen interactions in which seasonality plays an important role. Our models thus give qualitative insight into the dynamics of host-pathogen interactions, while providing a quantitative interpretation of our gypsy moth-virus data.

Most mathematical models of disease assume that transmission is linearly dependent on the densities of host and pathogen. Recent data for animal diseases, however, have cast doubt on this assumption, without assessing the usefulness of alternative models. In this article, we use a combination of laboratory dose-response experiments, field transmission experiments, and observations of naturally occurring populations to show that virus transmission in gypsy moths is a nonlinear function of virus density, apparently because of heterogeneity among individual gypsy moth larvae in their susceptibility to the virus. Dose-response experiments showed that larvae from a laboratory colony of gypsy moths are substantially less heterogeneous in their susceptibility to the virus than are larvae from feral populations, and field experiments showed that there is a more strongly nonlinear relationship between transmission and virus density for feral larvae than for lab larvae. This nonlinearity in transmission changes the dynamics of the virus in natural populations so that a model incorporating host heterogeneity in susceptibility to the virus gives a much better fit to data on virus dynamics from large-scale field plots than does a classical model that ignores host heterogeneity. Our results suggest that heterogeneity among individuals has important effects on the dynamics of disease in insects at several spatial and temporal scales and that heterogeneity in susceptibility may be of general importance in the ecology of disease.