Pathogen-driven outbreaks in forest defoliators revisited: Building modelsfrom experimental data

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-vi rus interaction that are missing from earlier models, including seasonality in host reproduction, delays between host infection and death, and heterog eneity 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 conclusion s than earlier models. First, these models suggest that pathogen-driven out breaks in forest defoliators occur partly because newly hatched insect larv ae have higher average susceptibility than do older larvae. Second, the mod els show that a combination of seasonality and delays between infection and death can lead to unstable cycles in the absence of a stabilizing mechanis m; these cycles, however, are stabilized by the levels of heterogeneity in susceptibility that we have observed in our experimental data. Moreover, ou r experimental estimates of virus transmission rates and levels of heteroge neity in susceptibility in gypsy moth populations give model dynamics that closely approximate the dynamics of real gypsy moth populations. Although w e built our models from data for gypsy moth, our models are, nevertheless, quite general. Our conclusions are therefore likely to be true, not just fo r other defoliator-pathogen interactions, but for many host-pathogen intera ctions in which seasonality plays an important role. Our models thus give q ualitative insight into the dynamics of host-pathogen interactions, while p roviding a quantitative interpretation of our gypsy moth-virus data.