Kyle, C. | Search bioRxiv

bioRxiv2023

Climate-Change Driven Decline of an Insect Pathogen Increases the Risk of Defoliation by a Forest Pest Insect

The effects of climate change on forest-defoliating insects are poorly understood, a problem that is especially urgent in the case of the spongy moth (formerly "the gypsy moth", Lymantria dispar). For decades following its introduction in 1869, the spongy moth severely defoliated North American forests, but the introduction of the pathogen Entomophaga maimaiga in 1989 drastically lowered defoliation levels. E. maimaiga, however, needs cool, moist conditions, whereas climate change is bringing hot, dry conditions to the range of the spongy moth. Here we use an empirically validated mathematical model to project that climate change will sharply reduce E. maimaiga infection rates, greatly increasing spongy moth defoliation. Recent data show that defoliation has strongly rebounded, supporting our projections. Our work shows that the effects of climate change on insect pathogens can have dire consequences for forests, and demonstrates the importance of understanding how climate change can alter species interactions.

ecology↗

bioRxiv2016

Disentangling the Effects of Weather and Densityon the Dynamics of a Fungal Pathogen

Nonlinear fitting algorithms have illuminated the role of weather in human diseases, by allowing for robust tests of mechanistic transmission models, but a lack of data has prevented applications to animal diseases. This is important because classical models that neglect weather predict that there will be a host density threshold, below which epidemic intensity will be slight, but models that include weather predict that this threshold will often be obliterated by weather variability. To test the applicability of thresholds to animal diseases, we estimated infection rates of the fungal pathogen Entomophaga maimaiga in the gypsy moth, by collecting larvae during epidemics at a range of host densities and weather conditions, and we estimated the pathogen's force of infection, by exposing experimental larvae to the pathogen for 24 h periods in the field. By fitting a range of models to our data, we show that epidemics of this pathogen are best explained by a model that allows for positive effects of both host density and cool, moist weather on transmission, such that weather-only and density-dependence-only models provide vastly poorer explanations for the data. Despite the effects of weather, the combined model shows that the effects of density in E. maimaiga are strong enough to ensure that the density threshold will have important effects on the probability of epidemics. Our work shows that weather and density-dependent transmission can interact in non-intuitive ways, and provides an illustration of the usefulness of nonlinear fitting for understanding animal diseases.

ecology↗