Mechanistic models to forecast the response of an insect fungal pathogen to global climate change

Background/Question/Methods

Fungal pathogens represent a group of diseases in which climate change is expected to have direct,
dramatic impacts due to the strong influence of environmental factors on fungal life cycles and ecology. The
effects of climate on pathogen transmission, however, are poorly studied, and consequently it is unclear
whether climate change will cause epizootics of fungal pathogens to be more or less frequent. By using a
combination of field experiments and model-fitting, we are constructing mechanistic models predicting how
one such pathogen, Entomophaga maimaiga, which serves as a biological control for invasive gypsy moth
caterpillars (Lymantria dispar), will respond to different climate change scenarios. To investigate how this
disease functions under different climate conditions, we collected epizootic, experimental and weather data
during natural E. maimaiga outbreaks at three different sites along a 395.9 km latitudinal gradient weekly
for two summers. We experimentally measured fungal force of infection by placing cages of healthy insects
at each site for 24 hours each week. Using these data, we fit and compared different time- and weatherdependent
epidemiological models of pathogen dynamics. We tested general and site-specific models and
estimated parameter values using maximum likelihood calculations.

Results/Conclusions

A site-specific model in which fungal infection rate depended on total accumulated rainfall over the
previous 7 to 11 days proved to be the current best-fitting model. A general model with accumulated rainfalldependent
infection rates was the next highest ranked model (
ΔAICc > 3). Between-site differences are
likely due to inherent differences in soil quality and pathogen densities at each site. While meteorological
models predict that changes in mean precipitation levels will occur in some areas, an overall increase
in precipitation variance is expected to occur globally. Our data suggest that E. maimaiga transmission
is a strongly nonlinear function of rainfall. We therefore predict that higher variance in rainfall will lead to
pathogen epizootics that are more severe but that occur less frequently. Because pathogenic fungi naturally
help control pest-insects, understanding how these diseases will respond to climate change will allow us
to construct more informed long-term pest-control strategies. Moreover, our results provide one of the first
clear examples of the effects of weather on pathogen transmission. I plan to explore and test other spore
germination models in the future and will incorporate my findings into more realistic models of within- and
between-season disease transmission.