Though climate change is predicted to cause major shifts in infectious disease risk, definitive evidence is often elusive due to data limitations and confounding factors. Thus, disease outbreaks are often interpreted as stochastic events, rather than a response to changing environmental conditions. Nonlinearities in climate change patterns can also complicate inference of mechanistic drivers. Nonlinear stochastic events, such as droughts, are predicted to increase in frequency and severity throughout various parts of the globe and negatively affect fungal pathogens. Here we take advantage of a unique long-term dataset (two survey periods spanning ~19 years; over 8,000 individual hosts) of the fungal tree disease, white pine blister rust (Cronartium ribicola Fisch., blister rust). We predicted that blister rust was nonlinearly related to climate. Due to this nonlinear relationship, we expected that climate change over the past nineteen years shifted blister rust nonlinearly, increasing infections in colder regions and decreasing infections in hotter, drier regions. We hypothesized that a mechanism driving the nonlinear range shift was an interaction between increased water stress and blister rust infections. Finally, we predicted that the combined direct and interacting effects of climate change on blister rust resulted in a range shift, not a range expansion.
Results/Conclusions
Using a novel, first differences panel modeling approach, we found evidence of nonlinear responses of disease spread to rising temperatures. This nonlinear effect increased infections in colder climates and may have decreased new infections in the hottest, driest conditions. We demonstrated that the interaction of disease and water stress in infected hosts contributed to nonlinear shifts in pathogen infections. Though many studies predict nonlinear climate effects on disease spread, we present some of the first empirical evidence of this relationship. Our results underscore the importance of quantifying nonlinearities in climate–disease interactions to improve predictions of disease outbreaks.