Extreme climate events significantly impact ecosystems and are predicted to increase in frequency and/or magnitude with climate change. Generalized extreme value (GEV) distributions describe most ecologically-relevant extreme events, including hurricanes, wildfires, exotic species outbreaks, and disease spread. The GEV is widely used in climate science as an accurate and flexible tool for studying how such extreme events are shifted by climate change over large spatial scales (>> 105 km2). However, we lack a realistic method to systematically incorporate such changes into ecological models on a small spatial scale, and so do not know how ecosystems are affected by climate-driven increases in severity or rate of extreme events.
Results/Conclusions
Here we show how to model extreme events shifted by climate change (or other factors) in four steps: (i) fit generalized extreme value (GEV) models on an ecologically-relevant spatial scale (~ 104 km2); (ii) use predictions from climate change models to describe changes in the parameters of the GEV; (iii) use the GEV to drive realistic, empirically-based population models; and (iv) examine population level impacts and ecological implications of climate change.
Research predicts hurricanes will increase in intensity, but frequency may remain the same, and our framework describes both changes in magnitude and frequency. As a case study, we use an understory shrub in southeast Florida with hurricane-driven dynamics, and measure the effects of change using stochastic population growth rate. We find that increasing hurricane magnitude has a much smaller effect on population growth than does increasing hurricane frequency, suggesting that stronger hurricanes in the future may not have drastic impacts on our study species. We use sensitivity analysis to analyze how our conclusions are affected by potential changes in damage levels to the canopy and canopy recovery rates. By applying our technique to other species, and to communities, scientists can determine the potential impacts of a variety of extreme events on ecosystems. In a rapidly changing world, our methods show how to combine realistic models of extreme events and of ecological populations to assess ecological impacts, and to prioritize conservation actions for at-risk populations.