This contains model code associated with the paper titled Population response to extreme climate events depends on population spatial distribution Torstenson MS, Shaw AK Extreme climate events, which are increasing in frequency and severity with climate change, can cause mass mortality events in animal populations. Meanwhile, populations of migratory animals around the world are in decline. We illustrate how the spatial aggregation typical in many migratory populations can increase the likelihood of population declines in response to extreme climate events. First, we demonstrate that high levels of spatial aggregation make it possible for higher levels of population mortality to result from spatially limited disturbances. This aligns with observations of mass mortality events due to extreme climate events in migratory animal populations. We go on to use a flow-kick model to demonstrate that because higher levels of spatial aggregation result in less frequent, but more severe impacts, population crashes in response to extreme events are more likely in highly aggregated populations. This provides a mechanism by which migratory populations may be especially vulnerable to climate change. We quantify what regimes of disturbance (with respect to frequency and severity) lead to population collapse versus resilience, and we show how our results depend on the form of disturbance (proportional vs density-dependent). Finally, we compare results from an analytic approximation with those from a simulation and discuss differences. The results of our model can also be used to understand the interacting effects of shifting extreme climate event regimes and land use change. We predict that land use changes that increase the spatial aggregation of populations, such as habitat destruction or degradation of habitat corridors, will increase the likelihood of population declines due to extreme climate events. Conservation plans that increase the dispersion of populations across the landscape may increase population resilience to changing extreme climate event regimes.