One of the greatest challenges ecologists face is predicting how climate change will affect the organisms with which we share our planet. Ecological theory predicts that species current distributions are determined by their climatic niches (i.e. fitness as a function of climate). Statistical models relating species geographic distributions to climate (SDM’s – species distribution models) are therefore used to predict shifts in species distributions with climate change. However, there are challenges to using this approach at the local scale at which most conservation biologists and ecologists work. For one, these models ignore population dynamics and dispersal, which determine how rapidly species ranges shift through the establishment of new populations at the leading range edge, and the extinction of populations at the trailing range edge. Additionally, species interact, and these interactions are likely to add complexity to projections of community shifts based on ‘summing up’ projections from individually fit SDM models. Finally, the climatic drivers of species performance are modeled (not measured), and may not be accurate at fine spatial scales. Here, I discuss these challenges and some of the approaches my lab has used to overcome them using extensive data collected from Mt. Rainier National Park.