To keep pace with future climate change, forest tree species are often predicted to need to shift their geographic ranges and phenology to minimize exposure to climates they have not experienced in the recent past. While many approaches have been developed to predict range shifts and shifting phenology, most large-scale, spatial techniques do not explicitly account for intraspecific genetic variation. This can be problematic when populations are locally adapted to climate, a common characteristic of plant species, as species-level responses to climate may not be representative of populations. In this dissertation, I use balsam poplar (Populus balsamifera L.), a northern North American deciduous tree species, to test a variety of techniques of integrating genetic information with spatial models of balsam poplar’s distribution and phenology. First, I tested multiple hypotheses, identified in the literature, for their ability to predict genetic diversity in balsam poplar. Results show that diversity in balsam poplar was highest in the center of the range and lowest near the range edge – consistent with the ‘central-periphery hypothesis.’ Second, I tested whether genetically-informed distribution models are more transferable through time, than standard distribution models. Using pollen and fossil records to validate models, I show that standard and genetically-informed distribution models perform similarly through time, but genetically-informed models offer additional insights into where populations may have originated on the landscape during the last glacial maximum. Third, I developed a new approach to predict population’s exposure to future climate change. Using spatial models of adaptive genetic differentiation, I show that populations in the eastern portion of balsam poplar’s range have the greatest predicted exposure to climate change as they would need to migrate the furthest and will see the greatest disruption in their gene-climate association. Fourth, I assessed whether a genomic prediction of common garden observations of phenology can inform phenology measured on the landscape with remote sensing. I show that the genomic prediction was the most important variable explaining the date of spring onset on the landscape, but was relatively unimportant in predicting the heat sum accumulated at the date of spring onset. I also show that model error was correlated with multiple meteorological variables, including winter temperatures – illustrating the challenges of predicting phenology in changing climates.