We demonstrate new multi-phase, multi-scale approaches for sampling and modeling native and exotic plant species to predict the spread of invasive species and aid in control efforts. Our test site is a 54,000-ha portion of Rocky Mountain National Park, Colorado, USA. This work is based on previous research wherein we developed vegetation sampling techniques to identify hot spots of diversity, important rare habitats, and locations of invasive plant species. Here we demonstrate statistical modeling tools to rapidly assess current patterns of native and exotic plant species to determine which habitats are most vulnerable to invasion by exotic species. We use stepwise multiple regression and modified residual kriging to estimate numbers of native species and exotic species, as well as probability of observing an exotic species in 30 × 30-m cells. Final models accounted for 62% of the variability observed in number of native species, 51% of the variability observed in number of exotic species, and 47% of the variability associated with observing an exotic species. Important independent variables used in developing the models include geographical location, elevation, slope, aspect, and Landsat TM bands 1-7. These models can direct resource managers to areas in need of further inventory, monitoring, and exotic species control efforts.