Distribution models of invasive plants are very useful tools for conservation management. There are challenges in modeling expanding populations, especially in a dynamic environment, and when data are limited. In this paper, predictive habitat models were assessed for three invasive plant species, at differing levels of occurrence, using two different habitat modeling techniques: logistic regression and maximum entropy. The influence of disturbance, spatial and temporal heterogeneity, and other landscape characteristics is assessed by creating regional level models based on occurrence records from the USDA Forest Service’s Forest Inventory and Analysis database. Logistic regression and maximum entropy models were assessed independently. Ensemble models were developed to combine the predictions of the two analysis approaches to obtain a more robust prediction estimate. All species had strong models with Area Under the receiver operator Curve (AUC) of >0.75. The species with the highest occurrence, Ligustrum spp., had the greatest agreement between the models (93%). Lolium arundinaceum had the most disagreement between models at 33% and the lowest AUC values. Overall, the strength of integrative modeling in assessing and understanding habitat modeling was demonstrated.