At some alpine treelines in the Rocky Mountains, whitebark pine (Pinus albicaulis)?a keystone species?plays a central role in tree island development through facilitation. Whitebark pine occurs both as a solitary tree and also as a component of tree islands, although relative importance of these two patterns varies geographically. We examine the utility of four predictive models to understand how the functional role of a keystone species varies spatially with biophysical conditions. We use a novel data set to predict whitebark pine's functional role, characterized by spatial association and relative position within a tree island at three North American Rocky Mountain treelines. For the study areas combined, and at a study area level, we compared prediction accuracy and variable importance among these modeling approaches: general linear models, classification and regression trees, random forests, and support vector machines. Results revealed that the keystone role of whitebark pine varied spatially. For the combined model, growing season temperature and slope curvature were the most important predictive variables for association and relative position, as revealed by overall agreement among the four models. Prediction accuracy and variable importance varied at the study area level, though, indicating that different conclusions could be drawn from each model, if examined independently. We advocate comparing results from different modeling approaches for complex, field-derived data sets because it might enable a better understanding of model and variable selection and appropriateness of input data resolution. Furthermore, comparative modeling enables assessment of the relative predictive and interpretive capacities of each modeling approach.