Ecological niche modeling (ENM) is an effective tool for providing innovative insights to questions in evolution, ecology and conservation. As environmental datasets accumulate, modelers need to evaluate the relative merit of different types of data for ENM. We used three alternative environmental data sets: climatic data, remote-sensing data (Normalized Difference Vegetation Index), and elevation data, to model the distribution of six bird species of the genus Grallaria in the Ecuadorian Andes. We assessed the performance of models created with each environmental data set and all possible combinations by comparing the geographic predictions of our models with detailed maps developed by expert ornithologists. Results varied depending on the specific measure of performance. Models including climate variables performed relatively well across most measures, whereas models using only NDVI performed poorly. Elevation based models were relatively good at predicting most sites of expected occurrence but showed a high over-prediction error. Combinations of data sets usually increased the performance of the models, but not significantly. Our results highlight the importance of including climatic variables in ENM and the simultaneous use of various data sets when possible. This strategy attenuates the effects of specific variables that decrease model performance. Remote-sensing data, such as NDVI, should be used with caution in topographically complex regions with heavy cloud-cover. Nonetheless, remote-sensing data have the potential to improve ENM. Finally, we suggest a priori designation of modeling purposes to define specific performance measures accordingly.