Conservation of bird populations is increasingly focused on landscapes. We combined data collected in 2005–2011 from 16,250 North American Breeding Bird Survey (BBS) survey points with local and remotely sensed environmental data to model the distribution of 7 grassland bird species in the Northern Great Plains of the United States. We analyzed data at the survey point level, which is consistent with the scale of conservation treatments that we apply, and avoided information loss caused by pooling data at the BBS route level. By accounting for observer effects, nesting of survey points within routes, and sequence of survey points, we accommodated BBS survey design, refined estimates of important habitat predictors, improved model fit, and reduced or eliminated positive spatial autocorrelation in model residuals. The predictive power of models was greatly increased by including variables that characterized annual and long-term precipitation, as well as local land cover attributes not available from satellite-derived land cover data. Occurrence models from survey-point-level BBS data and environmental data with high thematic resolution were able to describe habitat relationships that are often associated with fine-grained, local studies, but across broad spatial extents and at scales relevant to local conservation actions. Predicted occurrence was strongly correlated with observed numbers, suggesting that occurrence models may be useful indicators of density. Relationships derived from models allowed us to develop spatially explicit decision support tools, which can be used to target areas for conservation treatments and to assess the conservation actions of multiple conservation programs and joint ventures (e.g., Prairie Pothole, Rainwater Basin, and Northern Great Plains joint ventures) in the U.S. Northern Great Plains.