Random Forests is frequently used to model species distributions over large geographic areas. Complications arise when data used to train the models have been collected in stratified designs that involve different sampling intensity per stratum. The modeling process is further complicated if some of the target species are relatively rare on the landscape leading to an unbalanced number of presences and absences in the training data. We explored means to accommodate unequal sampling intensity across strata as well as the unbalanced species prevalence in Random Forest models for tree and shrub species distributions in the state of Nevada. For the unequal sampling intensity issue, we tested three modeling strategies: fitting models using all the data, down-sampling the intensified stratum; and building separate models for each stratum. We explored unbalanced species prevalence by investigating the effects of down-sampling the more prevalent response (presence or absence), and by optimizing the cutoff thresholds for declaring a species present. When modeling species presence with stratified data that was collected with different sampling intensities per stratum, we found that neither down-sampling the intensified stratum, nor fitting individual strata models, improved model performance. We also found that balancing the number of presences and absences in a training data set by down-sampling did not improve predictive models of species distributions, and did not eliminate the need to optimize thresholds. We then apply our final choice of model to the full raster layers for Nevada to produce statewide species distribution maps.