Usnea hirta, an important member of the lichen family Parmeliaceae, has long been used as a bio-monitor of air pollution, particularly of sulphur dioxide in North America. Although U. hirta has a wide geographical distribution, it is important to be able to identify accurately the optimal habitat conditions for air pollution-sensitive species, thus making it possible to more effectively and efficiently establish air quality bio-monitoring stations. We modelled the distribution of U. hirta as a function of nine variables, five macroclimatic variables: average monthly precipitation, average monthly minimum temperature, average monthly maximum temperature, solar radiation, and integrated moisture index, and four topographic variables: elevation, slope, aspect, and land forms and uses for the White River National Forest, Colorado. The response variable was developed based on the presence or absence of U. hirta at each of 72 bio-monitoring baseline sites established in selected portions of four intermountain area states. Our model was developed using Non-Parametric Multiplicative Regression (NPMR) analysis, a modelling approach that analyzes environmental gradients, or predictor variables, against known locations for individuals of the model species. Finally, we evaluated our model on the basis of log β values and overall improvement over a naïve model and the Monte Carlo Permutation Test with 1000 randomized runs. The best model for U. hirta included four variables – solar radiation, average monthly precipitation, and average monthly minimum and maximum temperatures (log β=3·68). Among these four variables, average monthly maximum temperature was the most influential predictor (sensitivity=0·71) for the distribution of U. hirta. The occurrence rate for U. hirta, based on field validation, was 45·5%, 65·4%, and 70·4% for low, medium, and high probability areas, respectively. This study showed that our model was successful in predicting the distribution of U. hirta in the White River National Forest. Based on these results, the north-eastern and western portions of the forest appear to offer the most favourable conditions for the installation of future air quality bio-monitoring baseline sites.