We tested the efficacy of a snow-tracking-based model for predicting wolf (Canis lupus) distribution and environmental relationships, using n independent radiotelemetry data dataset. We documented tracks in snow on highway rights-of-way and adjacent transects in the central Rocky Mountains of Alberta, Canada between November and March, 1997–2000. Radiotelemetry data (ground and aerial) were collected in the same region for 2 wolf packs between 1991–1993. We assessed the relationship between wolf track data and topographic, vegetative, and prey metrics, using a Geographic Information System (GIS), logistic regression, and Akaike's Information Criterion (AIC). We transformed our optimal regression model into a probability surface in GIS and verified that surface using radiotelemetry data and a Receiver Operating Characteristic (ROC) curve. The optimal model showed that wolf presences were positively related to wetness (mature, possibly more complex forest), and elk (Cervus elaphus), and deer (Odocoileus sp.) track density and negatively associated with terrain ruggedness and open canopy. The ROC curve indicated that the track-based model was robust (AUC=0.78). We concluded that track data provide a reliable, cost-effective approach for determining distribution and predicting wolf–environmental relationships in mountainous regions.