Recent studies suggest that climate warming in interior Alaska may result in major shifts from spruce-dominated forests to broadleaf-dominated forests or even grasslands. To quantify patterns in tree distribution and abundance and to investigate the potential for changes in forest dynamics through time, we initiated a spatially extensive vegetation monitoring program covering 1.28 million ha in Denali National Park and Preserve (DNPP). Using a probabilistic sampling design, we collected field measurements throughout the study area to develop spatially explicit Bayesian hierarchical models of tree occupancy and abundance. These models demonstrated a strong partitioning of the landscape among the six tree species in DNPP, and allowed us to account for and examine residual spatial autocorrelation in our data. Tree distributions were governed by two primary ecological gradients: (1) the gradient from low elevation, poorly drained, permafrost-influenced sites with shallow active layers and low soil pH (dominated by Picea mariana) to deeply thawed and more productive sites at mid-elevation with higher soil pH on mineral substrate (dominated by Picea glauca); and (2) the gradient from older, less recently disturbed sites dominated by conifers to those recently affected by disturbance in the form of fire and flooding with increased occupancy and abundance of broadleaf species. We found that the establishment of broadleaf species was largely dependent on disturbance, and mixed forests and pure stands of broadleaf trees were relatively rare and occurred in localized areas. Contrary to recent work in nearby areas of interior Alaska, our results suggest that P. glauca distribution may actually increase in DNPP under warming conditions rather than decline as previously predicted, as P. glauca expands into areas formerly underlain by permafrost. We found no evidence of a shift to broadleaf forests in DNPP, particularly in the poorly drained basin landscape positions that may be resistant to such changes. Overall, our results indicate that probabilistic sampling conducted at a landscape scale can improve inference relative to the habitat associations driving the distribution and abundance of trees in the boreal forest and the potential effects of climate change on them.