| Bioclimate envelope models are widely used to project potential species habitat under changing climate. Conceptually, these models are also well suited to match natural resource management practices to new climatic realities, for example by guiding species choice in reforestation programs. Nevertheless, uncertainty due to a variety of causes has so far limited the practical application of bioclimate envelope models. The goal of this thesis is to examine sources of uncertainty, to reduce uncertainty if possible, and to develop methodology to systematically deal with the remaining variability in model projections. Secondly, this thesis develops practical climate change adaptation strategies for the forestry sector in western Canada. This requires answering what species should be used for reforestation for a particular site, and subsequently selecting planting stock of the species that is best adapted to current and anticipated environments.;Given the large uncertainties in model projections, it is important to remember that ultimately, climate change adaptation has to be guided by climate trends that actually materialize. A considerable portion of this thesis therefore analyzes climate trends in western Canada over the past century. In a case study for aspen, it is shown that the combined information from multiple bioclimate envelope model runs, climate trends that have already materialized, and observed climate change impacts can make a strong case for implementing adaptation strategies in central Alberta. Amendments to aspen reforestation practices are proposed, avoiding the use of the species in areas where it is likely to lose habitat in the future, and recommending movement of planting stock so that it is reasonably well adapted under a range of future climate scenarios.;Using a novel approach to partition variance in results from multiple model runs, climate data were identified as arguably the most important source of uncertainty. Variation was primarily caused by different general circulation models, followed by different emission scenarios. Also, the method used to interpolate current weather station data was an important contributor to uncertainty at specific locations. Other sources of uncertainty were the choice of predictor variables and different bioclimate envelope modeling methods, which primarily contributed to uncertainty through interaction effects. For example, different modeling methods provided similar habitat projections for western Canada on average, but under certain climate change scenarios their results differed markedly. |
