Skip to main content
U.S. Department of Energy
Office of Scientific and Technical Information

Canadian vegetation response to climate and projected climatic change

Thesis/Dissertation ·
OSTI ID:5644844
The response of Canadian vegetation to climate and climatic change was modeled at three organizational levels of the vegetation mosaic. Snowpack, degree-days, minimum temperature, soil moisture deficit, and actual evapotranspiration are components of climate that physiologically constrain distribution of dominant plant life-forms and species. The rule-based Canadian Climate-Vegetation Model (CCVM) predicts the response of vegetation formations to climate. The CCVM simulation for current climatic conditions is more accurate and detailed than those of other equilibrium models. Ecological response surfaces predict the probability of dominance for eight boreal tree species in Canada with success. Variation in the probability of dominance is related to the species' individualistic response to climatic constraints within different airmass regions. A boreal forest-type classification shows a high degree of geographic correspondence with observed forest-types. Under two doubled-CO[sub 2] climatic scenarios, CCVM predicts a reduction in arctic tundra and subarctic woodland, a northward shift in the distribution of boreal evergreen forest, and an expansion of temperate forest, boreal summergreen woodland, and two prairie formations. The response surfaces predict significant changes in species dominance under both climatic scenarios. Species exhibit an individualistic responses to climatic change. Most of the boreal forest-types derived from future probabilities of dominance are analogous to extant forest-types, but fewer types are distinguished. Geographic correspondence in the simulated boreal forest regions under both the current and projected climates provides a link between the results of the two modelling approaches. Even with constraints, the realism of the vegetation scenarios in this study are arguably the most reliable and comprehensive predictions for Canada.
Research Organization:
Oregon State Univ., Corvallis, OR (United States)
OSTI ID:
5644844
Country of Publication:
United States
Language:
English