Spatial prediction of tree succession in northern Cape Breton Island, Nova Scotia (Cape Breton Island, Nova Scotia)

This thesis describes the development and application of a spatially-explicit forest-transition model to the Cape Breton Highlands in northern Cape Breton Island, Nova Scotia. Spatial quantities of available sunlight, degree-day accumulation, and soil-water content were used to calculate species-specific potential growth (PG), which was then linked to the establishment and growth of 16 tree species. Environment-species interactions were captured by way of an artificial neural network (ANN) trained to detect temporal patterns produced with a forest gap model (GIZELA) calibrated for environmental and forest conditions encountered in northern Cape Breton Island. Gap model runs for 20 vegetative community-biophysical condition combinations representative of the landscape heterogeneity in the Cape Breton Highlands were used to train the ANN. The ANN was found to capture much of the variability present in GIZELA simulations and have the ability to correctly predict the GIZELA-simulated dominant species 88% of the time. (Abstract shortened by UMI.)...