• Andrews, Caitlin Marie
  School of Forest Resources, University of Maine
  

The spruce-fir (Picea-Abies) forest type of the Acadian Region is at risk of disappearing from the United States and parts of Canada due to climate change and associated impacts. Managing for the ecosystem services provided by this forest type requires accurate forecasting of forest metrics across this broad international region in the face of the expected redistribution of tree species. This analysis linked species specific data with climate and topographic variables using the nonparametric random forest algorithm, to generate models that accurately predicted changes in species distribution due to climate change. A comprehensive dataset, consisting of 10,493,619 observations from twenty-two agencies, including historical inventories, assured accurate assignation of species distribution at a finer resolution (1 km2 ) than previous analyses. Different dependent variables were utilized, including presence/absence, a likelihood value, abundance variables (i.e. basal area, stem density, and importance value), and predicted maximum stand density index (SDImax), in order to inspect the difference in results in regards to their conservation management utility, as well as the effects of inherent species life history traits on outcomes.