Predicitng potential changes in suitable habitat and distribution by 2100 for tree species of the Eastern United States

We predict current distribution and abundance for tree species present in eastern North America, and subsequently estimate potential suitable habitat for those species under a changed climate with 2 x CO2. We used a series of statistical models (i.e., Regression Tree Analysis (RTA), Multivariate Adaptive Regression Splines (MARS), Bagging Trees (BT) and Random Forests (RF)) via our model, DISTRIB, for this purpose. These techniques were evaluated on several tree species, and advantages and disadvantages of each method were noted. RF provides the best prediction maps of potential suitable habitat. Overall, a combination of RTA, BT, and RF may yield the best information and most interpretable maps of suitable habitat. Using these tools, we provide statistics on potential changes in suitable habitat for 135 tree species of eastern North America. A suitable habitat does not guarantee the presence of a species, as many barriers for the species still exist before it will be able to colonize that new suitable habitat. Dispersal ability, abundance of the colonizing species, and the nature of fragmented landscapes also influence migration and are modeled with our cellular automata model, SHIFT. For each cell outside a species' current boundary, SHIFT creates an estimate of the probability that each unoccupied cell will become colonized over 100 years. By evaluating the probability of colonization within the potential "new" suitable habitat, we can estimate the proportion of new habitat that might be colonized within a century. This proportion is low (15%) for five example species, suggesting that there is a serious lag between the potential movement of suitable habitat and the potential for the species to migrate into the new habitat. However, humans could accidentally or purposefully alter the migration rates of species by physically moving the propagules.