Modeling potential future individual tree-species distributions in the eastern United States under a climate change scenario: a case study with Pinus virginiana

We are using a deterministic regression tree analysis model (DISTRIB) and a stochastic migration model (SHIFT) to examine potential distributions of 66 individual species of eastern US trees under a 2 x C02 climate change scenario. This process is demonstrated for Virginia pine (Pinus virginiana). USDA Forest Service Forest Inventory and Analysis data for more than 100000 plots and nearly 3 million trees east of the 100th meridian were analyzed and aggregated to the county level to provide species importance values for each of more than 2100 counties. County-level data also were compiled on climate, soils, land use, elevation, and spatial pattern. Regression tree analysis (RTA) was used to devise prediction rules from current species-environment relationships, which were then used to replicate the current distribution and predict the potential future distributions under two scenarios of climate change (2 x CO,). RTA allows different variables to control importance value predictions at different regions, e.g. at the northern versus southern range limits of a species. RTA outputs represent the potential 'environmental envelope' shifts required by species, while the migration model predicts the more realistic shifts based on colonization probabilities from varying species abundances within a fragmented landscape. The model shows severely limited migration in regions of high forest fragmentation, particularly when the species is low in abundance near the range boundary. These tools are providing mechanisms for evaluating the relationships among various environmental and landscape factors associated with tree-species importance and potential migration in a changing global climate.