Changes in regional forest composition in response to climate change are often predicted using niche-based models or biophysical process models that either do not account for or greatly simplify processes such as succession, dispersal, and tree harvest. We simulated changes in forest composition and structure from year 2000 to 2300 in the Northeastern U.S. using a modeling approach that accounted for succession, tree harvest, and climate change. We used a coupled forest landscape (LANDIS PRO) and ecosystem (LINKAGES II) modeling approach to capture stand and landscape level processes for 24 tree species at a high resolution over a large geographic extent. In general succession was the primary driver of forest change but in the medium to long term climate change became more important. Species at the northern or southern extents of their range were the most affected by climate change. We believe our approach provides a more realistic simulation of forest change because it mechanistically accounted for many detailed processes such as tree growth, mortality, competition, dispersal, establishment, and harvest that are not similarly addressed in many other modeling approaches.
Frank Thompson is a Research Wildlife Biologist with USDA Forest Service Northern Research Station and a Cooperative Professor in the Department of Fisheries and Wildlife Sciences at the University of Missouri-Columbia; he also represents the University of Missouri as a principal investigator for the Northeast Climate Science Center. He received his PhD in Fisheries and Wildlife Sciences at the University of Missouri-Columbia in 1987 where he has remained as a researcher for the Forest Service and adjunct faculty. His research addresses the ecology and conservation of songbirds, bats, and herpetofauna but his primary focus has been understanding factors affecting the demographics and viability of migrant songbird populations. During the last 10 years his work has expanded to include the development and application of population and landscape models to large spatial scales to understand the implications of land management and climate change on populations and forest landscapes at regional scales.