Coupling LINKAGES and LANDIS Pro to predict future tree species distributions in the Central Hardwoods and Appalachian regions
Land managers and planners may want to consider potential change in tree distributions in response climate change when managing forests. We investigated changes in the distribution of 20 tree species under alternative climate and land management scenarios in the Central Hardwoods and Appalachian Regions of the Eastern United States. We linked downscaled climate data with coupled ecosystem and landscape forest simulation models to project landscapes forward in time. We used the LINKAGES II model to predict establishment of trees based on climate (e.g. daily precipitation, temperature, solar radiation), site characteristics (e.g. landform, soils) and tree attributes, and incorporated these into the LANDIS model. We used LANDIS to simulate species birth, growth, death, regeneration, mortality, dispersal, disturbances, and management at 10-year time steps and 90m resolution. We addressed model uncertainty by bracketing simulation scenarios at the high and low end of climate change predictions by considering current climate and the PCM-B1, GFDL-A1fi, and CGCM3(T47)-A2 IPCC climate and emission scenarios.
All the climate models considered predicted warming temperatures compared to current climate but there were substantial differences in the amount and timing of precipitation. Differences among climate models and from current climate were reflected in predicted changes in the distribution and abundance of trees. In general, Southern species such as shortleaf and loblolly pine were predicted to increase under the three climate models and northern species such as sugar maple were predicted to decrease compared to current climate. Comparison among LINKAGES and LANDIS output demonstrated that while regeneration probabilities might decrease substantially for some species they can persist in the landscape for a long time because trees are long-lived, but management and disturbance can hasten species turnover. Our results can help guide forest management decisions to address how well adapted future forests are to future climate.