Climate- and soil-based models of site productivity in eastern US tree species

Abstract

As concerns rise over potential effects of greenhouse gas related climate change on terrestrial ecosystems, forest managers require growth and yield modeling capabilities responsive to changing climate conditions. Our goal was to develop prediction models of site index for eastern US forest tree species with climate and soil properties as predictors for use in predicting potential responses of forest productivity to climate change. Species-specific site index data from the USDA Forest Service Forest Inventory and Analysis (FIA) program were linked to contemporary climate data and soil properties mapped in the USDA Soil Survey Geographic (SSURGO) database. Random forest regression tree based ensemble prediction models of site index were constructed based on 37 climate-related and 15 soil attributes. In addition to a species-specific site index, aggregate models were developed for species grouped into two broad categories: conifer (softwood) and hardwood (broadleaved) species groups. Species-specific models based on climate and soil predictors explained the most variation in site index of any models tested (R2= 62.5%, RMSE = 3.2 m). Comparable results were found when grouping species into conifer and hardwood groups (R2= 63.9%, RMSE = 4.6 m for conifers; R2= 35.9%, RMSE = 4.2 m for hardwoods). Model predictions based on multiple global circulation models (GCMs) and Intergovernmental Panel on Climate Change (IPCC) development scenarios were tested for statistical significance using bootstrap resampling methods. Results showed significant increases over the 21st century in mean site index for conifers between +0.5 and +2.4 m. Over the same time period, mean hardwood site index showed decreases of as much as −1.7 m for the scenarios tested. The results demonstrate the utility of using climate and soils data in predicting site index across a large geographic region, and the potential of climate change to alter forest productivity in the eastern US. Additional investigation is needed to interpret spatial patterns and ecological relationships related to predictions from this type of model.