Incorporating Climate Sensitivity for Eastern United States Tree Species into the Forest Vegetation Simulator

Abstract

Detecting climate-induced effects in forest ecosystems become increasingly important as more evidence of greenhouse-gas-related climate change were founded. The Forest Vegetation Simulator (FVS) is an important growth and yield model used to support management and planning on public forest lands over the southern United States, however its prediction accuracy was challenged due to its climate- insensitive nature. The goal of this study was to develop species-specific prediction models for eastern U.S. forest tree species with climate and soil properties as predictors in order to incorporate the effects of climate and soils-based variables on forest growth and yield into FVS-Sn. Development of climate- sensitive models for site index, individual-tree mortality and diameter increment were addressed separately, which were all developed using Random Forests on the basis of USDA Forest Service Forest Inventory and Analysis program linked to contemporary climate data and soil properties mapped in the USDA Soil Survey Geographic SSURGO database. Results showed climate was a stronger driver of site index than soils. When soils and climate were used together, site index predictions for species grouped as conifers or hardwoods were almost as precise as species-specific models for many of the most common eastern forest tree species. Model comparison was conducted to pursue the most suitable individual-tree mortality prediction model for 20 most important species among Logistic Regression, Random Forests, and Artificial Neural Networks. Results showed that Random Forests with all indicators involved generally performed well, especially sound for species with medium and high mortality. At a chosen threshold, it frequently achieved the equally highest value of sensitivity and specificity among chosen candidates. To evaluate the prediction ability of Random Forests model on individual-tree diameter increment, Multiple Linear Regression model was built as baseline on each of most common 20 species eastern U.S. area. Comparison results showed that Random Forests gained advantages in model validation and future projection under climate change. Using the developed climate-sensitive models, multiple maps were produced to illustrate how forest tree growth, yield, and mortality of individual tree may change in the eastern U.S. over the 21st century under several climate change scenarios.