Incorporating Climate Sensitivity for Southern Pine Species into the Forest Vegetation Simulator

Abstract

Growing concerns over the possible effects of greenhouse-gas-related global warming on North American forests have led to increasing calls to address climate change effects on forest vegetation in management and planning applications. The objectives of this project are to model contemporary conditions of soils and climate associated with the presence or absence and abundance of five southern pine species: shortleaf pine (Pinus echinata Mill.), slash pine (P. elliottii Engelm.), longleaf pine (P. palustris Mill.), pond pine (P. serótina Michx.), and loblolly pine (P. taeda L.). Classification and regression based Random Forest models were developed for presence-absence and abundance data, respectively. Model and diagnostics such as receiver operating curves (ROC) and variable importance plots were examined to assess model performance. Presence-absence classification models had out-of-bag error rates ranging from 6.32% to 16.06%, and areas under ROC curves ranging from 0.92-0.98. Regression models explained between 13.76% and 43.31% of variation in abundance values. Using the models based on contemporary data, predictions were made for the future years 2030, 2060, and 2090 using four different greenhouse gas emissions scenarios and three different general circulation models. Maps of future climate scenarios showed a range of potential changes in the geographic extent of the conditions consistent with current presence observations. Results of this work will be incorporated into eastern U.S. variants of the Forest Vegetation Simulator (FVS) model, similar to work that has been done for FVS variants in the West.