Using FIA Data to Predict Forest Understory Vegetation Structure
Understory vegetation is a significant component of terrestrial carbon stocks and play an important role in determining fuel loading and wildlife habitat. Given the paucity of regional to national-scale studies aimed at describing relationships between overstory and understory vegetation attributes and an increasing need to understand ecosystems, it is critical to evaluate the ability of such national datasets to reveal these relationships. Understory vegetation structure and its relationship with forest canopies and site conditions are important determinants of carbon stocks, wildlife habitat, and fuel loading for wildland fire assessments, and comprehensive studies are needed to better assess these relationships. One approach is to make use of preexisting forest inventory data to estimate understory vegetation height and cover from site and overstory attributes. In this study, USDA Forest Service scientists at the agency's Rocky Mountain Research Station obtained overstory, understory, and site condition data from more than 6,700 Forest Inventory and Analysis plots to assess how understory vegetation cover and height vary with overstory attributes and site characteristics for four common forest types of the western United States: lodgepole pine, Douglas-fir, ponderosa pine, and grand fir. They found that forest overstory attributes played an important role in influencing vegetation structure and corroborating much previous work demonstrating this at different scales. Models developed from this study were weak to moderate in their ability to predict understory cover and height but nonetheless suggest that predicting understory vegetation attributes to aid assessments of carbon, fuel, and wildlife habitat may be more generalizable across forests of the western U.S. using standardized national inventory data in conjunction with improved measurements. Overstory tree variables are most influential in predicting understory vegetation. Predictive models of understory cover were generally better than associated height models. Predictive models of shrub height performed best overall while forb height models were least predictive.