Monday, August 4, 2008
Exhibit Hall CD, Midwest Airlines Center
Background/Question/Methods Fine root turnover is critical to carbon (C) dynamics in terrestrial ecosystems. However, to observe fine roots is difficult, thus constraining field studies of patterns and controls of this major vegetation-to-soil pathway of C and nutrient transfer. A simple statistical model was developed to simulate fine root populations based on key factors (e.g., root architecture, biomass, longevity) so that difficult, and sometimes controversial, questions about root turnover estimates may be resolved with statistical rigor. Recognizing importance of heterogeneity in fine root populations, the model treated root branching order as its primary controlling factor, and used probability distributions to determine longevity and biomass of fine roots within orders. Model simulations were controlled by three allometric scaling functions (i.e., root number, biomass, longevity), which were based on either empirical data of root characteristics or fractal relationships across orders. From simulated data, the model calculated different measures of root longevity and turnover (e.g., median longevity, mean C residence time, biomass mortality). Specific objectives were to: (1) test the model against observed data, (2) perform sensitivity and uncertainty analyses of model behaviors, and (3) address questions such as which factor is the most critical to errors in various measures of root longevity and turnover.
Results/Conclusions Results indicated that the model was adequate in terms of generating realistic root population data and helping unravel patterns of fine root longevity and turnover. In addition, the sensitivity and uncertainty analyses showed that fine root heterogeneity and turnover calculation methods were the most critical factors to errors in longevity and turnover estimates. The model provides an essential platform on which different approaches, methods, and turnover estimates can be evaluated and compared with the same root population data.
Results/Conclusions Results indicated that the model was adequate in terms of generating realistic root population data and helping unravel patterns of fine root longevity and turnover. In addition, the sensitivity and uncertainty analyses showed that fine root heterogeneity and turnover calculation methods were the most critical factors to errors in longevity and turnover estimates. The model provides an essential platform on which different approaches, methods, and turnover estimates can be evaluated and compared with the same root population data.
