摘要
Forest biomass plays a key role in the global carbon cycle. In the present study, a general allometric model was derived to predict the relationships among the stem biomass Ms, aboveground biomass MA and total biomass MT, based on previously developed scaling relationships for leaf, stem and root standing biomass. The model predicted complex scaling exponents for MT and/or MA with respect to Ms. Because annual biomass accumulation in the stem, root and branch far exceeded the annual increase in standing leaf biomass, we can predict that MT ∝MA ∝ Ms as a simple result of the model. Although slight variations existed in different phyletic affiliations （i.e. conifers versus angiosperms）, empirical results using Model Type Ⅱ （reduced major axis） regression supported the model＇s predictions. The predictive formulas among stem, aboveground and total biomass were obtained using Model Type I （ordinary least squares） regression to estimate forest biomass. Given the low mean percentage prediction errors for aboveground （and total biomass） based on the stem biomass, the results provided a reasonable method to estimate the biomass of forests at the individual level, which was insensitive to the variation in local environmental conditions （e.g. precipitation, temperature, etc.）.
Forest biomass plays a key role in the global carbon cycle. In the present study, a general allometric model was derived to predict the relationships among the stem biomass Ms, aboveground biomass MA and total biomass MT, based on previously developed scaling relationships for leaf, stem and root standing biomass. The model predicted complex scaling exponents for MT and/or MA with respect to Ms. Because annual biomass accumulation in the stem, root and branch far exceeded the annual increase in standing leaf biomass, we can predict that MT ∝MA ∝ Ms as a simple result of the model. Although slight variations existed in different phyletic affiliations （i.e. conifers versus angiosperms）, empirical results using Model Type Ⅱ （reduced major axis） regression supported the model＇s predictions. The predictive formulas among stem, aboveground and total biomass were obtained using Model Type I （ordinary least squares） regression to estimate forest biomass. Given the low mean percentage prediction errors for aboveground （and total biomass） based on the stem biomass, the results provided a reasonable method to estimate the biomass of forests at the individual level, which was insensitive to the variation in local environmental conditions （e.g. precipitation, temperature, etc.）.
基金
The authors acknowledge the support of the National Natural Science Foundation of China(90 102 015,30 170 161)
the department of science and technology of Fujian province(No.2004 N010,2005 NZ1010).
