东北地区两个主要树种地上生物量通用方程构建

目前,东北落叶松地上生物量方程主要采用分树种或把不同树种归为一体的方法,但是,既能反映落叶松生物量与自变量的平均关系,又能反映不同树种间生物量差异程度的通用性落叶松生物量方程迄今尚未构建。因此,以东北地区兴安落叶松和长白落叶松地上生物量数据为研究对象,构建一元(自变量为胸径)、二元(自变量为胸径和树高)和三元(自变量为胸径、树高和冠幅)的不同树种生物量通用方程。由于起源和地域的不同,生物量可能会存在一定程度差异,进而,在所构建的不同树种生物量通用方程的基础上,考虑起源和地域的差异,利用哑变量方法构建既能考虑不同树种又能考虑林分起源和不同地域的东北落叶松地上生物量通用方程,并利用加权最小二乘法剔除方程异方差。结果表明:通过哑变量方法构建不同树种生物量模型方法可行;不论是传统的生物量方程,还是只考虑树种或同时考虑树种、起源和地域的通用生物量方程,增加自变量能提高方程预测效果,即,三元生物量方程预测精度最高,二元生物量方程次之,一元生物量方程最低;当同时考虑树种、起源和地域时,方程预测精度最高,只考虑树种的生物量通用方程次之,传统生物量方程最低。因此,如果数据允许,建议构建考虑不同树种、起源和地域的三元生物量方程估计东北地区长白落叶松和兴安落叶松地上生物量。

Generalized above-ground biomass equations for two main species in northeast China

Individual tree biomass equations have been frequently used in ecological and forestry research over the last 60 years. They represent a powerful tool to understand forest productivity, nutrient cycling, and carbon sequestration, and they are used to estimate other structural and functional characteristics of forest ecosystems. Current attempts to develop above-ground biomass equations for Larix forests in northeast China have been mainly focused on only one species or applied to the genus Larix as a whole. However, generalized above-ground biomass equations for Larix could be used to estimate the average relationship between above-ground biomass and different independent variables and also variations among different Larix species. We developed generalized biomass equations for different Larix species by using Larix olgensis and Larix gmelinii. In this study, a total of nine tree variables that were able to predict above-ground biomass in Larix species were examined using biomass equations. The results show that D, H, and CW contributed significantly to predict above-ground biomass. Therefore, three combinations of these variables, including D alone; D and H; and D, H, and CW, were selected as independent variables to develop univariate, bivariate, and trivariate biomass generalized equations, respectively. The trivariate biomass generalized equations predicted above-ground biomass better than the other two equation types, while the predictive power of the univariate equation was the worse than the rest. Theoretically, the prediction accuracy of trivariate biomass equations could be further increased by adding stands or tree variables; however, including an excessive number of parameters in the biomass equations may hinder computation convergence and reduce the speed required to estimate model parameters. Furthermore, including many stands or tree variables would increase the cost and time required to conduct forest inventories. Therefore, determining the appropriate number of independent variables able to provide the level of accuracy required by forest managers is essential in forest modeling. A parsimonious model with reliable accuracy of prediction has been suggested as a reasonable approach for efficient forest management. For this reason, D, H, and CW were finally selected as independent variables for the generalized biomass equations developed in this study. In general, the biomass of individual trees with the same D would depend on the region studied and the origin of the tree. Thus, the generalized above-ground biomass equations developed for different Larix species in northeast China consider this inter-regional variation by using a dummy variable. To reduce heteroskedasticity in the data, we used weighted least square regressions. The results showed that the predictive precision of the biomass equations could be improved by adding predictor variables. Regardless of the traditional biomass equation used, both generalized equations considering only tree species and those considering tree species, tree origin, and region showed the highest prediction power. In addition, the accuracy for predicting above-ground biomass did not differ among univariate, bivariate, and trivariate equations when tree species, tree origin, and region were considered. On the basis of these results, the trivariate generalized biomass equation that considers tree species, tree origin, and region was believed to be the best option for estimating the biomass of L. olgensis and L. gmelinii.