臭冷杉生物量分配格局及异速生长模型

摘 要：臭冷杉是长白山阔叶红松林中重要针叶树种，采用整株收获法分析21株臭冷杉地上地下生物量分配格局。在枝条水平上采用样枝直径（BD）、样枝长度（BL）、样枝所在轮生枝位置（WP）建立活枝、针叶生物量异速生长模型，在植株水平上采用胸径（DBH）、树高（H）、年龄（Age）、树冠长度（CL）、树冠比率（CR）、南北向冠幅（CW1）、东西向冠幅（CW2）等变量建立树干木质、树皮、活枝、针叶、粗根及整株生物量模型。并利用逐步线性回归法获得不同器官生物量最优模型。结果表明：（1）活枝生物量主要集中在树冠中下层，针叶生物量集中在树冠中层。树冠中层和下层枝叶生物量无显著差异（p>0.05）；（2）21株臭冷杉地上生物量和地下生物量变动范围分别为1.026–506.047 kg/株和0.241–112.000 kg/株。粗根、活枝、针叶、树干木质、树皮及枯枝生物量占整株生物量的相对比例分别为18.68%、18.39%、12.02%、39.29%、8.70%和2.92%；（3）地上生物量与地下生物量呈显著线性相关（p<0.001），拟合线性方程斜率为0.23；（4）枝条水平上，活枝生物量模型解释量超过95%，平均预测误差小于30%。与单变量（BD）活枝生物量模型相比，2变量（BD、BL）和3变量（BD、BL、WP）模型解释量分别提高1.2%和2.0%，平均预测误差分别下降6.26%和9.27%。针叶生物量相对较难预测，模型解释量仅为82.7%，平均预测误差接近50%，模型中增加BL 和WP变量并未提高针叶生物量的预测精度。活枝生物量与BD、BL、WP正相关，针叶生物量与BD正相关，与BL、WP负相关；（5）植株水平上，基于胸径的单变量模型可解释量大于90%，增加树高变量未能显著提高生物量模型的预测精度。年龄决定了臭冷杉的树干生物量，忽视年龄变量将会产生生物量预测误差。树冠特征是影响枝叶生物量预测精度的重要变量。综合考虑模型的可解释量及回归系数显著性可知，胸径是预测臭冷杉不同器官生物量的可靠变量。

Biomass allocation patterns and allometric models of Abies nephrolepis Maxim

Abies nephrolepis (Maxim) is an important coniferous tree species in the mixed broadleaved-Korean pine forests in the Changbai Mountain. In this study, the above- and belowground biomass allocation patterns of A. nephrolepis were analyzed using 21 harvested trees with different diameters at the breast height (DBH). At the branch level, allometric models for live branch and needle biomass were developed based on independent variables of branch diameter (BD), branch length (BL), and whorl position (WP). At the whole tree level, independent variables including DBH, tree height (H), tree age (Age), crown length (CL), crown ratio (CR), south-north crown width (CW1), and east-west crown width (CW2) were used to develop allometric models for biomass components of stem wood, bark, live branches, needle, coarse roots, and the whole tree. The best fitting models were identified by stepwise regression method. Results show that majority live branch biomass occurred in the middle and lower canopy layers, while the needle biomass was mostly allocated in the middle layer of the tree crown, with no significant difference between the middle and lower layers in the combined biomass of live branches and needle (P>0.05). The aboveground biomass and belowground biomass were 1.026-506.047 kg/tree and 0.241-112.000 kg/tree, respectively. Relative proportions of coarse roots, live branches, needle, stem wood, bark, and dead branches to total tree biomass were 18.68%, 18.39%, 12.02%, 39.29%, 8.70%, and 2.92%, respectively. There was a significant linear relationship between the aboveground biomass and belowground biomass (P<0.001). Slope of the fitted linear model was 0.23. At the branch level, allometric models of the live branch biomass explained more than 95% of the variations in data and the mean prediction error was less than 30%. Models based on two (i.e. BD and BL) or three variables (i.e. BD, BL, and WP) were better than the single-variable (i.e. BD) model, with the variability explainable increasing by 1.2% and 2.0% and the mean prediction error decreasing by 6.26% and 9.27%, respectively. Needle biomass was more difficult to estimate than the biomass of live branches. Allometric models of needle biomass explained only 82.7% of the data variability, with the mean prediction error reaching 50%. Compared with the allometric model based on a single variable, prediction accuracy improved little when including BL and WP variables for the needle biomass. The live branch biomass was positively related with BD, BL, and WP; whereas needle biomass was positively related with BD and negatively with BL and WP. At the tree level, the biomass allometric models based on DBH explained more than 90% of the data variability. Inclusion of tree height did not always improve biomass estimation. Stem biomass was age-related and biomass estimation without considering tree age could be slightly biased. Crown variables were very important to accurately estimate the biomass of live branches and needle. Considering the variability explainable and the significance of regression coefficient in allometric models, it can be concluded that DBH is a reliable predictor for estimating above- and belowground biomass in A. nephrolepis.