利用线性混合效应模型模拟杉木人工林枝条生物量

基于福建省将乐林场45株人工杉木解析木的572组枝条生物量数据，采用线性混合效应模型方法，建立杉木人工林枝条总生物量和枝、叶生物量的预测模型，并利用独立样本数据对模型进行检验.结果表明： 线性混合效应模型比传统多元线性回归模型的拟合精度高.不同随机效应参数的组合，其混合模型的精度不同.考虑异方差结构的混合模型能够消除数据间的异方差性，其精度更高，其中，对于枝条总生物量和叶生物量模型，以指数函数作为异方差结构时的模型精度最高；对于枝生物量模型，以常数加幂函数作为异方差结构时的模型精度最高.模型检验结果表明：对于杉木人工林枝条生物量预测模型，考虑随机效应和异方差结构的线性混合模型的检验精度比传统多元线性回归模型的精度有明显提高.

Simulation of the branch biomass for Chinese fir plantation using the linear mixed effects model.

Based on data obtained from 572 branches of 45 Chinese fir trees in Jiangle Forest Farm, Fujian Province, southeast China, prediction models for branch, foliage biomass and total branch and foliage biomass of individual tree were developed by linear mixed effects (LME) method, and tested by independent samples. The results showed that the LME models provided better performance than the multiple linear regression models for the branch, foliage and total biomass prediction of Chinese fir plantation. The LME models with different combinations of the random effects parameters had different fitting precisions. The LME models including variance structures could effectively remove the heteroscedasticity in the data and improved the precision. The LME model with the exponential function as the variance structure had better fitting precisions for the total biomass and foliage biomass models, and that with the constant plus power function as the variance structure had better performance for the branch biomass model. Model validation confirmed that the LME models with the random effects and heteroscedasticity structure could significantly improve the precision of prediction, compared to the multiple linear regression models.