南京林业大学学报（自然科学版）2017，Vol.41Issue(4)：121-128,8.DOI:10.3969/j.issn.1000-2006.201604066
基于GLMM的人工林红松二级枝条分布数量模拟
Modelling the vertical variation in the number of second order branches of Pinus koraiensis plantation trees through GLMM
摘要
Abstract
[Objective] Establish a method for estimating the spatial distribution of branch and foliage biomass within individual Korean pine (Pinus koraiensis) crowns,the aim of the present study was to develop a predictive model for the vertical variation in number of second-order branches in farmed Korean pines.[Method] Using count data from a total of 955 branches sampled from 65 Korean pines in the Mengjiagang Forest Farm,the number of second-order branches was modeled as a function of the relative distance into the crown (RDINc),crown length (CL),diameter (DBH) and height/diameter ratio (HDR),based on a previously developed model.Subject-specific variation was captured using treelevel random coefficients,and the auto correlation among the branches sampled in consecutive whorls of the same crown were taken into account using a first-order auto regressive correlation structure AR (1) in the generalized linear mixed models.The predictive accuracy of the random-coefficient models were compared with that of the fixed-effects model using common methods for validating forest models.[Result] All of the converged models with random coefficients provided better fits than the fixed-effect model,and the model with four random coefficients (intercept,lnRDINC,R2DINC and CL)and the first-order auto regressive correlation structure AR (1) proved to be the optimum mixed model.In the fixed-effect part of this model,the parameter estimates for lnRDIC,CL and DBH were positive,whereas those for R2DINC and HDR were negative.Consequently there was a peak in the number of predicted second-order branches as RDINC increased.The Pseudo-R2,RMSE,MAE and MAE％ of the optimal model were 0.896 1,5.15,3.83,and 23.25％,respectively.[Conclusion]The generalized linear mixed models with random coefficients had greater precision than the previously developed fixedeffect model since they delineated both the mean trend of vertical variation in number of second-order branches and treespecific deviation from the mean trend.关键词
红松/二级枝条数量/Poisson回归模型/广义线性混合模型Key words
Korean pine (Pinus koraiensis)/number of second order branches/Poisson regression model/generalized linear mixed model(GLMM)分类
农业科技引用本文复制引用
苗铮,董利虎,李凤日,白东雪,王佳慧..基于GLMM的人工林红松二级枝条分布数量模拟[J].南京林业大学学报（自然科学版）,2017,41(4):121-128,8.基金项目
国家自然科学基金项目(31570626) （31570626）
国家级大学生创新创业训练计划项目(201410225057) （201410225057）
