基于线性分位数混合效应的辽东山区红松冠幅模型  被引量:7

Crown width model for planted Korean pine in eastern Liaoning mountains based on mixed effect linear quantile

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作  者:佟艺玟 陈东升[2] 冯健[3] 高慧淋 TONG Yi-wen;CHEN Dong-sheng;FENG Jian;GAO Hui-lin(College of Forestry,Shenyang Agricultural University,Shenyang 110866,China;Research Institute of Forestry,Chinese Academy of Forestry,Beijing 100091,China;Liaoning Academy of Forestry Sciences,Shenyang 110032,China)

机构地区:[1]沈阳农业大学林学院,沈阳110866 [2]中国林业科学研究院林业研究所,北京100091 [3]辽宁省林业科学研究院,沈阳110032

出  处:《应用生态学报》2022年第9期2321-2330,共10页Chinese Journal of Applied Ecology

基  金:辽宁省教育厅一般项目(LSNQN201901);沈阳农业大学博士启动基金项目(880418014)资助。

摘  要:冠幅是反映单木生长状态及构建林木生长收获模型的重要变量。本研究以辽东山区大边沟林场10~55年生红松人工林为对象,基于66块固定样地的2763株红松的每木检尺数据,选取冠幅基础模型,采用再参数化的方法引入单木竞争指标(R_(d)),利用哑变量的方法引入了林分密度、林层变量,构建不同分位点(0.50、0.90、0.93、0.95、0.96、0.99)的冠幅分位数回归模型,并与传统方法进行比较,选取模拟林分最大冠幅的最优分位点。为反映林分中单木冠幅在林木个体之间的差异,建立了基于样地水平的最优分位点的线性混合效应分位数回归冠幅模型,分析各变量对单木冠幅的影响。结果表明:基于F统计检验,不同林分密度和林层的冠幅模型具有显著差异,在基础模型中引入林层、林分密度和竞争后,模型R_(a)^(2)提高0.0104,均方根误差降低0.0115,均方误差降低为7.4%;与最小二乘法比较,分位数回归模型能够较好地模拟林分状态下的单木最大冠幅,并选出0.96分位点和0.93分位点作为上林层和下林层的分位数回归模型的最优分位点。引入混合效应的线性分位数回归模型的赤池信息准则、贝叶斯信息准则、HQ信息准则等评价指标优于传统分位数回归,参数标准误显著降低,混合效应的引入很好地解释了样地之间的差异。就上林层和下林层而言,林分密度越大,最大冠幅越小;相对直径越大,最大冠幅越大,其中林分密度对下林层的冠幅影响大于上林层,当林分密度足够大时,冠幅随着胸径的增大先增大后降低。本研究构建的基于混合效应的分位数回归模型能有效提高模型的拟合优度,今后可通过调控林分密度、适度抚育间伐等措施,实现对辽东山区红松人工林的科学营建和可持续发展。Crown width is a critical variable in reflecting the individual tree growth status and in developing forest growth and yield models.With the crown width base model as reference,we developed the crown width quantile regression models for different quantiles(0.50,0.90,0.93,0.95,0.96,0.99)based on the data of 2763 Korean pines in 66 permanent plots from the 10-55 years old plantations in Dabiangou forest farm,mountainous areas of eastern Liaoning Province.We used the reparameterization method by introducing the single tree competition index(R_(d))and used the dummy variable method by introducing stand density and forest layer variables.We then selected optimal quantile of maximum crown width in the stand by comparing our model developed routine to the traditional methods.The final crown width linear mixed effect quantile regression model was developed based on the optimal quantile at the plot level.The influence of each variable on crown width was analyzed to reflect the difference of crown width among individual trees in the stand.The models with different stand densities and forest layers had significant difference based on F statistical test:the R_(a)^(2)of the model increased by 0.0104,the root mean square error decreased by 0.0115 and the mean square error reduction was 7.4%,after the variables of forest layer,forest density,and competition being incorporated into the basic model.The developed quantile regression model performed better than that of the ordinary least square method in simulating the maximum crown width of a single tree in the forest stand.The selected best quantile of the quantile regression model for the upper forest layer and lower forest layer was 0.96 and 0.93,respectively.The linear quantile regression model with the mixed effect was superior to the traditional quantile regression model in Akaike,Bayesion and HQ information criterion and other evaluation para-meters,the standard error for the parameters of estimates was significantly reduced,and the introduced mixed effect well explained di

关 键 词:红松 再参数化 冠幅模型 分位数回归 混合效应 

分 类 号:S791.247[农业科学—林木遗传育种]

 

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