Incorporating topographic factors in nonlinear mixed-effects models for aboveground biomass of natural Simao pine in Yunnan,China

查看全文 作　　者：Guanglong [1]Ou;Junfeng [1]Wang;Hui [1]Xu;Keyi [1]Chen;Haimei [1]Zheng;Bo [1]Zhang;Xuelian [1]Sun;Tingting [1]Xu;Yifa [1]Xiao

机构地区：[1]Key Laboratory of Biodiversity Conservation in Southwest China of State Forest Administration, Southwest Forestry University

出　　处：《Journal of Forestry Research》索引2016年第27卷第1期,共13页

基　　金：supported by National Natural Science Foundation of China(Grant No.31160157;31560209);Application Fundamental Research Plan Project of Yunnan Province,China(Grant No.2012FD027) 摘　　要：A total of 128 Simao pine trees(Pinus kesiya var. langbianensis) from three regions of Pu'er City,Yunnan Province, People's Republic of China, were destructively sampled to obtain tree aboveground biomass(AGB). Tree variables such as diameter at breast height and total height, and topographical factors such as altitude,aspect of slope, and degree of slope were recorded. We considered the region and site quality classes as the random-effects, and the topographic variables as the fixedeffects. We fitted a total of eight models as follows: leastsquares nonlinear models(BM), least-squares nonlinear models with the topographic factors(BMT), nonlinear mixed-effects models with region as single random-effects(NLME-RE), nonlinear mixed-effects models with site as single random-effects(NLME-SE), nonlinear mixed-effects models with the two-level nested region and site random-effects(TLNLME), NLME-RE with the fixed-effects of topographic factors(NLMET-RE), NLME-SE with the fixed-effects of topographic factors(NLMET-SE), and TLNLME with the fixed-effects of topographic factors(TLNLMET). The eight models were compared by modelfitting and prediction statistics. The results showed: model fitting was improved by considering random-effects of region or site, or both. The models with the fixed-effects of topographic factors had better model fitting. According to AIC and BIC, the model fitting was ranked as TLNLME[ NLMET-RE [ NLME-RE [ NLMET-SE [ TLNLMET[ NLME-SE [ BMT [ BM. The differences among these models for model prediction were small. The model prediction was ranked as TLNLME [ NLME-RE [ NLMES E [ N L M E T- R E [ N L M E T- S E [ T L N L M E T [BMT [ BM. However, all eight models had relatively high prediction precision([90 %). Thus, the best model should be chosen based on the available data when using the model to predict individual tree AGB. 关键词：混合效应模型非线性模型地上生物量地形因子思茅松云南省固定效应模型中华人民共和国

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Incorporating topographic factors in nonlinear mixed-effects models for aboveground biomass of natural Simao pine in Yunnan,China

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