Building mixed effect models of stand biomass for Simao pine (Pinus kesiya var. langbianensis) natural forest

In this paper we took natural Simao pine (Pinus kesiya var. langbianensis) forest as the research object, and investigated the aboveground, root and total biomass of 45 plots of at three typical sites (Tongguan town of Mojiang County, Yunxian town of Simao District, and Nuofu town of Lancang County) in Pu’er City, Yunnan Province. Firstly, we chose the best power function to the basic model. Secondly, considering random effect of the regional effect we constructed the mixed effects models of the biomass components of stand using technology of mixed effects models, and analyzed the variance and covariance structures of the models. Finally, based on the basic mixed effects models of the components, We constructed the mixed effects models including fixed effects from three types of environmental factors (including stand, topographic and climate factors) respectively. The models were evaluated by fitting and independence test indices. The fitting indices include logLik, Akaike information criterion(AIC) and Bayesian information criterion (BIC), and the test indices include sum relative error (SRE), mean relative error (MRE), absolute mean relative error (AMRE) and prediction precision (p). The results showed: (1) For the models fitting, the mixed model considering random effect of regional effect were significantly better than the ordinary models, and the mixed models including the fixed effect of environmental factors were better than the ordinary mixed models. Among the models including the fixed effects form three types of environmental factors, the models including topographic factors were the best models because of the lowest values for AIC and BIC. (2) For the independence test of models, except for the mixed models of stand root biomass including the fixed effects of topographic factors, the other mixed models were better than the ordinary models. Compared the mixed models including the fixed effect of environmental factors and the ordinary mixed models, the performance were different for three components, but for each component the differences among the models were small. (3) The best model for root biomass of stand was the mixed effects models only considering the random effect of regional effect, but for the other components (including aboveground biomass and the total biomass of stand), the best models were both the mixed effects models including the fixed effects of topographic factors and random effect of regional effect.