Zhang Xiongqing, Zhang Jianguo, Duan Aiguo
State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, P. R. China; Collaborative Innovation Center of Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, P. R. China.
PLoS One. 2015 Oct 6;10(10):e0139788. doi: 10.1371/journal.pone.0139788. eCollection 2015.
Self-thinning is a dynamic equilibrium between forest growth and mortality at full site occupancy. Parameters of the self-thinning lines are often confounded by differences across various stand and site conditions. For overcoming the problem of hierarchical and repeated measures, we used hierarchical Bayesian method to estimate the self-thinning line. The results showed that the self-thinning line for Chinese fir (Cunninghamia lanceolata (Lamb.)Hook.) plantations was not sensitive to the initial planting density. The uncertainty of model predictions was mostly due to within-subject variability. The simulation precision of hierarchical Bayesian method was better than that of stochastic frontier function (SFF). Hierarchical Bayesian method provided a reasonable explanation of the impact of other variables (site quality, soil type, aspect, etc.) on self-thinning line, which gave us the posterior distribution of parameters of self-thinning line. The research of self-thinning relationship could be benefit from the use of hierarchical Bayesian method.
自疏是指在完全占据立地条件下，森林生长与死亡率之间的动态平衡。自疏线的参数常常因不同林分和立地条件的差异而混淆。为克服分层和重复测量的问题，我们采用分层贝叶斯方法来估计自疏线。结果表明，杉木（Cunninghamia lanceolata (Lamb.)Hook.）人工林的自疏线对初始种植密度不敏感。模型预测的不确定性主要源于个体内部的变异性。分层贝叶斯方法的模拟精度优于随机前沿函数（SFF）。分层贝叶斯方法合理地解释了其他变量（立地质量、土壤类型、坡向等）对自疏线的影响，给出了自疏线参数的后验分布。自疏关系的研究可受益于分层贝叶斯方法的应用。
