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林业科学 ›› 2016, Vol. 52 ›› Issue (9): 95-102.doi: 10.11707/j.1001-7488.20160911

• 论文与研究报告 • 上一篇    下一篇

基于分层贝叶斯法的杉木人工林最大密度线

崔令军1,2, 张雄清1,2, 段爱国1,2, 张建国1,2   

  1. 1. 中国林业科学研究院林业研究所 国家林业局林木培育重点实验室 北京 100091;
    2. 南京林业大学南方现代林业协同创新中心 南京 210037
  • 收稿日期:2015-04-23 修回日期:2016-06-02 出版日期:2016-09-25 发布日期:2016-10-20
  • 通讯作者: 张建国
  • 基金资助:
    “十二五”国家科技支撑课题杉木专题(2015BAD09B0101);国家自然科学基金项目(31300537);中国林业科学研究院科研院所基本科研业务资金项目(CAFYBB2014QB002)。

A Hierarchical Bayesian Model to Predict Maximum-Size Density Line for Chinese Fir Plantation in Southern China

Cui Lingjun1,2, Zhang Xiongqing1,2, Duan Aiguo1,2, Zhang Jianguo1,2   

  1. 1. Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration Research Institute of Forestry, CAF Beijing 100091;
    2. Collaborative Innovation Center of Sustainable Forestry in Southern China, Nanjing Forestry University Nanjing 210037
  • Received:2015-04-23 Revised:2016-06-02 Online:2016-09-25 Published:2016-10-20

摘要: [目的] 利用分层贝叶斯法分析杉木最大密度线,以期为杉木自然稀疏规律研究提供一种新思路。[方法] 以福建杉木密度试验林为研究对象,根据林分平方平均直径与每公顷株数的关系,构建最大密度线方程。为分析初植密度对杉木最大密度线的影响,构建3种分层贝叶斯模型:1)在截距处考虑初植密度的随机效应,而斜率作为固定效应不变;2)在斜率处考虑初植密度的随机效应,而截距作为固定效应不变;3)同时在截距和斜率处考虑随机效应。[结果] 分层贝叶斯方法(R2=0.867 8)优于普通贝叶斯方法(R2=0.859 3),但是初植密度的随机效应不管是在截距上(σ02=0.008,SD=0.029)还是在斜率上(σ12=0.003,SD=0.016),其t值都小于1.96,差异不显著,由此得到初植密度不会影响杉木最大密度线。最大密度线估计的不确定性主要来源于相同初植密度内不同样地和重复测量所产生的不确定性。[结论] 研究基于分层贝叶斯法给出了最大密度线的斜率的后验概率分布图,能够更有效地反映出最大密度线的不确定性,也更合理,可为杉木密度试验管理提供参考依据。

关键词: 分层贝叶斯法, 杉木人工林, 最大密度线, 初植密度

Abstract: [Objective] Self-thinning line is an important curve for describing the tree death in a certain stand. The objective of this study was to provide a new idea for exploring self-thinning law using hierarchical Bayesian method based on the Chinese fir (Cunninghamia lanceolata) data with different initial densities in Fujian Province.[Method] Firstly, the maximum-size density equation was established according to the relationship between stand quadratic mean diameter and number of trees per hectare. For analyzing the effect of initial density on maximum-size density line, we introduced the hierarchical Bayesian models: 1) random effect on the intercept; 2) random effect on the slope; 3) random effects on the intercept and slope. [Result] The results showed that the hierarchical Bayesian models (R2=0.867 8) were better than non-hierarchical Bayesian model (R2=0.859 3). The random effects of initial density were not significant neither on the intercept (σ02=0.008,SD=0.029) nor on the slope (σ12=0.003,SD=0.016). In addition, the uncertainty of model predictions was mostly due to within-subject variability.[Conclusion] Hierarchical Bayesian method provided a reasonable explanation of the impact of other variables on maximum-size density line, which gave us the posterior distribution of parameters of maximum-size density line. The research of maximum-size density line could be benefit from the use of hierarchical Bayesian method and helpful for managing Chinese fir plantations.

Key words: hierarchical Bayesian method, Chinese fir plantation, maximum-size density line, initial planting density

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