基于Cox比例风险函数及混合效应模型方法的落叶松云冷杉林木枯损模拟研究
The mortality model of Mixed Stands of Larix olgensis, Abies nephrolepis, and Picea jazoensis based on Cox proportional hazard function and mixed effects models method
投稿时间:2019-07-03  修订日期:2019-08-22
DOI:
中文关键词:  生存分析方法  混合效应模型  枯损  Cox比例风险函数  风险率
英文关键词:the survival analysis  mixed effects model  mortality  Cox proportional hazard function  hazard ratio
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
作者单位E-mail
李春明 中国林业科学研究院资源信息研究所 lichunm@ifrit.ac.cn 
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中文摘要:
      [目的] 林木的枯损是森林生态系统中十分重要的组成部分。对其准确预测具有重要意义。传统的模型方法只能预测枯损是否发生,无法处理数据中存在的删失情况及时间依赖协变量。生存分析方法是唯一能够处理非正态分布数据,并且考虑删失数据和时间协变量的方法。[方法] 以吉林省汪清林业局20块落叶松云冷杉林样地数据为例,基于生存分析方法中的Cox比例风险函数模型方法,把林分因子和立地因子作为协变量加入到模型中去,构建林木的枯损及生存模型,并在此基础上考虑样地的随机效应。[结果] 研究结果表明,Cox比例风险函数模型在描述林木枯损时,具有很好的适应性。单木初始胸径与林木的风险函数呈反比,与生存率呈正比;大于对象木断面积与风险函数呈正比,与生存率呈反比;初始林分公顷株数与风险函数呈正比,与生存率呈反比;立地因子对林木的枯损及生存没有显著影响。与固定效应模型相比,Cox比例风险函数模型在考虑了样地水平的随机效应后,模型的模拟精度获得明显的提高,并且达到极显著程度。[结论] 在森林经营中,Cox比例风险函数模型的使用,可为森林经营者在确定合理的经营密度上提供很好的科学依据。
英文摘要:
      [Objective] The trees mortality is a very important part of forest ecology system. It is great significance to predict accurately of mortality. The conventional model can only predict whether tree mortality occurs or not, and can’t deal with the censored data or time-dependent covariate variables. Survival analysis method is the only way to deal with non-normal distribution data by considering censored data and time-dependent covariate variables. [Method] Taking 20 plots data of Mixed Stands of Larix olgensis, Abies nephrolepis, and Picea jazoensis in Wangqing Forestry Bureau of Jilin Province as an example, the tree mortality and survival model was constructed based on Cox proportional hazard function of survival analysis method, stand factor and site factor was added into the model as covariates. The plot’s random effect was considered and compared with the simulation effect of the traditional model. [Result] The result showed Cox proportional hazard function model has goodness of fit describing tree mortality. The initial DBH of tree was negatively correlated with the hazard function of the tree, and positively correlated with survival rate; the BAL was positively correlated with the hazard function of the tree, and negatively correlated with survival rate; initial stand density per hectare was positively correlated with the hazard function of the tree, and negatively correlated with survival rate. Compared with the fixed effect model, the accuracy of the Cox proportional hazard function model was greatly improved after considering plot’s random effect. [Conclusion] In forest management, the Cox proportional hazard function model provided a good scientific basis for determining reasonable management density for forest operators.
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