| 基于贝叶斯模型平均法构建杉木林分蓄积量生长模型 |
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| 引用本文： | 王震,鲁乐乐,张雄清,张建国,姜丽,段爱国. 基于贝叶斯模型平均法构建杉木林分蓄积量生长模型[J]. 林业科学研究, 2021, 34(3): 64-71. DOI: 10.13275/j.cnki.lykxyj.2021.03.007 |
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| 作者姓名： | 王震 鲁乐乐 张雄清 张建国 姜丽 段爱国 |
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| 作者单位： | 1.中国林业科学研究院林业研究所，国家林业和草原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，江苏 南京 210037 |
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| 摘 要： | 目的 探究杉木林分蓄积量变化的影响因素，为在气候变化背景下科学经营管理杉木人工林提供理论支撑。 方法 以福建邵武卫闽林场的杉木（Cunninghamia lanceolata）人工密度试验林为研究对象，分别利用贝叶斯模型平均法（BMA）和逐步回归法（SR）构建杉木林分蓄积量与林分变量因子（包括初植密度、每公顷胸高断面积、每公顷株数、平方平均胸径、林分优势高、年龄）和气候因子（包括年均气温、最热月平均温度、最冷月平均温度、年均降水量、年均湿热指数、低于0℃天数、夏季平均最高温度、冬季平均最低温度、春季平均气温）的关系模型。 结果 杉木林分蓄积量随着每公顷胸高断面积、平方平均胸径、林分优势高、年龄、夏季平均最高温、春季平均温和低于0℃天数的增加而增加，对于诸多的影响因子，SR法所确定的模型并不在BMA选出的后验概率较高的前5个模型中，模型表现出一定的不确定性，从模型后验概率角度看，SR模型精度较低。 结论 杉木林分蓄积量受到林分变量因子和气候因子的显著影响。相比于SR法，在构建杉木林分蓄积量模型方面，BMA方法考虑了模型的不确定性，模型表现更好。
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| 关 键 词： | 林分蓄积量 林分变量因子 气候因子 贝叶斯模型平均法 逐步回归法 杉木 |
| 收稿时间： | 2020-07-16 |
Stand Volume Growth Model of Chinese Fir Plantations Based on Bayesian Model Averaging Approach |
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| WANG Zhen,LU Le-le,ZHANG Xiong-qing,ZHANG Jian-guo,JIANG Li Ai-guo,DUAN Ai-guo. Stand Volume Growth Model of Chinese Fir Plantations Based on Bayesian Model Averaging Approach[J]. Forest Research, 2021, 34(3): 64-71. DOI: 10.13275/j.cnki.lykxyj.2021.03.007 |
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| Authors: | WANG Zhen LU Le-le ZHANG Xiong-qing ZHANG Jian-guo JIANG Li Ai-guo DUAN Ai-guo |
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| Affiliation: | 1. Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China;2. Collaborative Innovation Center of Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China |
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| Abstract: | Objective To study the factors influencing the stand volume change of Chinese fir (Cunninghamia lanceolata) plantations under the context of climate change. Method Based on the long term spacing trails of Chinese fir plantations established in Weimin Forest Farm, Shaowu, Fujian Province, the authors modeled the stand volume growth in relation to stand variables (including planting density, stand basal area per hectare, number of trees per hectare, stand quadratic mean diameter, dominant height, age) and climatic factors (including mean annual temperature, mean warmest month temperature, mean coldest month temperature, mean annual precipitation, annual heat-moisture index, degree-days below 0℃, summer mean maximum temperature, winter mean minimum temperature, spring (March to May) mean temperature) based on Bayesian model averaging (BMA) and stepwise regression methods (SR). Result The stand volume of Chinese fir increased with the increase of stand basal area per hectare, stand quadratic mean diameter, stand dominant height, age, summer mean maximum temperature, spring mean temperature, and Degree-days below 0℃. The model determined by SR method was not in the top five models with the highest posterior probability selected by BMA, which indicated that the model uncertainty. In view of the posterior probability of a model, SR method had lower accuracy. Conclusion The stand volume of Chinese fir plantations is significantly affected by stand and climate factors. Compared with SR method, BMA method shows a better performance because of its considering the model uncertainty. |
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| Keywords: | stand volume stand factors climate factors Bayesian model averaging stepwise regression Cunninghamia lanceolata |
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