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林业科学 ›› 2013, Vol. 49 ›› Issue (9): 1-7.

• 论文与研究报告 • 上一篇    下一篇

浙江毛竹林分非空间结构特征及其动态变化

刘恩斌1,2, 施拥军1,2, 李永夫1,2, 周国模1,2, 杨东3   

  1. 1. 浙江农林大学 浙江省森林生态系统碳循环与固碳减排重点实验室 临安 311300;
    2. 浙江农林大学环境与资源学院 临安 311300;
    3. 南京林业大学材料科学与工程学院 南京 210037
  • 收稿日期:2012-08-15 修回日期:2013-07-19 出版日期:2013-09-25 发布日期:2013-09-19
  • 通讯作者: 周国模
  • 基金资助:

    浙江省重点科技创新团队项目(2012R10023-02);国家自然科学基金(30972356;30900190;31170595);浙江省森林生态系统碳循环与固碳减排重点实验室开放基金(KFJJ2012001)。

Non Spatial Structural Characteristic of Moso Bamboo Forest and Its Dynamics in Zhejiang Province

Liu Enbin1,2, Shi Yongjun1,2, Li Yongfu1,2, Zhou Guomo1,2, Yang Dong3   

  1. 1. Zhejiang A&F University Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration Lin'an 311300;
    2. School of Environmental and Resource Sciences, Zhejiang A&F University Lin'an 311300;
    3. College of Materials Science and Engineering, Nanjing Forestry University Nanjing 210037
  • Received:2012-08-15 Revised:2013-07-19 Online:2013-09-25 Published:2013-09-19

摘要:

利用2004与2009年浙江省两期森林连续清查毛竹样地数据,应用因子分析、聚类分析、相关分析、多元方差分析及核密度估计方法,采用7个指标(样地生物量,样地株数,林分平均胸径,1,2,3和≥4度竹所占比例)描述毛竹林分非空间结构特征。结果表明:毛竹林分非空间结构特征的主导因素依次为年龄结构因子、样地生物量因子、林分平均胸径因子;浙江现有毛竹林老龄化问题较突出,林分中各龄级所占比极不合理,现有经营措施存在较大缺陷,从而使林分生产力衰退;样地株数较少时,林分平均胸径与样地株数几乎没有相关性,但随着样地毛竹株数的增加,表现出一定的负指数关系;影响毛竹林分生物量的主要因素依次是样地株数与林分平均胸径;7个指标中除3度竹所占比例没有显著差异外,其他指标都存在显著差异,使得毛竹林分非空间结构(由7个指标构成)在2004年与2009年存在显著性差异;平均胸径小于8.7cm的林分随着毛竹的生长所占比重在减少,而平均胸径大于8.7cm的林分在增加,这也说明2009年林分平均胸径比2004年显著增大,样地株数和林分生物量与林分平均胸径有类似的趋势。研究结果对毛竹林的可持续经营与固碳功能的提高有一定的参考价值。

关键词: 毛竹, 林分非空间结构, 多元统计分析, 显著性差异

Abstract:

Study on the non spatial structural characteristic and its dynamic change in moso bamboo (Phyllostachys edulis) forests would have important theoretical and practical significance for sustainably managing bamboo forest, understanding bamboo forest carbon sequestration capacity and improving forest productivity. In the present study, seven parameters, including biomass, number of trees, average diameter at breast height (DBH) of culms, proportions of 1 du, 2 du, 3 du, and ≥4 du moso bamboos, were used to describe the non spatial structural characteristic of moso bamboo forests. Factor analysis, cluster analysis, correlation analysis, multivariate analysis of variance, and kernel density estimation were conducted with the data collected from two consecutive inventories in bamboo plots. Results showed that: Age structure, plot biomass, average diameter of stands were the dominant factors of non spatial structural characteristic in moso bamboo forests.The aging of moso bamboo forests was a severe problem in Zhejiang Province. The proportions of different-age forests were unreasonable and there was drawback in the present management practices, which caused the recession of stand productivity. There was no relationship between average DBH of forests and number of culms when the number of culms in a plot was small, while a negative exponential relationship was observed between the above-mentioned two parameters with the increasing of number of culms.The number of culms and average stand DBH were the two main factors affecting biomass of moso bamboo forests. There were significant differences in the all parameters related to the non spatial structural characteristic except for the proportion of 3 du moso bamboo between the two inventories, thus there was a significant difference in non spatial structural characteristic of forests described with the two inventory data.The proportion of stands with average diameter of less than 8.7 cm was found to be decreased with the growth of moso bamboo, while the proportion of stands with average diameter of more than 8.7 cm increased, indicating that the average diameter of moso bambo forests in 2009 was significant greater than that in 2004. There were similar trends in number of culms in the plot and biomass of forests. The results would provide a certain reference value for the sustainable management of moso bamboo forests and improvement of carbon sequestration.

Key words: Phyllostachys edulis, non spatial structure of forests, multivariate statistical analysis, significant difference

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