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    樊艳文, 王襄平, 曾令兵, 武娴. 北京栓皮栎林胸径-树高相关生长关系的分析[J]. 北京林业大学学报, 2011, 33(6): 146-150.
    引用本文: 樊艳文, 王襄平, 曾令兵, 武娴. 北京栓皮栎林胸径-树高相关生长关系的分析[J]. 北京林业大学学报, 2011, 33(6): 146-150.
    FAN Yan-wen, WANG Xiang-ping, ZENG Ling-bing, WU Xian. Allometric relationship between diameter at breast height and height of Quercus variabilis plantations in Beijing.[J]. Journal of Beijing Forestry University, 2011, 33(6): 146-150.
    Citation: FAN Yan-wen, WANG Xiang-ping, ZENG Ling-bing, WU Xian. Allometric relationship between diameter at breast height and height of Quercus variabilis plantations in Beijing.[J]. Journal of Beijing Forestry University, 2011, 33(6): 146-150.

    北京栓皮栎林胸径-树高相关生长关系的分析

    Allometric relationship between diameter at breast height and height of Quercus variabilis plantations in Beijing.

    • 摘要: 基于幂函数H=aDb对北京市平谷区四座楼自然保护区的16个栓皮栎人工林样地的胸径(D)和树高(H)相关生长关系进行了分析,以检验分形网络模型等4个理论模型对胸径-树高关系幂指数(b)的预测值是否合理。结果表明:在16个样地中,有12个样地的数据支持弹性相似模型、分形网络模型的理论预测值2/3,仅有2个样地的数据支持几何相似模型的预测值1,而应力相似模型的预测值1/2没有任何样地数据支持;一元和多元一般线性模型分析表明,各种生境因子和林分因子(地形、土壤、光照、林龄等)对胸径树高生长关系的幂指数都没有显著影响;弹性相似模型、分形网络模型相对于几何相似和应力相似模型更适合描述树木的胸径树高相关生长关系。

       

      Abstract: In order to test the fractal network model and other three allometric models, the allometric relationship between stem diameter (D) and tree height (H) in Quercus variabilis plantations from the Sizuolou Nature Reserve of Beijing was studied based on the power function H=aDb. The results of reduced major axis (RMA) regression showed that in 12 out of 16 plots, the RMA slopes between logD and logH supported the predicted value (2/3) of elastic similarity and fractal network model, while only 2 plots supported the geometry similarity model and no data supported the stress similarity model; both univariate and multivariate analyses showed that habitat and stand factors did not have significant influence on DH allometry. This study suggests that the elastic similarity and fractal network model are more suitable for describing the allometric relationship between D and H than geometry similarity or stress similarity model.

       

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