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    程希平, 水崎大二郎, 王四海, 吴利华, 马月伟, 巩合德. 基于空间自相关构建树木生长模型[J]. 北京林业大学学报, 2012, 34(5): 113-119.
    引用本文: 程希平, 水崎大二郎, 王四海, 吴利华, 马月伟, 巩合德. 基于空间自相关构建树木生长模型[J]. 北京林业大学学报, 2012, 34(5): 113-119.
    CHENG Xi-ping, MIZUSAKI Daijiro, WANG Si-hai, WU Li-hua, MA Yue-wei, GONG He-de. Construction of tree growth model based on spatial autocorrelation[J]. Journal of Beijing Forestry University, 2012, 34(5): 113-119.
    Citation: CHENG Xi-ping, MIZUSAKI Daijiro, WANG Si-hai, WU Li-hua, MA Yue-wei, GONG He-de. Construction of tree growth model based on spatial autocorrelation[J]. Journal of Beijing Forestry University, 2012, 34(5): 113-119.

    基于空间自相关构建树木生长模型

    Construction of tree growth model based on spatial autocorrelation

    • 摘要: 2004—2008年,在日本九州地区宫崎县田野天然次生林内设置的一块100 m×100 m固定标准地中,通过每木调查,测量了固定标准地内树木的种类、空间位置、生长等数据。为了便于理解,以固定标准地中优势树种蚊母树为主要研究对象,在考虑周边所有树木的影响的同时,利用贝叶斯统计方法分析了空间自相关及树木间的对称竞争、非对称竞争对树木生长过程的影响,并比较了忽略空间自相关的情况。结果表明:在构建研究对象树种的生长模型时,树木个体间的竞争是不可缺少的参数,尤其是个体间的对称竞争。在利用空间自相关参数建模时,最终模型的决定系数R2=0.83;而忽略空间自相关参数的模型,其决定系数R2=0.74。通过其他主要树种的分析也表明了导入空间自相关参数的优越性,因此可以认为,考虑空间自相关的随机效应模型能更精确地预测树木的生长。本研究所采用的空间自相关模型不仅可以利用树木个体的分布信息推测其生态学特征,还为树木生长模拟提供了理论与方法上的借鉴。

       

      Abstract: We established a permanent 100 m×100 m plot located at Tano secondary forest in Miyazaki prefecture, southwestern Japan between 2004 and 2008. Within this plot, we recorded all stems with the girth at breast height (GBH) no less than 15 cm, mapped the positions of corresponding stem bases, and identified all tree species with recorded GBH. We focused on studying one dominant tree species, Distylium racemosum. A Bayesian statistical approach was applied to quantify a spatially autocorrelated random effect. The effect of competition on individual growth was also considered. We compared the model including the spatially autocorrelated random effect with that did not. The results showed that competition, especially symmetrical competition, played a very important role in affecting individual growth. When spatially autocorrelated random effect was included, compared with the model that did not include the random effect (R2=0.74), the model accounted for significant proportions of the variation (R2=0.83). We also analyzed other tree species in the plot, and high correlation was consistently found when spatial autocorrelation was included. The Bayesian approach used in this study, including the intrinsic CAR model, is a powerful tool that can obtain important ecological information from forest census data, and provide theories and methods for future researches on simulation of individual tree growth.

       

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