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李颖, 张婕, 郭东罡, 上官铁梁. 基于大样地油松种群的地统计学分析[J]. 植物科学学报, 2015, 33(2): 158-164. DOI: 10.11913/PSJ.2095-0837.2015.20158
引用本文: 李颖, 张婕, 郭东罡, 上官铁梁. 基于大样地油松种群的地统计学分析[J]. 植物科学学报, 2015, 33(2): 158-164. DOI: 10.11913/PSJ.2095-0837.2015.20158
LI Ying, ZHANG Jie, GUO Dong-Gang, SHANGGUAN Tie-Liang. Geostatistical Analysis of Pinus tabulaeformis Population Spatial Patterns Based on Large Plots[J]. Plant Science Journal, 2015, 33(2): 158-164. DOI: 10.11913/PSJ.2095-0837.2015.20158
Citation: LI Ying, ZHANG Jie, GUO Dong-Gang, SHANGGUAN Tie-Liang. Geostatistical Analysis of Pinus tabulaeformis Population Spatial Patterns Based on Large Plots[J]. Plant Science Journal, 2015, 33(2): 158-164. DOI: 10.11913/PSJ.2095-0837.2015.20158

基于大样地油松种群的地统计学分析

Geostatistical Analysis of Pinus tabulaeformis Population Spatial Patterns Based on Large Plots

  • 摘要: 在对山西省灵空山国家级自然保护区4 hm2样地内油松种群调查的基础上,采用地统计学的半方差分析法对油松种群的空间分布格局进行了研究,并用克里金插值法绘制了不同径级油松种群胸径的等值线图。结果表明:油松种群径级Ⅰ、Ⅲ、Ⅳ的最优半方差拟合模型均为指数模型,径级Ⅱ的最优拟合模型为球状模型,说明4个径级都为聚集分布且均为中等的空间相关性;油松种群径级Ⅰ、Ⅱ、Ⅲ、Ⅳ的空间自相关范围分别为23.4、15.2、11.1、24.9 m,分维数大小依次为径级Ⅱ(1.999)>径级Ⅲ(1.995)>径级Ⅳ(1.973)>径级Ⅰ(1.969)。以树高为变量作半方差分析的结果与以胸径作为变量的分析结果基本一致,且以树高为变量进行的类似分析也进一步证实了该结果的准确性。利用Surfer软件绘制的Kriging图直观反映了油松种群空间分布的斑块聚集效果,即油松种群空间格局纹理图。本文利用地统计学的半方差分析法和Kriging插值方法相结合弥补了传统格局分析方法的不足,能精确直观地反映出油松种群个体的空间分布、斑块的聚集效果等,可为植物空间分布格局的分析提供有效的研究方法。

     

    Abstract: The distribution patterns of the Pinus tabulaeformis population in the National Nature Reserve of Lingkong Mountain in Shanxi Province were studied using geostatistics. According to the data of 4 hm2 sample plots, the semivariances of the P. tabulaeformis diameter grades were calculated, semivariogram models were simulated, and Kriging interpolation was performed to draw Kriging maps showing different diameter grades. Results showed that the semivariograms of diameter gradesⅠ, Ⅲ and Ⅳ well fit the exponential model, while diameter grade Ⅱ fit the spherical model, suggesting aggregated spatial distribution patterns in all four diameter grades. In addition, all diameter grades exhibited medium spatial correlation with the range of spatial autocorrelation of 23.4 m, 15.2 m, 11.1 m and 24.9 m, respectively, and the fractal dimensions were ranked as: diameter grade Ⅱ (1.999) > diameter grade Ⅲ (1.995) > diameter grade Ⅳ (1.973) > diameter grade Ⅰ (1.969). The semivariances of height were basically the same as the semivariances of diameter at breast height (DBH), which further verified the accuracy of our analysis. The Kriging map drawn by the Surfer software demonstrated the patch aggregation of the P. tabulaeformis population. The semivariance analysis and Kriging interpolation methods filled the loopholes of traditional population spatial distribution analyses, and were advantageous in revealing the changes in spatial pattern and aggregate intensity for more precise studies of plant patterns in the landscape.

     

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