Spatial heterogeneity of soil organic matter in Mayi lake area of Karamay city
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摘要:目的
研究克拉玛依市东部生态屏障的水源地玛依湖区土壤有机质的空间分布规律, 为湖区的生态环境保护提供科学依据和数据支撑。
方法以玛依湖区为研究对象，通过野外采样和室内分析，利用趋势分析法、反距离权重插值法、空间自相关法和半变异函数法分析玛依湖区不同土层深度土壤有机质的空间分布规律。
结果趋势法分析表明，玛依湖区土壤有机质含量在0~20、20~40、40~60和60~80 cm土层的变化速率存在差异，整体趋势为土壤有机质含量南北方向呈增加趋势、东西方向呈减少趋势。反距离权重插值法(IDW)研究表明，玛依湖区不同土层土壤有机质水平分布差异较大，局部地区土壤有机质含量存在明显的垂直分布特征，土壤有机质含量变化趋势同趋势法分析结果高度一致，整体表现为土壤有机质含量南北方向呈增加趋势、东西方向呈减少趋势。空间自相关法研究表明，0~20、20~40、40~60和60~80 cm土层的Moran指数分别为0.1643、0.1236、0.1955和0.2461，均在空间上呈现出显著正相关；4个土层的Z值分别为3.1510、2.5934、3.5903和4.6355，底层(40~60和60~80 cm)的土壤有机质空间正相关较显著、空间聚集程度最高，表层(0~20和20~40 cm)空间相关性不显著、空间聚集程度较低。半变异函数分析法表明，底层(40~60和60~80 cm)土层的块金效应分别为0.427和0.420，说明土壤有机质具有一定的空间相关性；表层(0~20和20~40 cm)土层的块金效应分别为0.033和0.045，土壤有机质的空间相关性较弱。
结论不同土层土壤有机质含量水平差异较大，南北方向呈增加趋势，东西方向呈减少趋势，局部地区存在明显的垂直分布特征。土壤有机质在表层(0~20和20~40 cm)空间相关性不显著，空间聚集程度较低；在底层(40~60和60~80 cm)空间相关性较显著，空间聚集程度较高。土壤有机质空间异质性受土壤类型、土壤质地、外围植被类型以及湖区面积变化的影响较大；在湖区外围生态屏障建设时，防护林树种、种植深度、种植密度的选择应当结合土壤有机质含量的空间分布状况进行。
Abstract:ObjectiveTo study the spatial distribution of soil organic matter in Mayi lake, the water source of the eastern ecological barrier of Karamay city, and provide scientific basis and data support for the ecological environment protection of the lake area.
MethodThe study area of this paper was Mayi lake. Through field sampling and indoor analysis, the study used trend analysis method, inverse distance weight interpolation method, spatial autocorrelation method and semi-variogram function method to analyze the spatial distribution law of soil organic matter at different depths of Mayi lake.
ResultThe trend analysis showed that the change rates of soil organic matter content in Mayi lake were different from 0−20, 20−40, 40−60 and 60−80 cm soil layers, but the overall trend was that soil organic matter content increased in the north-south direction and decreased in the east-west direction. The inverse distance weight interpolation method (IDW) showed that there are large horizontal distribution differences of soil organic matter contents in different soil layers, and regional obvious vertical distribution characteristics of soil organic matter content in Mayi lake. The trends in soil organic matter content change were highly consistent with trend analysis method. The overall performance of the north-south direction of soil organic matter content increased, the east-west direction showed a trend of decrease. The spatial autocorrelation method showed that Moran indexes of four layers were 0.164 3, 0.123 6, 0.195 5 and 0.246 1 respectively, showing a significant positive correlation on the space. The Z values of 0−20, 20−40, 40−60 and 60−80 cm soil layers were 3.151 0, 2.593 4, 3.590 3 and 4.635 5 respectively. The soil organic matter of underlayers (40−60, 60−80 cm) had obvious positive spatial correlation with the highest space aggregation degree. The surface layers (0−20, 20−40 cm) had no significant spatial correlation, and low spatial aggregation degree. The semi-variogram function method showed that the nugget effects of the underlayers (40−60 and 60−80 cm) were 0.427 and 0.420 respectively, indicating spatial correlation of soil organic matter was general. The nugget effects of the surface layers (0−20 and 20−40 cm) were 0.033 and 0.045 respectively, indicating high spatial correlation of soil organic matter.
ConclusionThe contents of soil organic matter in different soil layers vary greatly, and the north-south direction tends to increase, while the east-west direction tends to decrease. The spatial correlations of soil organic matter in the surface layers (0−20 and 20−40 cm) are not significant, and the spatial aggregation degrees are low, while the spatial correlations are significant and the spatial aggregation degrees are high in the underlayers (40−60 and 60−80 cm). The spatial heterogeneity of soil organic matter content is greatly affected by soil type, soil texture, vegetation type and the variation of lake area. While constructing ecological barrier in the periphery of lake area, the selection of shelterbelt tree species, planting depth and planting density should be combined with the spatial distribution of soil organic matter content.
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图 1 不同土层土壤的有机质含量变化趋势
Figure 1. The variation trend of soil organic matter content at different soil layers
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图 2 不同土层土壤有机质含量的反距离权重插值(IDW)
Figure 2. Inverse distance weight(IDW) interpolation method of soil organic matter content at different soil layers
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图 3 不同土层土壤有机质含量的空间自相关分析
A1、B1、C1和D1显示各土层莫兰指数(I)；A2、B2、C2和D2显示各土层莫兰指数经置换后对应的期望值(Ie)
Figure 3. Spatial autocorrelation analysis of soil organic matter content at different soil layers
A1, B1, C1和D1 show Moran index (I) values at different soil layers; A2, B2, C2和D2 show the expected values (Ie) of Moran index after iteration at different soil layers
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图 4 不同土层土壤有机质含量的变异函数模拟模型
Figure 4. Variogram model of soil organic matter content at different soil layers
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图 5 不同土层土壤有机质含量的垂直分布特征
Figure 5. Vertical distribution characteristics of soil organic matter content at different soil layers
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图 6 不同湖区不同土层土壤有机质含量多重比较分析
各图中，柱子上方的不同小写字母表示差异显著(P<0.05，Duncan’s法)
Figure 6. Multiple comparative analysis of soil organic matter content at different soil layers of different lake areas
In each figure, different lowercase letters on bars indicate significant differences(P<0.05, Duncan’s test)
表 1 不同土层土壤有机质含量统计特征值
Table 1 Statistical characteristic values of soil organic matter content at different soil layers
土层/cm
Soil layerw(有机质)/ (g·kg−1) Organic matter content 统计特征值 Statistical characteristic value 最小值
Min.最大值
Max.平均值
Average标准差
Standard deviation方差
Variance变异系数/%
Coefficient of variation偏度
Skewness峰度
KurtosisP 0~20 0.41 35.15 8.72 6.31 39.87 72.39 1.50 3.88 0.168 20~40 0.28 25.68 6.36 4.95 24.51 77.84 1.54 3.71 0.341 40~60 0.19 13.20 5.12 3.18 10.10 62.11 0.58 −0.17 0.837 60~80 0.33 15.23 5.30 3.39 11.48 63.93 0.77 0.73 0.646 
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表 2 各土层土壤有机质含量变异函数模型参数
Table 2 Variogram model parameters of soil organic matter content at different soil layers
土层/cm
Soil layer变异函数模型类型
Variogram model块金值(C0)
Nugget基台值(C0+C)
Sill变程/m
RangeC0/(C0+C)
Nugget/Sill决定系数(R2)
Coefficient of determination0~20 球状模型 Spherical model 1.30 38.96 1 510.00 0.033 0.177 20~40 球状模型 Spherical model 1.08 24.22 1 250.00 0.045 0.061 40~60 高斯模型 Gaussian model 4.23 9.91 7 170.69 0.427 0.661 60~80 指数模型 Exponential model 5.32 12.68 39 660.00 0.420 0.413
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