Analysis of small-scale spatial pattern of Endoclita excrescens based on SADIE and SPPA
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摘要:
目的 柳蝙蛾是一种多食性的森林害虫，在我国东北地区对水曲柳危害严重。在小尺度上研究柳蝙蛾的空间格局，可为精准管理提供基础信息。 方法 本文将柳蝙蛾蛀孔数及其寄主树木水曲柳胸径作为标记的空间点过程的标记，使用基于距离指数的空间分析（SADIE）来分析标记的空间格局和空间关联性。使用L函数来检验水曲柳分布的空间随机性，使用标记条件均值函数来度量标记与点之间的独立性，使用标记变异函数和Stoyan标记相关函数来度量标记的空间相关性。每个样方划分成2种不同密度的小样方来利用SADIE研究水曲柳分布与蛀孔的空间关联性。 结果 两个样地中柳蝙蛾蛀孔均呈显著的聚集分布。样地G1的斑块和间隙分别处于样地的两端，而样地G2的斑块和间隙混杂在一起。样地G1中，在4.0 ~ 4.8 m和14.5 ~ 16.0 m 距离上存在显著的蛀孔数少的树与其他蛀孔少的树互为邻居的格局。样地G2中，在8.5 ~ 9.0 m距离上存在上述格局。标记变异函数分析表明，两个样地中的空间自相关性均不显著。水曲柳的空间格局及其胸径的空间格局均与柳蝙蛾蛀孔的空间格局成强烈的关联性，表明这两个因素均影响柳蝙蛾蛀孔空间格局的形成。SADIE分析及L函数分析均表明水曲柳的空间格局为聚集性。标记条件均值函数分析表明，标记（蛀孔数）不依赖于点（水曲柳位置）。 结论 柳蝙蛾蛀孔在水曲柳林中的空间分布呈聚集性。水曲柳的空间格局及其胸径的空间格局均影响柳蝙蛾蛀孔的空间格局。 Abstract:Objective The swift moth, Endoclita excrescens (Lepidoptera: Hepialidae), is a polyphagous forest insect causing great damages to Manchurian ash (Fraxinus mandshurica) in Northeast China. The spatial patterns of the moth at fine-scale were investigated in this study, which will provide basic information for precision-targeted management. Method Spatial analysis by distance indices (SADIE) was employed to measure spatial aggregation and spatial association of the count of bored holes and the DBH (diameter at breast height) of the host-tree, which were marks of a marked spatial point pattern. The spatial randomness of the distribution of ash tree was tested by the L function. The independence between mark and point was tested by the conditional mean function. Mark variogram and Stoyan mark correlation function were used to measure spatial autocorrelation. Each plot was divided into quadrats to calculated SADIE aggregation indices and clustering indices. Two density plans were used to devide the plot. Result Results from SADIE indicated that bored holes significantly aggregated in the two plots (G1 and G2). In plot G1, patches and gaps were separated and located at the two ends of the plot, respectively; while in plot G2, patches and gaps were mixed. In plot G1, trees with small number of bored holes were significantly close to other trees with small number of bored holes at distances of 4.0−4.8 m or 14.5−16.0 m; while in plot G2, same spatial patterns were identified at distances of 8.5−9.0 m. The results from mark variogram functions showed that there were no significant spatial autocorrelations at small distances in both plots. The spatial patterns of the ash tree and its DBH were strongly associated with the patterns of bored holes, respectively, indicating both of them play roles in the spatial pattern formation of bored holes. Results from either SADIE spatial aggregation index or L function suggested that the host trees were spatially aggregated. And the results from conditional mean of the mark function showed that the mark (count of bored holes) was independent on the point (tree location). Conclusion The spatial pattern of the bored hole of the swift moth is aggregation in the stand of ash. Both the location and size of the host tree shape the spatial patterns of bored holes. -
Key words:
- SADIE /
- spatial point pattern /
- spatial association /
- mark variogram /
- mark correlation function
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图 1 研究样地水曲柳胸径及每株树上虫孔数分布图
A. 样地G1水曲柳胸径 DBH of ash tree in sample plot G1；B. 样地G1虫孔数 Count of bored holes in sample plot G1；C. 样地G2水曲柳胸径 DBH of ash tree in sample plot G2;D. 样地G2虫孔数 Count of bored holes in sample plot G2
Figure 1. DBH of host ash tree and counts of bored holes
图 2 研究样地划分小样方方案
A. 样地1，方案P1 Plan P1 for sample plot G1；B. 样地1，方案P2 Plan P2 for sample plot G1；C. 样地2，方案P1 Plan P1 for sample plot G2；D. 样地2，方案P2 Plan P2 for sample plot G2
Figure 2. Plans for quadrating
图 3 水曲柳蛀孔数及胸径分布的红蓝图
A.样地G1，蛀孔 Bored holes of sample plot G1；B.样地G1，胸径 DBH of sample plot G1；C.样地G2，蛀孔 Bored holes of sample plot G2；D.样地G2，胸径 DBH of sample plot G2
Figure 3. Red-blue plots of bored holes and DBH of ash
图 4 水曲柳空间分布随机性检验
A. 样地G1 Sample plot G1；B. 样地G2 Sample plot G2。包迹线的宽度为2 × dcrit，obs和theo分别代表观测值和期望值，lo = 期望值 − dcrit，hi = 期望值 + dcrit。The simultaneous critical envelopes have constant width 2 × dcrit; obs and theo represent observed values and expected values, respectively; lo = expected − dcrit and hi = expected + dcrit.
Figure 4. Spatial randomness test for distribution of ash trees
表 1 基于点数据的空间聚集指数和空间关联指数
Table 1. Spatial aggregation indices and spatial associations based on point-referenced data
样地 Sample plot 蛀孔 Bored hole 胸径 DBH 空间关联指数
Spatial association index (X)修改的t检验 Modified t test Ia P Ia P F df1, df2 P G1 2.897 2 < 2.22×10−16 2.251 4 < 2.22×10−16 0.685 4 195.025 1 1, 220.121 7 0 G2 2.106 0 < 2.22×10−16 3.059 1 < 2.22×10−16 0.453 3 28.612 9 1, 110.612 5 0 注：Ia.聚集指数。下同。Note: Ia, index of aggregation. The same below. 
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表 2 基于小样方数据的空间聚集指数和空间关联指数
Table 2. Spatial aggregation indices and spatial associations based on quadrat-referenced data
样地 Sample plot 小样方划分方案
Quadrating plan蛀孔 Bored hole 寄主树 Host tree 空间关联指数
Spatial association index修改的t检验 Modified t test Ia P Ia P F df1, df2 P G1 P1 1.501 0 0.01 1.430 0 0.03 0.735 4 31.005 6 1, 26.324 5 0 P2 1.704 3 0.01 1.612 9 < 2.22×10−16 0.725 0 72.074 3 1, 65.041 2 0 G2 P1 1.398 4 0.08 1.346 0 0.07 0.782 9 34.416 0 1, 21.726 7 0 P2 1.791 5 0.01 1.933 9 < 2.22×10−16 0.821 2 61.212 3 1, 29.563 3 0
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