自组织特征映射网络在北京松山自然保护区山地草甸数量分析中的应用

doi:10.3773/j.issn.1005-264x.2010.07.006

摘要/Abstract

摘要：

采用自组织特征映射网络(SOM)对松山自然保护区山地草甸群落进行了数量分类研究, 并用Kruskal-Wallis检验和Tukey多重比较方法分析了草甸类型的环境因子之间差异的显著性。结合完全连接法和SOM, 将松山自然保护区的山地草甸群落分为7个类型, 其群落结构、物种组成等特征明显。这7个山地草甸群落主要受海拔高度、坡度、枯枝落叶层厚度和土壤深度等环境因子的影响, 其差异极显著。生态学分析表明SOM是非常有效的植物群落分类方法, 适合于山地草甸植被的研究。

关键词: 分类, 山地草甸, 数量方法, 自组织特征映射网络, 松山自然保护区

Abstract:

Aims Vegetation classification is an important topic in plant ecology, and many quantitative techniques for vegetation classification have been developed. The artificial neural network is a comparative new tool of data analysis. In this paper, self-organizing map (SOM) is applied to cluster analysis of mountain meadow data to determine whether SOM is suitable for classifying meadow vegetation.

Methods Data for 40 quadrats, 87 species, and six environment variables (elevation, slope, aspect, litter layer thickness, soil depth and soil density) from mountain meadow communities in the Sonshan Nature Reserve were analyzed. SOM was used to classify sample quadrats using importance values of species.

Important findings The trained SOM classified sample quadrats into seven groups: Saussurea nivea + Sanguisorba officinalis + Bupleurum chinensis, Sanguisorba officinalis + Artemisia annua + Vicia unijuga, Polygonum divaricatum + Sanguisorba officinalis + Carex rigescens, Carex rigescens + Sanguisorba officinalis + Saussurea nivea, Carex rigescens + Polygonum divaricatum + Rosa dahurica, Polygonum bistorta + Carex rigescens + Artemisia gmelinii and Carex rigescens + Saussurea iodostegia + Polygonum bistorta. The characteristics of community structure and species composition were significant. SOM classification and the dominant species enumeration in the meadow community data reflected the effects of elevation, slope, litter layer thickness and soil depth. SOM is suitable for classifying the mountain meadow communities and could be useful for assessing ecosystem quality and meadow community variations caused by environmental disturbances.

Key words: classification, mountain meadow, numerical method, self-organizing map, Songshan Nature Reserve

苏日古嘎, 张金屯, 田世广, 张钦弟, 张斌, 程佳佳, 刘素军. 自组织特征映射网络在北京松山自然保护区山地草甸数量分析中的应用. 植物生态学报, 2010, 34(7): 811-818. DOI: 10.3773/j.issn.1005-264x.2010.07.006

SURI Guga, ZHANG Jin-Tun, TIAN Shi-Guang, ZHANG Qin-Di, ZHANG Bin, CHENG Jia-Jia, LIU Su-Jun. Application of self-organizing map to quantitative analysis of mountain meadow in the Songshan Nature Reserve of Beijing, China. Chinese Journal of Plant Ecology, 2010, 34(7): 811-818. DOI: 10.3773/j.issn.1005-264x.2010.07.006

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链接本文: https://www.plant-ecology.com/CN/10.3773/j.issn.1005-264x.2010.07.006

https://www.plant-ecology.com/CN/Y2010/V34/I7/811

图/表 5

图1 松山山地草甸样方的自组织特征映射网络分类图。 I-VII代表7个草甸类型。SU表示样方名称。

Fig. 1 Classification of the quadrats of the Songshan mountain meadow by self-organizing map (SOM). I-VII represent seven meadow types. SU shows the name of sample units.

图2 用完全连接法分类自组织特征映射网络(SOM)单元。数字(1-49) 表示其对应的SOM单元。

Fig. 2 Cluster analysis self-organizing map (SOM) unit with the complete linkage method using Euclidean distance. The numbers (1-49) on the dendrogram indicate their corresponding SOM units.

图3 松山山地草甸优势种在自组织特征映射网络训练图上的分布。 d表示重要值。

Fig. 3 Visualization of dominant species of the Songshan mountain meadow in the trained self-organizing map (SOM). d means important value (IV).

表1 松山7个山地草甸类型环境因子的Kruskal-Wallis检验

Table 1 Kruskal-Wallis test of six environmental variables between seven clusters of the Songshan mountain meadow

	海拔 Elevation	坡度 Slope	坡向 Slope aspect	枯枝落叶层厚度 Litter layer thickness	土壤深度 Soil depth	土壤紧实度 Soil solidity
卡方检验Chi-Square	23.751	22.628	7.117	18.163	21.255	6.779
自由度 df	6	6	6	6	6	6
p	0.001	0.001	0.310	0.006	0.002	0.342

图4 松山山地草甸7个群落环境因子的差异(平均值±标准偏差)。不同字母表示差异显著(p < 0.05)。A, 海拔高度。B, 坡度。C, 坡向。D, 枯枝落叶层厚度。E, 土壤深度。F, 土壤紧实度。

Fig. 4 Differences of environmental variables at seven clusters of the Songshan mountain meadow (mean ± SD). Different letters indicate significant difference at the 0.05 level. A, Elevation. B, Slope. C, Aspect. D, Litter layer thickness. E, Soil depth. F, Soil solidity.

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