Spatial ordination of vegetation data using a generalization of Wartenberg's multivariate spatial correlation
Spatial ordination of vegetation data using a generalization of Wartenberg's multivariate spatial correlation
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DOI：
10.3170/2007-8-18312
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发表时间：
2008-02-01
影响因子：
2.8
通讯作者：
Debias, Francois
中科院分区：
文献类型：
--
作者：
Dray, Stephane;Said, Sonia;Debias, Francois
问：植物群落的组成是否存在空间结构？方法：植物空间结构的识别和测量是植物生态学研究的热点。空间自相关的单变量测量，如Moran's I和Geary's c，被广泛使用，但对多变量情况的扩展（即。e.多物种）是罕见的。在这里，我们提出了一个多变量空间分析的基础上莫兰的我（MULTISPATI）通过引入一个行和标准化的空间权重矩阵的统计三重符号。这种分析是瓦尔滕贝格的多元空间相关性方法的推广，意味着许多变量之间的关系（多元分析）和它们的空间结构（自相关性）之间的折衷。MULTISPATI方法非常灵活，可以处理各种类型的数据（定量和/或定性数据，列联表）。一项研究是为了说明使用对应分析的空间版本的方法。位置：Territoire d 'Etude et d' Experimentation de Trois-Fontaines（法国东部）。排序的植被地块的空间分析是相当强大的参考稀有物种和突出的空间格局相关的土壤特性。
Question: Are there spatial structures in the composition of plant communities?Methods: Identification and measurement of spatial structures is a topic of great interest in plant ecology. Univariate measurements of spatial autocorrelation such as Moran's I and Geary's c are widely used, but extensions to the multivariate case ( i. e. multi-species) are rare. Here, we propose a multivariate spatial analysis based on Moran's I (MULTISPATI) by introducing a row-sum standardized spatial weight matrix in the statistical triplet notation. This analysis, which is a generalization of Wartenberg's approach to multivariate spatial correlation, would imply a compromise between the relations among many variables ( multivariate analysis) and their spatial structure ( autocorrelation). MULTISPATI approach is very flexible and can handle various kinds of data ( quantitative and/or qualitative data, contingency tables).A study is presented to illustrate the method using a spatial version of Correspondence Analysis.Location: Territoire d'Etude et d'Experimentation de Trois-Fontaines ( eastern France).Results: Ordination of vegetation plots by this spatial analysis is quite robust with reference to rare species and highlights spatial patterns related to soil properties.