Spatial ordination of vegetation data using a generalization of Wartenberg's multivariate spatial correlation
Abstract: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'Expérimentation 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.
Document Type: Research Article
Publication date: February 1, 2008
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- The Journal of Vegetation Science publishes original articles, short notes and review articles in the field of vegetation science, both methodological and theoretical studies, and descriptive and experimental studies of plant communities and plant populations. The Journal is the Official Organ of the International Association for Vegetation Science (IAVS).
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