On The Empirical Performance of Non-Metric Multidimensional Scaling in Vegetation Studies

Abstract

Non-metric multidimensional scaling (NMDS) is widely used as a routine method for ordination in vegetation studies. Its use in statistical softwares often requires the choice of several options on which the accuracy of results will depend. This study focuses on the combined effect of sample size, similarity/dissimilarity indexes, data standardization and structure of data matrix (abundance and binary) on NMDS efficiency based on real data from the Lama Forest Reserve in Southern-Benin. The Spearman’s Rank Correlation coefficient and the s-stress were used as an assessment criterion. All the four factors were found to influence the efficiency of the NMDS and the samples (plots) standardization to equal totals gave the best results among standardization procedures considered. The Jaccard and Sorensen similarity/dissimilarity indexes performed equally whatever the nature of the matrix. However, with binary matrices, Sokal and Michener similarity index performed better. A quadratic relationship was noted between s-stress and sample size. A lower optimal sample size (75 plots) was observed for the binary matrices than for the abundance ones (90 plots).