On The Empirical Performance Of Non-Metric Multidimensional Scaling In Vegetation Studies V. K. Salako 1 , A. Adebanji 2 and R. Glèlè Kakaï 1 1 Faculty of Agronomic sciences, University of Abomey-Calavi, 01 BP 526, Cotonou, Bénin Email: gleleromain@yahoo.fr 2 Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana Email: tinuadebanji@gmail.com 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-Bénin. 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). Keywords: Non-metric multidimensional scaling, efficiency, vegetation studies. Mathematics Subject Classification: 91C15, 68U20 1. INTRODUCTION In vegetation studies, ordination aims at sorting samples and/or species along a few axes which must represent the main compositional gradients in the data set, using either abundance or presence/absence data (Økland, 1996). It seeks a parsimonious representation of samples and/or species in a space of low dimensionality. Parsimony in this context implies that distances between samples and/or species in ordination space optimally represent their original dissimilarities (between samples or species) in variable space, in some defined sense (Kenkel and Orloçi, 1986). Common goals of ordination methods in vegetation studies are: description and recognition of vegetation distribution patterns, identification of plant’s communities and examination of plants and communities distribution in relation to environmental factors and gradients (Kenkel and Orloçi, 1986; Kent and Ballard, 1988; Podani, 2006). International Journal of Applied Mathematics and Statistics, Int. J. Appl. Math. Stat.; Vol. 36; Issue No. 6; Year 2013, ISSN 0973-1377 (Print), ISSN 0973-7545 (Online) Copyright © 2013 by CESER Publications www.ceser.in/ijamas.html www.ceserp.com/cp-jour www.ceserpublications.com