||On The Empirical Performance Of Non- Metric Multidimensional Scaling In Vegetation Studies.
||International journal of applied mathematics and statistics
||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).
||Non-metric multidimensional scaling, efficiency, vegetation studies.
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