Nonmetric MultiDimensional Scaling 
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MultiDimensional Scaling (MDS) is a technique for expressing the similarities between different objects in a small number of dimensions. Hopefully, this allows a complex set of interrelationships to be summarised in a simple figure. The method attempts to place the most similar objects (samples) closest together. The starting point for the calculations is a similarity or dissimilarity matrix between all the sites or quadrats. These can be nonmetric distance measures for which the relationships between the sites/objects/samples (columns) cannot be plotted in a Euclidean space. The aim of Nonmetric MDS is to find a set of metric coordinates for the sites which most closely approximates their nonmetric distances.
The basic MDS algorithm is as follows:
CAP uses Kruskals's least squares monotonic transformation to minimise the stress (see Kruskal, 1964; Kruskal & Wish, 1977). The program is designed to find an optimal twodimensional representation of the data. It can happen that no useful twodimensional representation can be produced. While it is possible to produce anything up to a sixdimensional solution, in practice this is of little use, as it cannot be displayed. When requested, CAP lists solutions for 3 or more dimensions, but does not plot them.
From the ordination dropdown menu select Nonmetric MDS. The setup for Nonmetric MDS is displayed. This offers a series of options that are described in NMDS Starting Configuration. The program can usually be run with the default values.
Output from Nonmetric MDS is presented under a number of tabbed components that can each be viewed by clicking on the tab. These are described in turn below:
