Comparing and classifying communities
irrespective of abundance, equal weighting whereas the analysis undertaken on
the variance   covariance matrix will reflect differences in abundance, but can
result in the dominant species determining the output. When successful, PCA will
present major features of a complex ecological community in only 2 or 3
dimensions and the ordination of sites along these new axes can be related to
underlying environmental factors that are moulding community structure. PCA
can be judged a success when the first two or three principal axes explain an
appreciable proportion of the total variability in the data set. For large ecological
data sets with > 20 species if the three largest axes can explain more than 30%
of the variance this would be good.
Non metric multidimensional scaling (NMDS)
NMDS can be viewed as a more generalised development of PCA. Benthic
ecologists working with full quantitative data from grab samples particularly favour
it. It is widely applicable and frequently produces a better ordination than PCA. It
takes more computational effort than PCA and has a potentially important feature
that should be noted. There is no guarantee that the ordination will be the best
possible and it is therefore essential to do a number of runs with different starting
conditions to be sure that you have obtained a stable ordination.
Reciprocal averaging (RA)
Reciprocal Averaging
 (also termed Correspondence Analysis) and an adjusted
version called Detrended Correspondence Analysis (
) are the final
types of ordination method that are frequently used. They are best used on
quantitative data although they can give good results with classed abundance
data. Both of these methods are particularly effective when it is suspected that
the sites can be arranged along an environmental gradient. Further, the method
allows the site and species ordinations to be plotted on the same figure, which
allows the influence of the species in determining the ordination of the sites to be

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