Theory, Examples & Exercises
Ordination diagrams are (usually two-dimensional) representations of the ordination analysis results. Different ordination methods may differ in conventions which and how the results are displayed (see the comparison of PCA, CA, RDA and CCA ordination diagrams on Fig. 1; visual appearance also varies among programs and authors. Important for plotting ordination diagrams is the fact that ordination methods return “scores” for descriptors (often species, but could also be e.g. environmental variables) and samples (sites), which are their positions (coordinates) along ordination axes. Most of the ordination methods contain many ordination axes (with the number of axes depending on the algorithm used), but only a few of them are worth to display and interpret, which makes drawing of ordination diagrams meaningful.
The following are standard conventions for displaying ordination diagrams:
Several different types of ordination diagrams are used, depending on the number or type of variables displayed (Fig. 2): *scatterplot (one type of data, e.g. only species scores or only site scores), biplot (two types of data, e.g. species scores and environmental variables), triplot (three types of data, e.g. species and samples scores and environmental variables). Joint plots** are another term used for ordination diagrams mostly showing the vectors of environmental variables, used to interpret the relationship (correlation) between them (from the angle and the length of the vector, more on this in the section Ordination analysis).
Most often two, sometimes three, rarely four, almost never more. If you are familiar with your data (e.g. sampling design, environmental variability), you will probably know what number of ecological dimensions is worth to consider (e.g. reflecting the number of main uncorrelated environmental variables shaping species composition). But do not attempt to interpret too many axes; as put by McCune & Grace (2001): “Very few ecologists have dared to venture into the uncertain waters of four or more dimensions”.
In the case of unconstrained ordinations, you may plot the scree plot (barplot) of eigenvalues. Eigenvalue represents the variation in data which is captured by given ordination axis, and ordination axes are ordered according to their eigenvalues (from highest to lowest). From the scree plot, you can see whether there is some major discontinuity (i.e. whether there is a steep decline in the eigenvalue from some of the axis) and use only those axes which capture a considerably higher number of variation. Also, the following two methods can help with the decision of how many axes to keep (Borcard et al. 2011, Fig. 3):
In the case of constrained ordination, you may consider testing the significance of individual axes and project only those, which are significant.