Table 2.

Interpretation of ordination diagrams

Linear methods (PCA, RDA)
PCA, RDA		RDA
				Scaling 1	Scaling 2
Samples	Species	ENV	NENV	Focus on sample (rows) distance	Focus on species (columns) correlation
✓				Euclidean distances among samples	–
	✓			–	Linear correlations among species
		✓		Marginal effects of ENV on ordination scores	Correlations among ENV
			✓	Euclidean distance between sample classes	–
✓	✓			Abundance values in species data
✓		✓		–	Values of ENV in the samples
✓			✓	Membership of samples in the classes
	✓	✓		Linear correlations between species and ENV
	✓		✓	Mean species abundance within classes of nominal ENV
		✓	✓	–	Average of ENV within classes
Unimodal methods (CA, CCA)
CA, CCA		CCA		Focus on sample (rows) distance and Hill's scaling	Focus on species (columns) distances
✓				Turnover distances among samples	χ2 distances between samples
	✓			-	χ2 distances among species distributions
		✓		Marginal effects of ENV	Correlations among ENV
			✓	Turnover distances between sample classes	χ2 distances between sample classes
✓	✓			Relative abundances of the species table	Relative abundances of the species table
✓		✓		–	Values of ENV in the samples
✓			✓	Membership of samples in the classes
	✓	✓		Weighted averages – the species optima in respect to particular ENV
	✓		✓	Relative total abundances in the sample classes
		✓	✓	–	ENV averages within sample classes

The interpretation of ordination diagrams depends on the focus of the study, because sample scores are rescaled as a function of the scaling choice. Approximate relationships between and among the different elements represented in biplots and triplots as species (represented as dots or arrows), samples (dots), environmental variables (ENV; arrows), and nominal (qualitative) environmental variables (NENV; dots). A meaningless interpretation (“–”) happens when the suggested comparison is not optimal because of inappropriate scaling of the ordination scores. Adapted from ter Braak (1994); Leps & Smilauer (1999); ter Braak & Smilauer (2002).