In RDA redundancy analysis, what is the meaning of the principal components that appear in the result I ran an RDA through the vegan package in R using water quality (dependent variable) and land use (independent variable) data.
In my test I got the following results:

The total inertia was 9, and the model explained approximately 41.66% (Constrained = 0.4166 = ) of the variance between the quality and land use data.
The model still provides the main axes (RDA1, RDA2, RDA3, RDA4) and 6 more Principal Componentes (PC1 to PC6) as shown in the figure.
I would like to know what the meaning of these components? how to interpret them? I understand that the components absorbed the unexplained variance (Unconstrained =0.5834)

The constrained axes are linear combinations of the explanatory variables (in order of which explain the most variation of the species matrix). The axes are orthogonal to each other (i.e. right angles).
Your results provide the proportion explained by each of the 5 constrained axes (RDA1 to RDA5), and six unconstrained axes (PC1 to PC6). So the Eigenvalues and Proportion Explained provide additional breakdowns of how the explained and unexplained variance is split between those axes. Next you can use ordination plots to see which of your explanatory variables each axis is separating.
