The following dynamic plots demonstrate some issues handles in lectures. Dynaic features require allowing JavaScript.

- PCA as rotation of species
space: the graph shows a species space of three reindeer lichens
(
*Cladina*). The dots show the abundance of each species in sample plots. The axes are centred so that zero corresponds to the average cover of each species. The crossed lines show the first Principal Components. If you rotate the graph so that only first and second PC are visible, you have performed the PCA. - CA as rotation of spcies space:
The differences to PCA are
- Data are proportional. Because of this, you only have independent informatin on two species: you know the proportion of the third species when you sum together abundances of two other species. Therefore points lay on a plane (2D) in the 3D space.
- Analysis is weighted: the size of the dot is proportional to the total abundance of these three lichens. There are only two CA axes, and they will lay on this plane, and the first axis as as close as possible to heavy (large) points.

- CA distortion: Correspondence Analysis of Bryce Canyon data shows marked arcs in 3D. These are actually more complicated than the standard simple arc. These do not describe meaningful features in the data and therefore they are regarded as artefacts.
- DCA distortion: Detrended Correspondence Analysis attempts to remove CA distortion, but it brings along its own kind of artefacts. Spinning the graph, you see that the plot is like a plane twisted in 3D. This is known as lolly paper effect (in Queen's English) or as a lasagna effect (in US English).