PCoA, also called classical multidimensional scaling, answers the same visual question as NMDS — do my samples group? — but from a dissimilarity matrix and with a metric fit. In Magic Stat it is a dialog, not a script.
PCoA takes a dissimilarity matrix between samples and places them in a low-dimensional space, trying to preserve the distances themselves rather than only their ranking. That is the practical difference from NMDS: PCoA is a metric method, so it reports how much of the variation the axes explain, while NMDS only reports stress.
It is the natural companion to any distance-based analysis — the ordination you draw next to a PERMANOVA, using the same dissimilarity measure, on the same data. If you have already chosen Bray–Curtis for one, staying with Bray–Curtis for the other is not a stylistic choice, it is what makes the two results describe the same thing.
The awkward part has always been the plumbing: computing the distance matrix, feeding it to the ordination, and getting a figure whose axes you can label without guessing.
Dissimilarity measures: Bray–Curtis · Euclidean · Manhattan · Canberra · Jaccard · Sorensen · Horn–Morisita.
Transformations: None · Z-score (standardise) · ln(1+x) · ln(x) · Hellinger · Chord · Chi-square · Square root · Range 0–1.
PCoA is metric: it preserves the distances and tells you how much variation the axes explain. NMDS is non-metric: it preserves only the ranking, and reports stress. For strongly non-linear data NMDS often gives the more readable picture; for a figure that pairs with PERMANOVA, PCoA is the natural choice.
Match it to the data. Bray–Curtis for abundance, Jaccard or Sorensen for presence/absence, Euclidean for standardised continuous variables.
No. If you use R comfortably, keep it; the value here is the output and the manuscript workflow.
No — the analysis runs locally. The only automatic signal is an anonymous installation counter with no data in it.