One must remember is not properly a statistical analysis tool but a data visualization tool. Is biased. (although you can do further PC regression and even partial least squares regressions, but that's just for completition) In this case, since the information I wanted to represent and the point I try to make, a cline and how is a linear continuum of change across geography. PCA's represent linearly changes in a continuum of a set, it sounds like dimensional reduction is a match made in heaven for us in this case.

If you want to represent a lot of information from several variables, with probable correlations analysis, in a single graph while conserving most of the variance of the data. The first advantage is you can represent it in two dimensions (draw it, I mean try to draw 7 variables and their axis), the second is that the clustering or redundancy hints at the information that was lost in the flattening or reduction of the dimensions.

^{Video on the intuition of PCA 1}, ²

The two more common options are either PCA or Heatmaps with hierarchical clustering.

^{Article comparing their differences}

Those clusters are methods to group the shared redundancies of the data of those variables. It saves a lot of computational and graphical costs instead of doing this:

ps: Sorry if you get lots of notifications on the edits. I'm doing this from a phone and I have OCD.