12 December 2017 8 8K Report

Hello,

I have a series of environmental data layers in r that I am trying to do a PCA on to reduce dimensionality for my SDM (species distribution model). I am attempting to do this in R in order to run maxent with the dismo package.

All of the papers I have read that do this technique do not give much detail regarding how to accomplish this. My specific questions are as follows:

  • when you look for correlations between variables and run a PCA, do you use both presence and absence/background points together or do correlations for each separately? I assume it is the former but I want to make sure before I code it.
  • Regarding absence points, I am using background points for maxent, so they are not true absence points. Therefore I can select as many points as I want and I’m finding confounding information regarding how many points to use. Maxent uses 10,000 by default - should I be using 10,000 in my correlation and PCA?
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