Solved – Do I always need to log transform the data to do a canonical correspondence analysis

I have species relative abundance data (as percentages) and several environmental parameters- and I have done normality tests on my data and it all seems to be normally distributed, but do I need to log transform the data anyway? I saw an online tutorial for CCA and it said to, but I would like to be sure.

CCA is sensitive to outliers and assumes species response is a symmetrical unimodal function of position along environmental gradients. Hypothesis testing is based on randomization, so does not have distributional assumptions. But, CCA or not, transformations should be applied only if they improve data distribution (demonstrated using normality tests or PPCC fit).

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