A canonical correspondence analysis (CCA) was carried out using the same dataframes of species occurences and environmental values as previously mentioned in order to test whether a constrained ordination would show a significant effect of treatment on species composition (Legendre & Legendre, 2012). In order to deal with potential errors associated with very rare species, the data was first log+1 transformed. The analysis was then carried out using the function “cca” from the “vegan” package. The function uses chi-square transformation of the data matrix and performs a weighted linear regression on the constraining variables, the fitted values of which are then subjected to correspondence analysis via singular value decomposition (Oksanen, 2021a). Variance inflation factors were calculated using the “vif.cca”
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function from “vegan”, with no values >10, indicating no collinearity between the environmental variables (Oksanen, 2021c). An analysis of variance was then perfomed for the constraining variables using the function “anova.cca” from “vegan”. The same function was also used to test the significance of the constraining axes, of which the first three were significant. The centroid value coordinates of the environmental variables were then extracted and plotted along with the sample coordinates using the “ggplot” function from the “ggplot2” package.
The process was then repeated for the biomass estimates.