How to calculate R2 or contribution to RDA of each loading in a PCA in R?

Question

I've run a standard RDA analysis for my data. I am struggling to find a formal analysis for some important insights (i.e. I need a way to report these statements in my paper)

1. The R2 value + rank or order of importance of each environmental variable? I can see that shrub explains the least variation, but between the other three, I cannot identify which is longer. Also, is there a way of getting R2 or Contribution to RDA model? The anova.cca(mite.spe.rda.signif, step = 1000, by = "term") only provides the variance and p-value. I want to learn more about the strength and contributions. Basically I want to create a table like so (attached below) for my paper

2. Consider the red x lying in between Shrub and WaterCont at ~(-0.5,-0.5). I want to estimate if Shru or WaterCont explains more of the variation. How can I calculate the distance from each loading to support my point?

   # Load mite species abundance data
    data("mite")
    # Load environmental data
    data("mite.env")
   
    # Hellinger transform the community data
    mite.spe.hel <- decostand(mite, method = "hellinger")
   
    # Standardize quantitative environmental data
    mite.env$SubsDens <- decostand(mite.env$SubsDens, method = "standardize")
    mite.env$WatrCont <- decostand(mite.env$WatrCont, method = "standardize")
   
    mite.spe.rda.signif <- rda(mite.spe.hel ~ WatrCont + Shrub +
        Substrate + Topo + SubsDens, data = mite.env)
   
    # Find the adjusted R2 of the model with the retained env
    # variables
    RsquareAdj(mite.spe.rda.signif)$adj.r.squared
   
    anova.cca(mite.spe.rda.signif, step = 1000, by = "term")
   
    # Scaling 2
    ordiplot(mite.spe.rda.signif, scaling = 2, main = "Mite RDA - Scaling 2")
   

Table I want to create