How to calculate length of loading vector and distance from given point 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)

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:

1. The length of each loading vector (WaterCont and Shrub)

2. The distance or red, x from each loading to find out which contributed to more variation?

   # 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")
   
   # 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")