I am learning to apply constrained ordination to community data using the *vegan* package in R. According to some materials, like [this one][1], in a scaling type 2 RDA triplot: 

>  Angles between all vectors reflect linear correlation. 

It confuses me how the triplot would look like if all explanatory variables are uncorrelated. I tested in R by creating a matrix consists of 5 vectors with very low（0.001）correlations (code copied from [the second answer][2]) as explanatory variables using code below :

```
set.seed(42)
require(vegan)

# generate uncorrleated variables
n <- 5

R <- matrix(.001, nrow = n, ncol = n)
diag(R) <- 1

# Cholesky decompostion of correlation matrix
Lut <- chol(R)
L <- t(Lut)

# Standard deviations
sds <- seq(10, 1, length.out = n)

# VCOV matrix
Sigma <- diag(sds) %*% L %*% Lut %*% diag(sds)

# Generate variables
library(MASS)
X <- mvrnorm(50, mu= rep(0, n), Sigma, empirical = TRUE)
cor(X)

#generate response variables
b1=c(0.2,0.3,0.5,0.3,0.4)
b2=c(0.5,-0.3,-0.2,0.7,0.8)
y1=b1%*%t(X)+rnorm(10,sd=0.01)
y2=b2%*%t(X)+rnorm(10,sd=0.01)

#do RDA
y=cbind(as.vector(y1),as.vector(y2))
X=as.data.frame(X)
rd=vegan::rda(Y=X,X=y)
plot(rd,scaling=2)

```
And it generates plot like this, with V1,V4 and V5 very close to each other though they have low correlations. 

[The plot][3]

So my question is what is wrong with my understanding and how should I interpret angles between explanatory vectors in a RDA triplot?

Thanks in advance.

                                               


  [1]: https://fukamilab.github.io/BIO202/06-B-constrained-ordination.html
  [2]: https://stats.stackexchange.com/questions/180654/create-uncorrelated-random-multivariate-normals
  [3]: https://i.sstatic.net/jRXM7.png