VarCorr vs ranef

Can someone tell me why correlations between raw random effects are
different from that provided in VarCorr for lme models?
For example:

fm1 = lme(distance ~ I(age-8), random = ~ 1 + I(age-8) | Subject, data =
Orthodont)
R# VarCorr(fm1)
Subject = pdLogChol(1 + I(age - 8))
           Variance StdDev Corr
(Intercept) 3.55937  1.8866 (Intr)
I(age - 8)  0.05127  0.2264 0.209
Residual    1.71620  1.3100

and

R# cor(ranef(fm1))
           (Intercept) I(age - 8)
(Intercept)      1.0000     0.5764
I(age - 8)       0.5764     1.0000

This isn't a complete answer, but the figures in VarCorr and the model
summary are the population estimates for the random effects (the
parameters) while everything derived from ranef() refers to the actual
Subjects in the data (the BLUPs).

Look at:

sd(ranef(fm1))

(Intercept)  I(age - 8)
  1.7359554   0.1557322

Those figures don't match the VarCorr standard deviations either,
especially the second.

I don't know why the BLUPs pattern differently, exactly, but I did
look at plot(coefs(fm1)) which suggested Sex should be added as a
fixed effect. Once I did that, the correlation between the random
effects changed quite a lot (but was still different between VarCorr
and ranef; the population correlation was actually negative...)

D