summary.lme() vs. anova.lme()

Dear R list:

I modelled changes in a variable (mconc) over time (d) for 
individuals
(replicate) given one of three treatments (treatment) using:
mconc.lme <- lme(mconc~treatment*poly(d,2), 
random=~poly(d,2)|replicate,
data=my.data)

summary(mconc.lme) shows that the linear coefficient of one of the
treatments is significantly different to zero, viz.
                           Value Std.Error  DF   t-value p-value
...      ... ...      ...
...
treatmentf:poly(d, 2)1  1.3058562 0.5072409 315  2.574430  0.0105

But anova(mconc.lme) gives a non-significant result for the 
treatment*time
interaction, viz.
                    numDF denDF   F-value p-value
(Intercept)              1   315 159.17267  <.0001
treatment                2    39   0.51364  0.6023
poly(d, 2)               2   315  17.43810  <.0001
treatment:poly(d, 2)     4   315   2.01592  0.0920

Pinheiro & Bates (2000) only discusses anova() for single arguments 
briefly
on p.90.
I would like to know whether these results indicate that the 
significant
effect found in summary(mconc.lme) is spurious (perhaps due to
multiplicity).

Many thanks,
Dan Bebber

Department of Plant Sciences
University of Oxford
South Parks Road
Oxford OX1 3RB
UK
Tel. 01865 275000