[R-sig-ME] Generalized randomized block design

[leverkus]

Dear R users,

I am struggling with the formulation in lme of a generalized randomized 
block design with subsampling, and I would very much appreciate some 
help. The experiment consists of 3 plots (of ca. 20 ha each) located at 
different altitudes on a mountain slope. In each plot there are 9 
subplots, which are 3 replicates of 3 post-fire wood management 
treatments. In each subplot we sampled 8 transects for plants (except in 
one subplot, where only 5 transects were sampled), and my response 
variable is species diversity. In order to take account for the 
experimental design and get the correct number of denominator degrees of 
freedom, I am using (1|Plot/Subplot) in the random effects. Subplot is a 
categorical variable which joins treatment names (treatments are "SL", 
"NI", "PCL") and replicates (1,2,3): SL1, SL2, SL3, NI1... This gives me 
the correct replication: 3 plots and 27 subplots. As for now, my model 
looks like this:

lme(diversity~Treatment,random=~1|Plot/Subplot)

However, treatment effects are likely to vary with altitude, so I wish 
to test for the treatment x plot interaction. This is where I am stuck. 
By including plot as a fixed effect (diversity~Treatment*Plot) I have no 
df to calculate the plot effect and this looks weird to me. Besides, I 
want to have plot as a random effect. Could anyone give me some 
suggestions? (I don´t mind using lmer instead.)

Thanks in advance,

alex