[R-sig-ME] Is my model correct (1 random effect + spatially structured outcome) ?

[trichter m@ili@g off u@i-breme@@de]

Dear list,

i have already posted once about this dataset, however now with a  
different approach.

My dataset consists of six sampling dates (several months apart) with  
60 sampling stations each (within 100 square meters).
Initially, i wondered if i can calculate Tukey contrasts by sampling  
dates if they are possibly both fixed and random.
This time, my approach is fairly basic. I would like to model the  
influence of some environmental predictors (e.g. pH) on my outcome.
I dont think my stations (specified with x,y coordinates) have random  
intercepts (as they are close to each other), but they likely feature  
spatial autocorrelation.
This time, i treat time as random, and since the sampling dates are  
months apart, and the sampling grid was always different, i assume  
there is no temporal autocorrelation or effects
of repeated measures.
So, i would then fit a model like this:

model1 <- lmer(Outcome ~ Var1+Var2+...+(1|sampling date),  
correlation=corXXXX(1,form=~x+y), data=data, REML=false)
(alternatively also as interaction between the fixed effect).

Assuming that i have normally distributed outcomes (which i dont), is  
this a proper approach?

Alternatively, i could fit a model for each of the six sampling dates  
independently, and not use random effects at all.

Thank you!