[R-sig-ME] GLMMs to identify the pure seasonal effect in a repeated measurement

[trichter at uni-bremen.de]

Dear list,

i am very new to mixed models. My data encompasses species composition  
matrices from six different time points with spatial correlation  
structure. For each species, i want to know if there is a pure effect  
by time, f.e. if abundance changes can be purely explained by time  
alone.
I used to glht() with time being a simple factor (so not accounting  
for the repetitive nature of my data), but this seems  
inapprobiate/wrong. So, i am actually trying to do:

fit <- lme(fixed=abundance ~ time, random=~1|time, data,  
correlation=corxxx(form=~x.pos + y.pos))

with time being a factor with 6 levels (a side question would be, if  
it would be better to use "time" as.time?)

Because my data is actually negative binomially distributed, i was  
advised to use glmmPQL, but this gives me only intercepts, no  
significancies or ways to compare models by log likelihood or AIC.

The basic question is, if that syntax is correct? Because i have seen  
many examples looking at interactions, but never anything where the  
only fixed predictor is also random. I do get an output, which i can  
interpret and which resembles what i can actually see from boxplots.

The overarching question is, if there are post-hoc tests for repeated  
measurements of spatially autocorrelated, non-normally distributed data.

Thank you, Tim