R help - Dec 2011 - Stratification criteria mess up the use of a random factor?

Hi!

 In my study on kelp I want to see how wave exposure and current (both 
 continuous variables) affect kelp physical measures along the Norwegian 
 coast. To assure a balanced design, I stratified my study area into 9 
 different classes, defined by all combinations of three levels of wave 
 exposure and three levels of current and randomly selected 27 stations 
 such that all 9 combinations of the variable levels where represented 
 three times. Then 10 samples were taken at each station.

 Analyzing these data without taking into account the dependencies 
 between samples within each station will be wrong, due to 
 pseudoreplication. So I wanted to perform a mixed model, including 
 station as a random factor. But since, due to the sampling design, 
 station is strongly confounded with the variables in question, I lose 
 all my variation to this variable and nothing is left for the two 
 variables in interest, i.e. wave exposure and current.
 
 So how should I take this dependency into account? Do I have to perform 
 my analyses on averaged values from each sample, and thereby reduce 
 power and the possibility of treating wave exposure and current as 
 continuous variables? I hope not! Can I possibly do some kind of nested 
 analysis, to specify the sampling structure, without including station 
 as a random factor?

 Any help/comments on this is greatly appreciated!

 Gunda