I am working on a data set that aims to test various treatments with respect to vegetation regeneration. The experiment is replicated at many sites across a large geographical gradient and has been measured over multiple years.
So far so good. Normally I would go ahead and use a (generalized) linear-mixed model approach in which I would set
Location as random effects and
treatment as fixed effect. However there is one twist and that is that one treatment level in the experiment consists of testing plant seed mixes which are native to the individual location (location being the random effect in my model). So the locations do not share the same seed mixes for that specific treatment level. Or in other words that particular treatment level is not the same across locations but instead was adjusted for the given regions.
Is this "not the same across locations" a problem when using
Location as a random effect in my model? I have been scratching my head now for a while.
My question is:
Is it reasonable to use
Location as a random effect and only run a single model and being able to say whether seed mix is significantly different from the other treatments in general - or should I subset the data by
Location and run separate models on each location instead and interpret the results on a location by location basis?
The problem with the individual approach is that it would cut down the number of samples per treatment level.