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I have looked around on cross validated as well as other places but can't seem to find an answer. I'm running a generalized linear mixed-effects model.
Y~initial abundance + Treatment + (1|Month)
Where Y is count data (abundance of prey) Initial abundance is a covariate of initial abundances before any treatments Treatment is a factor of two levels (control and treated) Month is a random intercept due to repeat sampling
The context of this model is an experiment where I reduced a predator in "treated plots" and left the other plots as "controls". Prior to the start of this experiment baseline sampling was done in all plots. I realized some plots natural had higher abundances of prey than other plots. So I decided to use these initial abundances as a covariate to control for the natural unequal abundances between plots. Here is the question: Should this covariate be included as a random effect versus a covariate that's a fixed effect? My understanding is that it could be technically be used as either.
I used it as a covariate because I know that if more prey are abundant than the effects of reducing a predator would most likely be higher than in a poorly abundant plot. It was something that I knew would have an effect on my response of prey abundance. However, I don't know if this reasoning is justified. Any info or opinions would be welcomed!