GLMM random effect conceptualization

Hello, I have a general question concerning GLMM. I would like to model animal counts as a function of characteristics of the sampling location and also characteristics of the forest fragment containing the sampling locations, with a random effect of forest fragment because there are multiple sampling sites within each forest fragment. However, the forest fragment characteristics are not replicated, such that Fragment ID has a 1-to-1 relationship with Fragment size. If I add a random effect of fragment, how is the random effect of fragment ID partitioned from the fixed effect of fragment size?

While most examples I've seen of GLMM use characteristics of the larger grouping variable that don't have a 1-to-1 relationship with group ID (either because there are multiple groups for every value of the characteristics or because there are multiple values of the characteristics for each group), I have also seen examples (published in a GLMM modeling book) that DO have a 1-to-1 relationship between group ID and group characteristic. This makes me think that I should be able to do it, too, but I would like to know, conceptually, how the model can partition effect between the random-effect of fragment ID and the fixed-effect of fragment size.