Generalized Mixed Model with repeated measurements

Question

I’ve have been working with a mixed model (glmmTMB) to analyse the abundance of snails in dependency of several categorical predictors. The data was measured twice in the same sample sites in two different years (YEAR) and at 11 different farms (FARM).

So far, I have been using YEAR and FARM as random effects, but the number of two levels seems not sufficient for a random effect, as it has been discussed by several authors, e.g., http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#should-i-treat-factor-xxx-as-fixed-or-random.

I’ve come across the recommendation to use a low-level explanatory factor as a fixed effect instead, however, as I have done a repeated measurement in the same sample sites (YEAR), I think this would violate the assumption of independence of observations. I have been looking for alternative ways to handle this, but without success so far, so any advice or corrections would be highly appreciated.

Answer 1

Based on your description, I would expect correlations on the farm level, i.e., measurements in the same farm will be correlated. And then on the site level, i.e., measurements within the same site will be correlated. Sites seem to be nested within farms. Hence, you could see if nested random effects are needed. More on the model-specification in lme4 and glmmTMB in these cases here: https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#nested-or-crossed

Are measurements in the same year but from different sites and farms expected to be correlated? If not, then you need to include year only as fixed-effects. Otherwise, it could be included as a random factor.