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Repeated measure comparisons: should the identity be a random or a fixed variable?

Hello everybody!

I'm relatively new at the mixed-models world and I'm facing a
theoretical/philosophical problem.
Let's go to my data collection.

I wanna compare the number of eggs laid by females (different
individuals or the same, I have no idea) at the time 1 and at the time
2 in the same location. Therefore, I have repeated measures by
location and wanna compare time 1 versus time two. Given I have count
data, to minimize the overdispersion I have considered the Poisson
distribution for the errors.
Furthermore, I have collected this data throughout one year and I'm
also interested in temporal variation among months.

model0 <- glmer ( y ~ 1 + (1|location) + (1|month), family="poisson")
model1 <- glmer ( y ~ x + (1|location) + (1|month), family="poisson")

where y = number of eggs laid,
          x = factor concerning the first or the second oviposition
          location = factor concerning the exactly position in the
space (just an identity of the oviposition site and responsible for
the repeated comparison)
          month = factor concerning the month when I've collected the data

Is that right? If I wanna repeated comparison regarding specific
identity of oviposition sites, should this factor (location) be a
random variable?

Furthermore, in both examples above, I'm just considering a temporal
variation (among months) as random a effect. But I'm also interested
if there are significant seasonal variation in the comparison (the
difference could be higher during warm season or not even existent
during cold season). Then:

model2 <- glmer ( y ~ x + month + (1|location), family="poisson")
model3 <- glmer ( y ~ x * month + (1|location), family="poisson")

Is that right too?
Finally, I'll use a model selection approach to compare the different
models and rank the most likely one to reproduce the data observed in
the nature.
Thanks to everyone