I have this model below. I have five replicates (not repeated) for each month and site. I measured weight every month for 12 months. However, I get warnings about singular fit, and I read that simplifying the model could help.

site has two levels, and season has two levels.

lmer(log_weight ~ site * season + (1 | season / month))
I understand that this is same as

lmer(log_weight ~ site * season + (1 | season)+ (1|season:month))

Someone has suggested

lmer(log_weight ~ site * season + (1|month))

because of the unique months per season, but I am not sure about dropping 1|season . I know i dont need this. but the nestedness / structure is lost?

I am not sure the best and correct way to approach this.

i found this below which is helpful

R code if my random effect is nested under another random effect

but still not sure with season as a fixed effect.

I appreciate any ideas.