Effect size in GLMM In the output of a GLMM, using a binary variable as response variable and continuous variables as explanatory variables [family = binomial(link="logit")], I obtain, for each variable, an estimate value,  standard error, a z-value and a Pr(>|z|). 
1) Is the z-value simmilar to the effect size?
2) If not, how can I obtain the effect size for each variable?
 A: 
1) Is the z-value similar to the effect size?

No, it is a Wald statistic to test the null hypothesis that the estimate is zero.

2) If not, how can I obtain the effect size for each variable?

Since this is a generalized linear mixed model, you can't calculate effect sizes such as cohen's d, but since it is a logistic model with a logit link you can report odds ratios as effect sizes. The raw coefficients are on the log-odds scale, so to calculate the odds ratios, these are just exponentiated.
A: Here is for your second question. A new function has recently been added to the package emmeans to calculate effect sizes (Cohen´s d). To use it, you will need the GLMM adjusted, the Sigma, and the df. But, you need a trick to calculate the Sigma from a Mixed Model. Let me copy here a part of the information you can find at: https://rdrr.io/cran/emmeans/man/eff_size.html
Oats.lme <- lme(yield ~ Variety + factor(nitro), 
                  random = ~ 1 | Block / Variety,
                  data = Oats)
                  
VarCorr(Oats.lme) 
# Combine variance estimates

totSD <- sqrt(214.4724 + 109.6931 + 162.5590);
emmV <- emmeans(Oats.lme, ~ Variety);
print(eff_size(emmV, sigma = totSD, edf = 5));
print(eff_size(emmV, sigma = totSD, edf = 51)

