Hello
I am trying to estimate additive genetic variance in fitness by specifying a zero inflated poisson mixed linear model.
The approach that I want to adopt with brm was designed with MCMCglmm (Bonnet et al. 2022). In the paper they specify a residual dispersion effect for both components of the zero inflated poisson model:
“all models contained a residual dispersion random effect, with a variance estimated for the Poisson component, a variance fixed to 1 for the zeroinflation component, and a covariance between the two components fixed to 0”
Since I am not yet very familiar with the brms syntax, I wanted to ask two questions:

Is it possible to specify this prior property (fix the residual variance for the Bernoulli trait to 1) using the brm framework?

How can I interpret the variance of the random effect estimated by my model (estimated with VarCorr)? Does this variance correspond to the additive genetic variance?
This is what my model looks like and how I infer the variance estimate:
brm_zip_va < brm(total_seeds ~ 1 + site+ (1  gr(ID, cov = m)),
data = fitness,
data2 = list(m = m),
family = zero_inflated_poisson(),
chains = 8, cores = 8, iter = 1000,
)
va_ID < (VarCorr(brm_zip_va, summary = FALSE)$ID$sd)^2
Any help and suggestions is very much appreciated.
I am not very much experienced with priors and modelling with brm.