Robust Variance Estimation in Bayesian Meta-analysis BACKGROUND
I am conducting a meta-analysis using the brms in R with structure akin to the following (leaving out priors, etc. for simplicity):
brm(yi|se(sei) ~ 1 + (1 | eid), data = dat)

Here yi is the effect, sei is the standard error of that effect and eid is a unique identifier for each experiment. My understanding is that inclusion of a random effect by experiment (eid) addresses concerns related to dependencies in my data (e.g., if one experiment reports two effects from the same sample they would share the same eid). I have published several papers using this approach (sometimes with additional random effects, moderators, etc.) in the past without issue. I have just received a review stating that the use of a multilevel Bayesian model is insufficient to account for such dependencies, and I must instead apply some form of robust variance estimator (of the sort recommended by Hedges, Tipton, & Johnson, 2010). However, I am unable to find anything in the Bayesian meta-analytic literature discussing this issue. The reviewer has recommended abandoning my Bayesian approach so that I can use the robust function from the metafor package instead. I am not keen to do that (as much as I love the metafor package).
QUESTION
I was hoping someone could point me towards an article or two that either (a) discusses robust variance estimation in the context of Bayesian meta-analysis, or, (b) explains why robust variance estimation is unnecessary in the context of Bayesian meta-analysis.

Please check this article by brms' author: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173660, where they say: "...outcomes reported by the same study were explicitly modeled as correlated. In the absence of any reported correlations in the primary studies, correlations were set to r = 0.7. To investigate the influence of this decision on the obtained results, a sensitivity analysis was conducted where correlations were varied between r = 0.1 and r = 0.9."
Fortunately, they used brms to run the meta-analysis and the code is available here: https://osf.io/gs6hk/
