Howdy!
Just as you use histograms to visualize the data, you could use histograms to visualize your posterior predictive distribution. So, to start with, try using the pp_check() function with type='hist'. Note, that you may need to adjust the binwidth in the histogram. Since you are using a zero-inflated Poisson, you might also want to check the proportion of zeros. You might also use type='bars' or bars_grouped. In any case, you don’t want to use a density plot on discrete spaces.
pp_check(m_b2_zip, type="hist")
prop_zero <- function(Patch_count) mean(Patch_count == 0)
prop_zero_check <- pp_check(m_b2_zip, type = "stat", stat = "prop_zero")
pp_check(m_b2_zip, type="bars")
Once you have a more appropriate visualization, then you can better check the model.
Note - if you don’t mind coding in base R, even better plots, in my opinion, are like those that Michael Betancourt does in his case studies, similar to the code I used to make the plot in this post Improving PPC Fit in MELS model (brms) when using Multimodal Data - #5 by jd_c . Betancourt has some functions to do this type of plot here GitHub - betanalpha/mcmc_visualization_tools: Markov Chain Monte Carlo Visualization Tools but I personally haven’t had a chance yet to use all of them for plotting purposes.