I have a question about the interpretation of residual diagnostics using DHARMa.

I fitted a binomial mixed model and used DHARMa for model diagnostics.

    simulationOutput <- simulateResiduals(m1_test, 
                                  n = 1000, 
                                  seed = 123)
    plot(simulationOutput)
    testResiduals(simulationOutput)

This is what the DHARMa plots look like:  

Given that I have a lot of data (n = 9587) and according to the DHARMa vignette there will very likely be significant patterns with large sample sizes, the plots look pretty good to me. However, I'm not sure if I should be concerned about underdispersion since the dispersion test yields a dispersion parameter of 0.80552:

The DHARMa vignette suggests that e.g. a dispersion parameter of 5 is reason for concern about overdispersion, but I cannot find anything about when a value indicating underdispersion should be taken seriously. Should I worry about underdispersion or is it fine? I also plotted the residuals against individual predictors. There are some significant deviations, but nothing outstanding that would point to large deviations.

I have a question about the interpretation of residual diagnostics using DHARMa.

I fitted a binomial mixed model and used DHARMa for model diagnostics.

library(DHARMa)
simulationOutput <- simulateResiduals(m1_test, n = 1000, seed = 123)
plot(simulationOutput)
testResiduals(simulationOutput)

This is what the DHARMa plots look like: 

Given that I have a lot of data (n = 9587) and according to the DHARMa vignette there will very likely be significant patterns with large sample sizes, the plots look pretty good to me. However, I'm not sure if I should be concerned about underdispersion since the dispersion test yields a dispersion parameter of 0.80552:

The DHARMa vignette suggests that e.g. a dispersion parameter of 5 is reason for concern about overdispersion, but I cannot find anything about when a value indicating underdispersion should be taken seriously. Should I worry about underdispersion or is it fine? I also plotted the residuals against individual predictors. There are some significant deviations, but nothing outstanding that would point to large deviations.