DHARMA to detect overdispersion in negative binomial

I'm new to negative binomial GLMMs and still trying to get a hold of checking my residuals. DHARMa has been a huge help, but I still am having some inconsistent results. I am looking at three groups of predictor variables (insects, vegetation, and environmental) and attempting to discover the parameters that most impact bat activity (count data with an offset for # of survey days and random variable for Site). My scaled and centered dataset looks something like:

 $$Year : Factor w/ 2 levels "2017","2018"$$ Reampled         : Factor w/ 2 levels "No","Yes"
$$Habitat : Factor w/ 4 levels "MCF","MM","MMF","REGEN"$$ Site             : Factor w/ 63 levels "MCF_001","MCF_002",...
$$Disturbed : Factor w/ 2 levels "Disturbed","Mature"$$ Species          : Factor w/ 3 levels "MYLU","MYSE", "NoID"
$$Count : int 1 3 0 1 0 0 6 38 3 43 ...$$ Bat.Survey.Nights: int  4 4 4 5 5 5 6 6 6 4 ...
$$Avg.Coleoptera : num -0.201 -0.201 -0.201 -0.336 -0.336 ...$$ Avg.Diptera      : num  -0.33 -0.33 -0.33 -0.579 -0.579 ...
$$Avg.Hemiptera : num -0.42 -0.42 -0.42 -0.42 -0.42 ...$$ Avg.Hymenoptera  : num  2.91 2.91 2.91 -0.497 -0.497 ...
$$Avg.Lepidoptera : num -0.448 -0.448 -0.448 -0.369 -0.369 ...$$ Avg.Other        : num  -0.448 -0.448 -0.448 -0.306 -0.306 ...
$$Avg.Trichoptera : num 0.0486 0.0486 0.0486 -0.3353 -0.3353 ...$$ Avg.Biomass      : num  -0.382 -0.382 -0.382 -0.484 -0.484 ...
$$Shannon.Weaver : num -0.6444 -0.6444 -0.6444 0.0588 0.0588 ...$$ Num.Orders       : num  0.0714 0.0714 0.0714 -1.9005 -1.9005 ...
$$Avg.Snags : num -0.855 -0.855 -0.855 1.846 1.846 ...$$ Avg.Understory   : num  -0.00715 -0.00715 -0.00715 -0.94871 -0.94871 ...
$$Avg.Midstory : num -0.352 -0.352 -0.352 0.256 0.256 ...$$ Avg.Canopy       : num  -1.061 -1.061 -1.061 0.695 0.695 ...
$$Avg.Canopy.Cover : num -0.831 -0.831 -0.831 0.506 0.506 ...$$ Perc.Dec.Dom     : num  -0.493 -0.493 -0.493 -1.095 -1.095 ...
$$Avg.Bat.Date : num -0.772 -0.772 -0.772 -1 -1 ...$$ Avg.Bat.Night.Hr : num  -0.841 -0.841 -0.841 -0.956 -0.956 ...
$$Avg.Bat.Temp : num 0.523 0.523 0.523 -0.559 -0.559 ...$$ Bat.Dist.Edge    : num  -0.882 -0.882 -0.882 -0.434 -0.434 ...
$$Bat.Elevation : num -0.743 -0.743 -0.743 -0.577 -0.577 ...$$ Bat.Moon         : num  0.665 0.665 0.665 -0.284 -0.284 ...
$$Bat.Dist.Water : num 1.075 1.075 1.075 0.951 0.951 ...$$ Bat.Water.Feat   : Factor w/ 3 levels "Lake","River", "Stream"


I ran all the models and was left with a best model, which I know, is quite complex:

glmm.nbin.all.3 <- glmer.nb(Count ~ Avg.Biomass + Num.Orders + Shannon.Weaver +
Species + Avg.Snags + Avg.Understory + Avg.Midstory + Avg.Canopy.Cover +
Perc.Dec.Dom + Avg.Understory*Species + Avg.Midstory*Species +
Avg.Canopy.Cover*Species + (Avg.Bat.Date*Avg.Bat.Temp)^2 + Bat.Elevation +
Bat.Moon + Bat.Water.Feat*Species + offset(log(Bat.Survey.Nights)) + (1|Site),
data = insect.data)


Before I found the DHARMa package, I was using a dispersion test (https://github.com/glmmTMB/glmmTMB/issues/224), which results in:

m1 <- glmmtmb.nbin.all.3

dispfun <- function(m) {
r <- residuals(m,type="pearson")
n <- df.residual(m)
dsq <- sum(r^2)
c(dsq=dsq,n=n,disp=dsq/n)
}
options(digits=4)
dispfun(m1)

dsq       n    disp
189.153 177.000   1.069


This indicates a slight overdispersion; I realize 1.06 isn't that much >1, but I wanted to double-check. However, when I look at DHARMa's output, it does not seem to indicate overdispersion. Which output should I be focusing on?

all.res3 <- simulateResiduals(glmm.nbin.all.3)
plot(all.res3)
testResiduals(all.res3)

$uniformity One-sample Kolmogorov-Smirnov test data: simulationOutput$scaledResiduals
D = 0.053, p-value = 0.6
alternative hypothesis: two-sided

$dispersion DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated data: simulationOutput ratioObsSim = 0.76, p-value = 0.7 alternative hypothesis: two.sided$outliers

DHARMa outlier test based on exact binomial test

data:  simulationOutput
outLow = 0.000, outHigh = 1.000, nobs = 207.000, freqH0 = 0.004, p-value = 0.9
alternative hypothesis: two.sided

$uniformity One-sample Kolmogorov-Smirnov test data: simulationOutput$scaledResiduals
D = 0.053, p-value = 0.6
alternative hypothesis: two-sided

$dispersion DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated data: simulationOutput ratioObsSim = 0.76, p-value = 0.7 alternative hypothesis: two.sided$outliers

DHARMa outlier test based on exact binomial test

data:  simulationOutput
outLow = 0.000, outHigh = 1.000, nobs = 207.000, freqH0 = 0.004, p-value = 0.9
alternative hypothesis: two.sided


It also doesn't ease my worries when the residuals plots differ and sometimes there is a hump shape to them (pictures below of the same model). Does anyone have any suggestions on whether this model upholds the assumptions?