GLMMs for count data shows significant deviation according to DHARMa diagnostics qqplot I am making a GLMM to test for various aspects of grooming in primates. I have made 3 different models for the different species. For 1 of the 3 models, I am getting an issue with the qqplot.
The data fits well with poisson distribution according to diagnose() in glmmTMB (I also ran it with nbinom2 - gives errors, poisson does not).
m3 <- glmmTMB(Groom_giv ~ Grph+Receiver_rank+Rank_diff+
             (1|Group)+(1|Actor)+(1|Receiver)+
             offset(log(Hours)),
           family=poisson,
           data = ER_M)

But the qqplot in DHARMa shows deviation is significant according to both the KS test and the dispersion test.

Going through the FAQs page I found that this might be indicating Underdispersion. And looking at he coefficient estimates I believe it might be the case.
 Family: poisson  ( log )
Formula:          Groom_giv ~ Grph + Receiver_rank + Rank_diff + (1 | Group) +      (1 | Actor) + (1 | Receiver) + offset(log(Hours))
Data: ER_M

     AIC      BIC   logLik deviance df.resid 
   275.0    293.5   -130.5    261.0       97 

Random effects:

Conditional model:
 Groups   Name        Variance  Std.Dev. 
 Group    (Intercept) 2.577e-12 1.605e-06
 Actor    (Intercept) 6.516e-11 8.072e-06
 Receiver (Intercept) 1.261e-10 1.123e-05
Number of obs: 104, groups:  Group, 4; Actor, 32; Receiver, 32

Conditional model:
                Estimate Std. Error z value Pr(>|z|)    
(Intercept)   -1.9341019  0.1878505 -10.296   <2e-16 ***
Grph           2.0189943  0.1982866  10.182   <2e-16 ***
Receiver_rank -0.0018369  0.0178349  -0.103    0.918    
Rank_diff     -0.0002208  0.0233696  -0.009    0.992    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

I am new to GLMMs, can someone suggest how I should proceed with this model?

Maybe exploring a Quasi-Poisson regression might solve the issue. Nevertheless, be aware that, contrary to overdispersion which is a common problem with methods to deal with, underdispersion might have other impacts and implications about your data.
Take a look at this question for several links that may help you.
