I am setting up models of the diversity data (Shannon Index) of bees and hoverflies to find out which in which studied seed mixture (site) the diversity was higher. In addition, I would like to know if this was influenced by the diversity of the plant species or by the flower cover of these. I added the site and the field visits (3 times) as a random effect, because they were chosen randomly and I do not expect a direct effect on the diversity, or this is not of interest.
Since I am not very familiar with model validation and there is no clear explanation online how to interpret the results of a test for overdispersion or zero inflation, I would like to pray for help.
What I have done so far:
my models basically look like this:
lmer(H_bee ~ seedmixture + H_plant + blueh_deck + (1|location) + (1|fieldvisit), data = insect_plant, REML = FALSE).
Some Information: There are two seed mixtures (RH and WD), I have visited 4 locations and those ones in 3 different month (fieldvisit).
Results of the Model
summary(lmer_H_schweb) Linear mixed model fit by maximum likelihood ['lmerMod'] Formula: H_bee ~ seedmixture + H_plant + blueh_deck + (1 | location) + (1 | fieldvisit) Data: insect_plant AIC BIC logLik deviance df.resid -87.2 -64.3 50.6 -101.2 189 Scaled residuals: Min 1Q Median 3Q Max -1.3061 -0.4246 -0.1407 0.0503 5.9872 Random effects: Groups Name Variance Std.Dev. location (Intercept) 0.002702 0.05198 fieldvisit (Intercept) 0.002588 0.05087 Residual 0.033112 0.18197 Number of obs: 196, groups: location, 4; fieldvisit, 3 Fixed effects: Estimate Std. Error t value (Intercept) 0.073241 0.060921 1.202 seedmixtureWD -0.043332 0.031068 -1.395 H_plant 0.034441 0.033140 1.039 blueh_deck -0.005288 0.005611 -0.943 Correlation of Fixed Effects: (Intr) StgtWD H_plnt SaatgutWD -0.544 H_plant -0.582 0.341 blueh_deck -0.398 0.433 0.012
I used the DHARMa Package to test for zero-inflation and also overdispersion. The overdispersion Graph looks quite good to me, but the zero-inflation is a bit confusing...
I also used the plot() function with the simulationOutput. Thats the point where I thought that the models are not well. First of all there is the note "Quantile deviations detected" and further more the QQ plot has that big step at the end to another bunch of Datapoints in the plot. Also the red line is not fitting the black points, as I know it from other models.
Zero inflation test
I would be so thankful to get some advice, whether this model is reliable, or how I could get the problem under control. If it were a Poisson distribution, I would switch to a negative binomial distribution. However, I have not found a solution for this in the lmer models.