Issue with boundary (singular) fit: see ?isSingular

Hi,

I'm running a piecewise SEM with 3 component models:

lmer(response variable1 ~ predictors + (1|Point) + (1|Year), input_table)

glmer(response variable2 ~ predictors + (1| Point) + (1|Year), family =
"binomial", input_table)

glmer(response variable3 ~ predictors + (1| Point) + (1|Year), family =
"binomial", input_table)

Because sampling involved visiting 18 points in spring of 2018 and again in
spring of 2019, I specified samping point and year as random effects.

When I run the model, this warning message appears:
Check model convergence: log-likelihood estimates lead to negative
Chi-squared!

This message also appears:
boundary (singular) fit: see ?isSingular

 From what I've read about the second message, it could be due to random
effect variance estimates of zero. I checked and this happens in the 1st
and 3rd component models. In the 1st model "Point" has zero variance, and
in the 3rd model "Year" has zero variance.

My first question is (and I apologize in advance if this is silly to ask)
whether this means that there's not really an effect coming from Point in
component model 1 and from Year in component model 2? If so, would it be
possible to remove those random effects to end up with:

lmer(Response variable1 ~ Predictors + (1|Year), input_table)

glmer(Response variable2 ~Predictors + (1| Point) + (1|Year), family =
"binomial", input_table)

glmer(Response variable3 ~ Predictors + (1| Point), family = "binomial",
input_table)

My second question is whether the warning "Check model convergence:
log-likelihood estimates lead to negative Chi-squared!" is related to these
singularity issues?

Oh and I am using the development version of the piecewise SEM package
installed using devtools. This is because this version provides additional
standardized coefficients for GLMM.


Thanks!