Unresolved PIRLS step-halvings error with Imer4 (binomial)

Question

I am trying to run a model selection anlysis (with MuMIn and AICc) based on a set of mixed effects models with a binomial distribution family. My data looks like this (sorry for not posting it more elegantly)

head of data

And the saturated model (out of 38 combinations) is this:

model1 <-glmer( occ~Habitat*NDVI_centre+Dist_Paths+Dist_Power_Lines+Dist_relocation + (1 | Individual) , family=binomial , data=g.hab, control=glmerControl(optimizer="bobyqa"),nAGQ=10)

Some of the models run smoothly but about half of the models give me this error:

Error in pwrssUpdate(pp, resp, tol = tolPwrss, GQmat = GHrule(0L), compDev = compDev, : (maxstephalfit) PIRLS step-halvings failed to reduce deviance in pwrssUpdate

I wasn't able to find a solution for this and will appreciate any guidance.

hopefully useful info :

1. all variable were centered
2. the same parameters (indp var) were used in a model with a different binomial response variable and worked fine.
3. the dataset is large (44000 observations)

In some more details:

The full dataset can be found here

The following models showed no error (just a couple examples):

`model38<-glmer( occ~Habitat + (1 | Individual) , family=binomial , data=g.hab, control=glmerControl(optimizer="bobyqa"),nAGQ=10)
 model33 <-glmer( occ~Habitat+Dist_Paths + (1 | Individual) , family=binomial , data=g.hab, control=glmerControl(optimizer="bobyqa"),nAGQ=10)
model8 <-glmer( occ~Habitat+Dist_Paths+Dist_Power_Lines+NDVI_centre + (1 | Individual) , family=binomial , data=g.hab, control=glmerControl(optimizer="bobyqa"),nAGQ=10)

` But these showed the above error :

model3 <-glmer( occ~Dist_Paths+Dist_Power_Lines+Dist_relocation+NDVI_centre + (1 | Individual) , family=binomial , data=g.hab, control=glmerControl(optimizer="bobyqa"),nAGQ=10)
model1 <-glmer( occ~Habitat*NDVI_centre+Dist_Paths+Dist_Power_Lines+Dist_relocation + (1 | Individual) , family=binomial , data=g.hab, control=glmerControl(optimizer="bobyqa"),nAGQ=10)

`

Thank you very much