Hi again,
To me the problem comes from that you have 98 observations for 94 levels of PPT under a Gaussian model. That way the residual variance is confounded with the variance of the random effect.
Under non-gaussian models that can be OK but here since there is no constraint on the residual variance that cannot be.
The model does get close to convergence but I would not trust it.
Simple solution: select a single observation per group and drop the PPT random effect.
Then you go back to a simple lm() and convergence should not be an issue.
This will make you loose 4 observations which should not be a big deal!