Spencer: (warning: highly biased, personal opinions) My $.02
Looking now at your output, I notice that "Corr" between "(Intercept)" and "trust.cz1" for the "Random Effects" "commid" is 1.000. This says that the structure of your data are not adequate to allow you to distinguish between random effects for "(Intercept)" and "trust.cz1" for "commid", while simultaneously estimating all the fixed effects you have in the model.
Quite right. Design is the cause; overfitting/identifiability is the symptom.
If I were you, I'd start be deleting all the terms from the model that don't have a "Signif. code" beside it in the table of "Fixed effects" and then refit the smaller model, preferably also using 'method="AGQ"'.
Well, this might work, but it's also a prescription for overfitting a highly biased model. What he really needs to do is carefully rethink. What is a parsimonious model given the data at hand? Unfortunately, this is far from a trivial issue. Model choice for nonlinear model fitting is conceptually and statistically difficult. IMHO, the tendency to overfit mechanistically motivated models with insufficient, poorly designed data is a ubiquitous scientific practice, rarely understood by scientists (due to the complexity). As a result, there are a lot of questionable results published in peer-reviewed literature. Eventually it gets sorted out, but it can take a while. See Kuhn and Feyerabend, for example. Always enjoy your comments. Keep 'em coming. -- Bert