muntjac | A web application GUI toolkit

The first clue is the warning

Model convergence problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')

This means the model hasn't converged, or doesn't think it has, to a solution where the log-likelihood surface is downward-curved (i.e., a true maximum). That's why the standard errors can't be calculated (if you did the usual calculation they'd come out negative or complex). The log-likelihood could be calculated, but the model fit is suspect so glmmTMB returns NA instead.

Next question: why? Sometimes this is mysterious and hard to diagnose, but in this case we have a good clue: when you see extreme parameter values (e.g. |beta|>10) in a (non-identity link) GLM, it almost always means that some form of complete separation is occurring. That is, there are some combinations of covariates (e.g. Keyword_1==Lesser Mouse-deer) where you always have zero counts. On the log scale, this means the density is infinitely lower than covariate combinations where you have a positive mean. The parameter is about -16, which corresponds to an expected multiplicative density difference of exp(-16) = 1e-07. This isn't infinitesimal, but it's small enough that glmmTMB gets small enough differences in the log-likelihood that the optimizer stops. However, since the likelihood surface is almost flat, it can't compute curvature etc..

You could lump together or drop categories or do some form of regularization (e.g. see here or here ...); it might also make some sense to treat your Keyword_1 variable as a random effect, which would also have the effect of regularizing the estimates.