quasi-complete seperation in logistic regression

Hello; I am working on a logistic regression model in which I have
quasi-complete separation on an explanatory variable (see table
below).  The response variable is Success of parrot reintroductions,
and one of the explanatory variables is PredThreat, a 3 category
variable designating the level of predator threat to the population.
When I fit the univariate logistic regression model Success ~
PredThreat, I get a huge standard error, which I believe is an
indication of the optimization algorithm failing due to
quasi-complete separation.  I am testing a variety of models using
information-theoretic model selection to judge which variables are
important to reintroduction success.  My question concerns what to do
to about PredThreat, since it appears to be an informative variable.
First I'm wondering if I can trust the AIC value calculated from the
model with PredThreat?  Second, to get at an effect size and also to
include it in multivariate models, I thought of treating PredThreat
! as a continuous variable.  When I do that in the univariate model,
I get a more reasonable parameter estimate and standard error, but a
much lower AIC.  I'd really appreciate any insights in how to deal
with this problem.