Including sex in interaction but no as a main effect in a conditional logistic regression I'm running a conditional logistic regression to assess habitat selection in coyotes. The conditional logistic regression essentially compares the environmental covariates of used locations (1's) to semirandomly generated "available" locations (0's) which are matched in strata to determine if certain covariates are more closely associated with use. These strata account for the fact that each available location is associated with a used location.
I'm particularly interested in whether the selection for areas of high-human density changes based on other variables like the amount of natural habitat in a 1 km radius. I'd also like to include sex in the model, but because of the conditional nature of the model sex never varies between used and available locations in the same strata and therefore the effect can't be estimated.
Does it makes sense in this case to include sex in an interaction but not as a main effect?

This is a limitation of conditional logistic regression and other fixed effects models for analyzing paired/stratified data: you cannot estimate the effect of between-stratum variables (i.e., variables that do not vary within strata) on the outcome. An alternative is to use generalized linear mixed models, i.e., models with random effects for stratum membership. You can include stratum-level predictors in the model and examine their relationship with the outcome while accounting for stratum membership using a random effect. In R this can be accomplished using lme4::glmer().
