Wildlife repatriation is an important tool to decrease extinction risk for imperiled species, but successful repatriations require significant time, resources and planning. Because repatriations can be long and expensive processes, clear release strategies and monitoring programs are essential to efficiently use resources and evaluate success. However, monitoring can be challenging and surrounded by significant uncertainty, particularly for secretive species with extremely low detection probability. Here, we simulated how alternative repatriation strategies influence repatriation success for the eastern indigo snake Drymarchon couperi, a federally-Threatened species that is currently being repatriated in Alabama and Florida. Critically, we demonstrate how observed population growth can differ from true population growth when detection probabilities are low and mark-recapture analyses are not an option. Specifically, we built a stochastic stage-based population model to predict population growth and extinction risk under different release strategies and use information from ongoing repatriations to predict success and guide future releases. Because D.  couperi is difficult to monitor, we modeled how detection probability influenced perceptions of abundance and population growth by monitoring programs. Simulated repatriation strategies releasing older, head-started snakes in greater abundance and frequency created wild populations with decreased extinction risk relative to scenarios releasing fewer and younger snakes less frequently. Ongoing repatriations currently have a 0.23 (Alabama) and 0.61 (Florida) probability of quasi-extinction, but extinction risk decreased to 0.07 and 0.10 at sites upon achieving the targeted number of releases. Abundances observed under realistic detection thresholds for D.  couperi did not always predict true population growth; specifically, we demonstrate that monitoring programs during repatriations of secretive species may indicate that efforts have been unsuccessful when populations are actually growing. Overall, our modeling framework informs release strategies to maximize repatriation success while demonstrating the need to consider how detection processes influence assessment of success during conservation interventions.