Although statistical population reconstruction (SPR) provides a flexible framework for estimating demographics of harvested populations using age-at-harvest data, that information alone is insufficient. Auxiliary data are needed to ensure all model parameters are estimable and to improve the precision and accuracy of the estimates. We examined the influence of two types of auxiliary information, independent estimates of annual abundance and annual harvest mortality from radio-telemetry studies, on the stability and precision of abundance estimates from SPR. Further, we evaluated whether the timing of auxiliary studies in the reconstruction affected the precision of abundance estimates. Monte Carlo studies simulated auxiliary data with precision levels defined by the coefficients of variation (CV) of 0.05, 0.125, 0.25 and 0.50 corresponding to the three levels of precision suggested by Robson & Regier (1964) for accurate research, accurate management and rough management and a minimal information scenario. For comparable levels of precision, radio-telemetry studies used to estimate harvest mortality stabilized the reconstructed population trends better than independent abundance surveys. However, independent abundance surveys were superior at improving the precision of reconstructed abundance estimates. We found that the timing of auxiliary studies did not influence the stability of SPR estimates, which has important implications for managers designing studies to collect auxiliary data. Our research highlights that different types and quality of auxiliary studies affects the precision and stability of SPR models differently.