Wildlife management is generally carried out under conditions of uncertainty. The exact population size is unknown, its future dynamics are uncertain and clear management objectives are often not formulated. In order to provide management advice in this situation, a framework is presented for combining different sources of information using a Bayesian approach for calibrating a management model. Harvesting strategies can then be explored based on predictions of future populations size and structure which incorporate parameter uncertainty. This method makes it possible to evaluate the probability of achieving certain objectives with different management strategies. The advantage of the approach presented in this paper lies in that both the model and the harvesting strategies are adaptable to any particular population of interest. The approach is illustrated for two Scottish red deer populations for which culling strategies corresponding to different management objectives are explored and their benefits evaluated. It is found that each population requires different culling rates for keeping population number stable, demonstrating the benefits of the population specific calibration of the management model.