In stock assessments of short-lived species, De Lury depletion models are commonly applied in which commercial catches and changing catch rates are used to estimate resource abundance. These methods are applied within fishing seasons to decide when to close the fishery and can be reliable if the data show a distinct decline in response to the catch removals. However, this is not always the case, particularly when sampling error variation masks trends in abundance. This paper presents a Bayesian hierarchical formulation of the De Lury model in which data from previous years are combined hierarchically in the same stock assessment model to improve parameter estimation for future stock assessments. The improved precision in parameter estimates is demonstrated using data for the Falkland Islands' Loligo gahi squid fishery.