Although the concept of bird mortality (or survivorship) changing with age is not new in ecology, its estimation remains limited. Traditional methods to calculate age-specific survivorship in birds and other groups rely on the availability of data on individual ages. This demographic trait is usually calculated from mark-recapture techniques and is constrained to those individuals for which at least the birth year (right-censored) is known and, when possible, those for which the year of death has been recorded (uncensored). As expected, this reduces considerably the number of capture histories that can be used if a large proportion of the dataset consists of birds first captured as adults (left-censored) with no information on their birth and death years. Thus, the inference process requires a modeling structure that can deal with this joint probability of unknowns: age for left-censored individuals; and the age-specific mortality rate, while using the information provided by the capture histories for uncensored individuals. Moreover, a hierarchical structure is required when recapture probability is low and when juvenile mortality is different from adult mortality. Here we developed a hierarchical Bayesian approach that assumes a parametric form for the age-specific mortality rate and that allows the estimation of ages for left-censored individuals while allowing for different sources of uncertainty. We applied this model to estimate age-specific survivorship and detection probability for a colony of sooty terns at the Dry Tortugas National Park.