Population models are needed to assess the threats to species at risk and to evaluate alternative management actions. Data to support modeling is limited for many species at risk, and commonly used approaches generally assume stationary vital rates, a questionable assumption given widespread ecosystem change. We describe a modeling approach that can be applied to time series of length composition data to estimate vital rates and test for changes in these rates. Our approach uses stage-structured population models fit within a Bayesian state-space model. This approach simultaneously allows for both process and observation uncertainty, and it facilitates incorporating prior information on population dynamics and on the monitoring process. We apply these models to populations of winter skate (Leucoraja ocellata) that have been designated as "endangered" or "threatened." These models indicate that natural mortality has decreased for juveniles and increased for adults in these populations. The declines observed in these populations had been attributed to unsustainable rates of bycatch in fisheries for other groundfishes; our analyses indicate that increased natural mortality of adults is also an important factor contributing to these declines. Adult natural mortality was positively related to grey seal (Halichoerus grypus) abundance, suggesting the hypothesis that increased adult mortality reflected increased predation by expanding grey seal herds. Population projections indicated that the threatened population would be expected to stabilize at a low level of abundance if all fishery removals were eliminated, but that the endangered population would likely continue to decline even in the absence of fishery removals. We note that time series of size distributions are available for most marine fish populations monitored by research surveys, and we suggest that a similar approach could be used to extract information from these time series in order to estimate mortality rates and changes in these rates.