Abstract
Development of data‐intensive stock assessment and ecosystem‐based models has improved our understanding of shifting species abundance in response to fishing, ocean ecology, and species interactions. Along with this analytical progress is evidence that many stocks lack data required for complex models, resulting in data‐limited options for estimating abundance and reference points for some species. Additionally, for even traditionally exploited species, historical data before the mid‐1900s is scarce, further limiting our understanding of long‐term population trends. To address this paucity of data and historical time series, we used a Bayesian Stochastic Stock Reduction Analysis (BSSRA) model to estimate historical population biomasses utilising historical catch, a population growth rate ( r ) and values of carrying capacity ( K ). These BSSRA historical biomass estimates are developed using available landings records for three traditionally exploited species—Atlantic cod ( Gadus morhua ), Atlantic halibut ( Hippoglossus hippoglossus ), and Atlantic Menhaden ( Brevoortia tyrannus ). We compared the BSSRA‐derived historical biomass trends with contemporary estimates from more data‐intensive stock assessments to evaluate the performance of this data‐limited approach. To assess shifting baseline syndrome, two different baseline years were used to evaluate how perceptions of stock status change over time and what implications this has for fisheries management. BSSRA models captured similar biomass trends to those in formal stock assessments and suggest that modern reference points may underestimate historical biomass by an order of magnitude. Integrating historical data through models like BSSRA can help set more realistic and ecologically meaningful baselines, enhancing recovery goals to the benefit of ecosystem‐based fisheries management.