Sea-level index of recruitment variability improves assessment model performance for sablefish Anoplopoma fimbria

Abstract

Environmental recruitment indices may improve the precision of stock assessments, allow hindcasting, and aid in near-term forecasting. We used Bayesian dynamic factor analysis (DFA) to find common trends in sea level from 16 tide gauges spanning the US West Coast. We then used these dynamic factors as predictors of sablefish Anoplopoma fimbria recruitment deviations from the 2021 assessment. We evaluated the ability of the resulting northern sea-level index (north of Cape Mendocino, ∼40°N) to inform recruitment estimates and its impacts on assessment model predictions by running two hindcast stock assessment models: (1) a catch-only model, which assumed average recruitment from the stock–recruit relationship, and (2) a catch plus sea-level model. In both cases, survey data were removed from 2011 forward. The model including sea-level index captured the observed increase in stock biomass from 2016 onwards, while the catch-only model did not, predicting a continued biomass decline. This work provides evidence of the potential to improve forward-looking stock projections by better capturing stock trends, providing an advance over average recruitment assumptions.