Estimating fish stock biomass using a Bayesian state-space model: accounting for catchability change due to technological progress

The assessment of trends in fish stocks using long-term time-series data is important for effective fisheries resource management. Despite technological advancements in recent decades, the resulting increase in fisheries catch potential with applied effort is often not adequately considered in stock assessments. To address this gap, we developed a framework for simultaneously estimating catchability and biomass using a state-space population model. This model allows for the flexible integration of the timing and functional form of the uptake of technological innovations that are assumed to influence catchability. Our objective was to test the effectiveness of this framework by applying it to 48 years of skipjack pole-and-line fishery data in Japanese waters. We utilized two population models, the Ricker-type and Gompertz-type, under three different scenarios of technology-driven catchability changes: constant, exponential, and S-shaped. The results indicate that the calculations converged for the constant and S-shaped scenarios, and that both the Ricker and Gompertz models performed almost equally well in terms of the goodness of fit and prediction accuracy under the S-shaped scenario, which assumes time-varying catchability. Although time-varying catchability poses challenges for accurate biomass estimation due to the large range of uncertainty, the decreasing trend in stock status is still detected. The estimated recent decline in the skipjack stock around Japan provides a warning for stock assessments that do not incorporate technological progress, despite the species’ high natural population growth rate and presumed stable stock status. Our methodology, based on publicly available archived catch records (catch and effort), can be applied to other species with known timelines of technological innovation.