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In marine management, fish stocks are often managed on a stock‐by‐stock basis using single‐species models. Many of these models are based upon statistical techniques and are good at assessing the current state and making short‐term predictions; however, as they do not model interactions between stocks, they lack predictive power on longer timescales. Additionally, there are size‐based multi‐species models that represent key biological processes and consider interactions between stocks such as predation and competition for resources. Due to the complexity of these models, they are difficult to fit to data, and so many size‐based multi‐species models depend upon single‐species models where they exist, or ad hoc assumptions when they do not, for parameters such as annual fishing mortality. In this paper, we demonstrate that by taking a state‐space approach, many of the uncertain parameters can be treated dynamically, allowing us to fit, with quantifiable uncertainty, size‐based multi‐species models directly to data. We demonstrate this by fitting uncertain parameters, including annual fishing mortality, of a size‐based multi‐species model of the Celtic Sea, for species with and without single‐species stock assessments. Consequently, errors in the single‐species models no longer propagate through the multi‐species model and underlying assumptions are more transparent. Building size‐based multi‐species models that are internally consistent, with quantifiable uncertainty, will improve their credibility and utility for management. This may lead to their uptake by being either used to corroborate single‐species models; directly in the advice process to make predictions into the future; or used to provide a new way of managing data‐limited stocks.

fish and fisheries

Quantifying uncertainty and dynamical changes in multi‐species fishing mortality rates, catches and biomass by combining state‐space and size‐based multi‐species models