Catch and Length Models in the Stock Synthesis Framework: Expanded Application to Data-Moderate Stocks

Many of the world’s fisheries are data-moderate, with data types (e.g., total removals, abundance indices, biological composition data) of varying quality (e.g., limited time series or representative samples) or availability. Integrated stock assessments are useful tools for data-moderate fisheries as they can include all information available, can be updated as more information becomes available over time, and directly test the inclusion and exclusion of specific data types. This study uses simulation testing and systematic data reduction from U.S. West Coast benchmark assessments to examine the performance of Stock Synthesis with catch and length compositions only (SS-CL). Simulation testing of various life history, recruitment variability, and data availability scenarios found that correctly specified SS-CL can estimate unbiased key population quantities such as stock status with as little as one year of length data, although five years or more may be more reliable. Error in key population quantities decreased with increasing years and sample size of length data. Removing length compositions from benchmark assessments often caused large model deviations in outputs compared to removing other data sources, indicating the importance of length data in integrated models. Models with catch and length data, excluding abundance indices and age composition, generally provided informative estimates of the stock status relative to the reference model, with most data scenarios falling within the confidence intervals of the reference model. The results of simulation analysis and systematic data reduction indicated that SS-CL could be viable for data-moderate assessments in the U.S., thus reducing precautionary buffers on catch limits for many stocks previously assessed in a lower tier with catch-only models. SS-CL could also be applied to many stocks around the world, maximising the use of data available via the well-tested, multi-feature benefits of SS.