Stock reduction analysis using catch-at-length data: Length-SRA

Many modern stock assessments estimate age- or length-based selectivity, often using simple parametric functions describing asymptotic or dome-shaped selectivity. We present a length-based stock reduction analysis (Length-SRA), which bypasses the requirement of estimating selectivity by calculating exploitation rate at length directly from observed catch-at-length data. We test the performance of Length-SRA using a simulation-evaluation framework under three exploitation rate trajectories and under fixed and time-varying selectivity scenarios. We also explore the impacts of misspecification of growth parameters. The Length-SRA yields low bias in parameter estimates and management benchmarks and is relatively accurate when tracking changes in selectivity through time. We use Length-SRA to assess two species, Pacific hake and Peruvian jack mackerel, showing that selectivity is quite variable in both species over time, leading to time-varying management reference points. Length-SRA provides assessment results with accuracy comparable to other methods, such as Virtual Population Analysis and Statistical Catch at Age Analysis, with the additional advantage of providing estimates of selectivity over time.