Jason Cope

Length-only stock assessment models (i.e., models with only length composition and life history parameters) are often an entry point to estimating stock status. Such methods have expanded both in name (e.g., LB-SPR, LIME, LBB) and global application. These methods do suffer common challenges, such as assuming logistic selectivity and limited data (by fleet or sex) and parameter (females only) resolution. Stock Synthesis (SS) is a flexible stock assessment framework used around the world to develop complex stock assessments. It can also be applied to data-limited situations. Here I demonstrate that SS can be used to mimic length-only approaches, but extends those methods by using the flexibility of SS to confront the challenges of length-only models. In addition, I demonstrate a Shiny tool that makes this and other data-limited methods accessible using one tool, the SS-DL-tool. This model is under development, so comments and suggestions are encouraged.