Modelling time-varying growth in state-space stock assessments

State-space assessment models (SSMs) have garnered attention recently because of their ability to estimate time variation in biological and fisheries processes such as recruitment, natural mortality, catchability, and selectivity. However, current SSMs cannot model time-varying growth internally nor accept length data, limiting their use. Here, we expand the Woods Hole Assessment Model to incorporate new approaches to modelling changes in growth using a combination of parametric and nonparametric approaches while fitting to length and weight data. We present these new features and apply them to data for three important Alaskan stocks with distinct data and model needs. We conduct a "selftest" simulation experiment to ensure the unbiasedness and statistical efficiency of model estimates and predictions. This research presents the first SSM that can be applied when length data are a key source of information, variation in growth is an essential part of the dynamics of the assessed stock, or when linking climate variables to growth in hindcasts or forecasts is relevant. Consequently, the state-space approach and growth estimation can be applied to more fish stocks worldwide, facilitating real-world applications and implementation of simulation experiments for performance evaluation of SSMs for the many stocks whose assessments rely on length data.