Modelling time-varying growth in state-space stock assessments

Abstract 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, selectivity. However, current SSMs cannot model time-varying growth internally nor accept length data, limiting use. Here, we expand the Woods Hole Assessment Model incorporate new approaches modelling changes using a combination parametric nonparametric while fitting weight data. We present these features apply them data for three important Alaskan stocks with distinct needs. conduct “self-test” simulation experiment ensure unbiasedness statistical efficiency estimates predictions. This research presents first SSM that can be applied when are key source information, is an essential part dynamics assessed stock, or linking climate variables hindcasts forecasts relevant. Consequently, state-space approach estimation more fish worldwide, facilitating real-world applications implementation experiments performance evaluation many whose assessments rely on