Haikun Xu1
1SAFS
November 14, 2017 9:00 (PST): FSH 203
Improving stock assessments from two perspectives: time-varying selectivity and data weighting
In this talk, I will present my recent research work regarding time-varying selectivity and data weighting in stock assessments. Selectivity is a key process in stock assessments and is typically varies with both age and time. To account for autocorrelated variation in selectivity, a common phenomenon in fisheries, I developed a new semi-parametric age- and time-varying selectivity method and implemented it in Stock Synthesis. Simulation experiments showed that the new selectivity method can improve the precision of model estimates from a data-rich assessment, given that the true deviations in selectivity are 2-dimensional (age and time) autocorrelated. Using the data for North Sea herring (Claupea harengus) as a Stock Synthesis case study, I found that the deviations in fishery selectivity were highly autocorrelated across both age and time, and the new selectivity smoother improved model fit and reduced the pattern in Pearson residuals for age composition. In the second part, I compared the three existing data-weighting methods (McAllister-Ianelli, Francis, and Dirichlet-multinomial) in Stock Synthesis to offer users good practices for weighting composition data when the new age- and time-varying selectivity feature is turned on. Simulation results suggested that when selectivity varies over time, the Dirichlet-multinomial method performs best in terms of weighting composition data. Moreover, it can be combined with the new age- and time-varying selectivity feature to simultaneously weight composition data and penalize selectivity variation inside the model.