Impact of CPUE index weighting on stock status indicators of Indian Ocean albacore Thunnus alalunga
Article
Lin, Q, Geng, Z, Zhu, J et al. (2022). Impact of CPUE index weighting on stock status indicators of Indian Ocean albacore Thunnus alalunga
. 31(6), 1522-1532. 10.12024/jsou.20210603481
Lin, Q, Geng, Z, Zhu, J et al. (2022). Impact of CPUE index weighting on stock status indicators of Indian Ocean albacore Thunnus alalunga
. 31(6), 1522-1532. 10.12024/jsou.20210603481
The data weighting for eatch per unit effort (CPUE) is essential for integrated fisheries stock assessments. An Age-Structured Assessment Program (ASAP) was developed using fishery-dependent and fishery-independent data of Indian Ocean albacore (Thunnus alalunga). An operating model (OM) was developed to mimic the population dynamics and fishing operations, and estimation models (EMs) based on the Statistical-Catch-At-Age model were used to compare the effect of different CPUE weighting scenarios on the estimation of population attributes. To investigate the confounding impact of misspecification of key model parameters with the CPUE weighting, nine combinations of natural mortality (M) and the steepness (h) of the Beverton-Holt stock-recruit relationship were considered in the EMs. The results showed that when M and h were correctly specified in the EMs, assigning higher weightings on CPUE indices with higher precision or longer time series would lead to better model performance in terms of both median relative errors (REs) and relative root mean square error (RMSE). Meanwhile, putting more weighting on the CPUE indices with higher uncertainty world lead to significantly over-estimated fishing mortality and a low accuracy in spawning stock biomass. Therefore, when using multiple sets of CPUE time series, assigning higher weights to CPUE indices with higher precision or longer time series may improve the accuracy of stock status indicators. Furthermore, the reliability and uncertainty of some key parameters (e. g., M and h) should also be taken into account when weighting CPUE; at least a sensitivity analysis should be conducted to cover possible uncertainty of the model or parameter misspecification and its associated CPUE weighting.
