The index of abundance and density prediction of albacore (Thunnus alalunga) in the eastern Indian ocean using dependence survey

Worldwide, the management of fish stocks is based on stock assessment models. One of the most critical inputs in most stock assessment models is a relative abundance index of the species of interest. The main problem in determining the abundance index occurs in a dependence survey where the catchability covariates are very influential on a species abundance index to cover the actual reality in nature. This study uses the Vector Autoregressive Spatiotemporal Model (VAST) on Albacore species in the Indonesian longline tuna fisheries in the Eastern Indian Ocean. The results indicate that the resulting abundance index is better with low residuals, excluded catchability, and included habitat covariates make the results better than the conventional GLM model. The population density is well illustrated in the VAST model, where the VAST model can impute the population density in unfished areas to obtain a weighted area index. It is a distinct advantage considering many unfished areas in our research survey. This information is expected to benefit stakeholders in decision-making in the field.