Using a multivariate auto-regressive state-space (MARSS) model to evaluate fishery resources abundance in the East China Sea, based on spatial distributional information

The abundance index (AI) is a representative indicator used to assess the state of fishery resources. Conventional AI is generally calculated by summing the catch per unit of effort (CPUE) weighted by the size of each fishing area. However, CPUE data has many missing values owing to annual changes in operational fishing areas, and this can lead to a considerable bias in the estimated AI. To obtain an unbiased AI, a multivariate auto-regressive state-space (MARSS) model was used to estimate and interpolate missing values in a spatially arranged, long-term bottom-trawl CPUE dataset for yellow seabream Dentex hypselosomus and largehead hairtail Trichiurus japonicus in the East China Sea. As expected, increasing the number of analyzed fishing grids improved interpolation accuracy, but remarkably increased the time required for the analysis. Reducing the maximum number of expectation–maximization (EM) iterations in the maximum likelihood procedure was an effective way to practically reduce analysis time, while keeping the accuracy of the estimation. Thus, this EM-reduction MARSS model was applied to the entire CPUE datasets of yellow seabream and largehead hairtail to address the annual shifts in their AIs and their seasonal migration.