A State-Space Model for Abundance Estimation from Bottom Trawl Data with Applications to Norwegian Winter Survey

We study a hierarchical dynamic state-space model for abundance estimation. A generic data fusion approach for combining computer simulated posterior samples of catch output data with observed research survey indices using sequential importance sampling is presented. Posterior samples of catch generated from a computer software are used as a primary source of input data through which fisheries dependent information is mediated. Direct total stock abundance estimates are obtained without the need to estimate any intermediate parameters such as catchability and mortality. Numerical results of a simulation study show that our method provides a useful alternative to existing methods. We apply the method to data from the Barents Sea Winter survey for Northeast Arctic cod (Gadus morhua). The results based on our method are comparable to results based on current methods.