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

Abstract

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 re- search survey indices using sequential importance sampling is pre- sented. Posterior samples of catch generated from a computer soft- ware are used as a primary source of input data through which sheries dependent information is mediated. Direct total stock abundance es- timates 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 re- sults based on our method are comparable to results based on current methods.