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

摘要

We study a hierarchical dynamic state-space model for abundanceestimation. A generic data fusion approach for combining computersimulated 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 sheriesdependent information is mediated. Direct total stock abundance es-timates are obtained without the need to estimate any intermediateparameters such as catchability and mortality. Numerical results of asimulation study show that our method provides a useful alternativeto existing methods. We apply the method to data from the BarentsSea Winter survey for Northeast Arctic cod (Gadus morhua). The re-sults based on our method are comparable to results based on currentmethods.