Assimilation of real time series data into a dynamic bioeconomic fisheries model : an application to the Norwegian cod fishery stock

Abstract

This paper combines the new and elegant technique of inverse methods and a Monte Carlo procedure to analyze real data for the Norwegian cod fishery (NCF) stock. A simple nonlinear dynamic resource model is calibrated to real time series of observations using the adjoint parameter estimation method of data assimilation and the Monte Carlo technique. By exploring the efficient features of the adjoint technique coupled with the Monte Carlo method, optimal or best parameter estimates together with their error statistics are obtained. Thereafter, the weak constraint formulation resulting in a stochastic ordinary differential equation (SODE) is used to find an improved estimate of the dynamical variable(s). Empirical results show that the average fishing mortality imposed on the NCF stock is 16 % more than the intrinsic growth rate of the biological species.