On the dynamics of commercial fishing and parameter identification

Abstract

This paper has two main objectives. The first is to develop dynamic models of commercial fisheries different from the existing models. The industry is assumed to have a well defined index of performance based on which it either invests or otherwise. We do not however, assume that the industry or firm is efficient or optimal in its operations. The hypotheses in the models are quite general, making the models applicable to different management regimes. The second is that a new approach of fitting model dynamics to time series data is employed to simultaneously estimate the poorly known initial conditions and the parameters of the nonlinear fisheries dynamics. The approach is a data assimilation technique known as the adjoint method. Estimation of the poorly known initial conditions is one of the attractive features of the adjoint method. Unlike the conventional methods, the method employed in this paper, requires relatively less data. Economic parameters were reasonably estimated without cost and price data. The estimated equilibrium biomass is very close to the maximum sustainable biomass which means open access in this case led to economic overfishing but not biological overfishing.