Analysis of non-linear relationships between catch per unit effort and abundance in a tuna purse-seine fishery simulated with artificial neural networks

Abstract

Abstract A simulation study, combining grid- and individual-based approaches, was conducted to analyse the shape of the relationship between catch per unit effort (cpue) and abundance in a tuna purse-seine fishery. To understand the effect of fleet dynamics on the interpretation of cpue, the decision-making process used by fishers while searching for the resource is modelled with artificial neural networks. The cpue of fishers operating independently (i.e. individuals) vs. fishers sharing information (i.e. a code-group) is compared, accounting for different environmental scenarios. The results show that a power curve non-proportional relationship between cpue and abundance performs better than a linear relationship. As the shape parameter of the power curve for the code-group fishers was lower in every scenario than that of individual fishers, we conclude that hyperstability, a phenomenon commonly observed in schooling fisheries, is mainly attributable to information exchange among vessels. Setting the individual-level state variables of the virtual system at a specific spatial and temporal scale may affect the results of the simulations.