Abstract

Conventional global production models are not suitable for certain stocks, because fishing effort variations explain only a part of the total variability of annual catches. Often the residual variability originates from the influence of environmental phenomena, which affect either the abundance or the catchability of a stock from one year to the next. Therefore, an additional environmental variable has been inserted into conventional models in order to improve their aeeuracy. This variable appear in simple formulae concerning either stock abundance, or the catchability coefficient, or both. The models were developed from Schaefer's linear production model, Fox's exponential model or Pella and Tomlinson generalised model. CLlMPROD is an experimental expert-system, using artificial intelligence, which provides a statistical and graphical description of the data set and helps the user to select the model corresponding to his case according to objective eriteria. The software fits the model to the data set using a non-linear regression routine, assesses the fit with parametric and non-parametric tests, and provides a graphical representation of the results. Limitations of this kind of model are considered. The models can provide a fairly good interpretacion of fishery history, particularly when a stock collapses unexpectedly without any appreciable increase in the nominal fishing effort. These models can also pro vide a useful tool efficient management of a fishery in those instances where climatic phenomena can be forecast, or when their influence is restricted to the year(s) preceding exploitation. Finally, the ana- * Trabajo presentado en las X Jornadas en Pesquerias Chilenas, UCV, noviembre de 1992, Valparaiso.

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