A new approach to the analysis of stock-recruitment relationships: "model-free estimation" using fuzzy logic

Abstract

In response to the need for overt recognition of uncertainty in management of natural resources, we present a new, innovative, causal approach for analysis of stock-recruitment relationships and prediction of recruitment. Applying principles and techniques developed from the theory of fuzzy sets, we demonstrate how heuristic reasoning can be used to define stock-recruitment relationships, explicitly characterise vagueness and uncertainty, and provide a functional relationship that combines stock size and past recruitment to predict future recruitment. The approach is termed model-free estimation or approximation. Tested on eight stock-recruitment data sets, there was no significant difference between recruitment predicted by the fuzzy approximation method and the Ricker or Beverton-Holt recruitment functions. We account for effects of nonstationarity by incorporating rules that relate past recruitment to future recruitment in the fuzzy stock-recruitment system. A weighting factor, w, represents the degree of belief in the importance of past recruitment and stock size in predicting future recruitment. The approach is robust with respect to the number of fuzzy sets used to define data clusters, can be tailored to individual circumstances, and thrives in data-poor situations where analytical methods may be inappropriate. It is a simple and broadly applicable solution with important implications for fish stock assessment and fisheries management in general.