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

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...

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