Fuzzy rule-based models are more transparent than their statistical counterparts. Their suitability to develop universal approximators facilitates the development of first pass predictive models. They can combine quantitative and qualitative information typically embedded in human knowledge. They are well suited to modeling complex systems when the nature of the data is incompatible with the time and effort required by a more sophisticated statistical approach. The availability of stable freeware, abundant literature and a growing interest among fishery researchers are strong incentives to consider fuzzy logic as a complement to more established statistical methodologies in fishery management.

In an FRDC funded project (2005/047) fuzzy logic was used to capture fisher’s knowledge for the purpose of developing a local model of fishing power in the Timor Reef Fishery (TRF). A fuzzy expert system of intrinsic vulnerability of commercial species to fishing pressure was implemented from the literature. Results obtained for the main target species of the TRF were validated by international experts.