Using mental models to quantify linear and non-linear relationships in complex fishery systems

Reducing uncertain outcomes of fishery management actions requires an understanding of how fishers will be affected by policy actions. This requires integrating the local expertise of commercial fishers and seafood processors into management planning to predict how regulatory changes could impact social-ecological systems. Participatory modeling between managers, scientists, and industry can be useful in integrating different perspectives and gaining a more comprehensive understanding of how different scenarios can cause direct and indirect impacts throughout fishery systems. We used group Fuzzy Cognitive Mapping exercises to describe the structure and dynamics of two economically important commercial fishery systems in Alaska, USA: the Bering Sea Pollock Catcher/Processor and Amendment 80 Groundfish fishery. Several components were identified by the fleets that had a relatively high number of links to other components in each system, including chum salmon catch for the pollock fleet and the deck sorting of halibut for the groundfish fleet. After identifying other critical relationships between regulatory, environmental, and operational changes, we developed a survey technique and regression analytical approach to define the shape of these relationships. Our results show both linear and nonlinear relationships between bycatch regulations and fishing operations; thus, providing evidence counter to the traditional assumption that the relationships between policy and fishing operations are always linear and constant across variable levels of change. Our novel approach permits for more specific explorations of curvilinear patterns, social tipping points, and asymptotic limits to change, thereby allowing for improved assessments of the effects of regulatory and environmental change on fishing communities.

Automated summary

Research shows that the impact of different fishery management policies on fishers and social-ecological systems depends on the interactions of various factors, and it also confirms that the relationship between regulations and fishing operations is not always linear. Participatory modeling can better integrate different perspectives of managers, scientists, and industry, thus providing a more comprehensive understanding of the direct and indirect impacts on fishery systems.