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Exploring social-ecological trade-offs in fisheries using a coupled food web and human behavior model

Anne A. Innes-Gold, Department of Fisheries, Animal and Veterinary Sciences, University of Rhode Island, Kingston, Rhode Island
Tyler Pavlowich, AIS Inc. in support of the Northeast Fisheries Science Center, Narragansett, Rhode Island
Margaret Heinichen, Graduate School of Oceanography, University of Rhode Island, Narragansett, Rhode Island
M. Conor McManus, Rhode Island Department of Environmental Management, Division of Marine Fisheries, Jamestown, Rhode Island
Jason McNamee, Rhode Island Department of Environmental Management, Bureau of Natural Resources, Providence, Rhode Island
Jeremy Collie, Graduate School of Oceanography, University of Rhode Island, Narragansett, Rhode Island
Austin T. Humphries, Department of Fisheries, Animal and Veterinary Sciences, University of Rhode Island, Kingston, Rhode Island; Graduate School of Oceanography, University of Rhode Island, Narragansett, Rhode Island

DOI: http://dx.doi.org/10.5751/ES-12451-260240

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Abstract

Marine fisheries represent a social-ecological system driven by both complex ecological processes and human interactions. Ecosystem-based fisheries management requires an understanding of both the biological and social components, and management failure can occur when either are excluded. Despite the significance of both, most research has focused on characterizing biological uncertainty rather than on better understanding the impacts of human behavior because of the difficulty of incorporating human behavior into simulation models. In this study, we use the fisheries in Narragansett Bay (Rhode Island, USA) as a case study to demonstrate how coupled modeling can be used to represent interactions between the food web and fishers in a social-ecological system. Narragansett Bay holds both a commercial fishery for forage fish, i.e., Atlantic menhaden (Brevoortia tyrannus) and a recreational fishery for their predators, i.e. striped bass (Morone saxatilis) and bluefish (Pomatomus saltatrix). To explore trade-offs between these two fisheries, we created a food web model and then coupled it to a recreational fishers’ behavior model, creating a dynamic social-ecological representation of the ecosystem. Fish biomass was projected until 2030 in both the stand-alone food web model and the coupled social-ecological model, with results highlighting how the incorporation of fisher behavior in modeling can lead to changes in the ecosystem. We examined how model outputs varied in response to three attributes: (1) the forage fish commercial harvest scenario, (2) the predatory (piscivorous) fish abundance-catch relationship in the recreational fishery, and (3) the rate at which recreational fishers become discouraged (termed “satisfaction loss”). Higher commercial harvest of forage fish led to lower piscivorous fish biomass but had minimal effects on the number of piscivorous fish caught recreationally or recreational fisher satisfaction. Both the abundance-catch relationship and satisfaction loss rate had notable effects on the fish biomass, the number of fish caught recreationally, and recreational fisher satisfaction. Currently, the lack of spatial and location-specific fisher behavior data limits the predictive use of our model. However, our modeling framework shows that fisher behavior can be successfully incorporated into a coupled social-ecological model through the use of agent-based modeling, and our results highlight that its inclusion can influence ecosystem dynamics. Because fisher decision making and the ecosystem can influence one another, social responses to changing ecosystems should be explicitly integrated into ecosystem modeling to improve ecosystem-based fisheries management efforts.

Key words

agent-based model; Ecopath with Ecosim; ecosystem-based fisheries management; estuary; forage fish; social-ecological system

Copyright © 2021 by the author(s). Published here under license by The Resilience Alliance. This article is under a Creative Commons Attribution-NonCommercial 4.0 International License. You may share and adapt the work for noncommercial purposes provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license.

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