Teresa R. Johnson - US grants

The purpose of this project is to gain a better understanding of the way competition between individual fishermen lead to the emergence of private incentives and informal social arrangements that are (or are not) consistent with conservation of the resource. These informal arrangements and incentives are important because they help us understand the extent to which private interests might strengthen or weaken on-going resource management and, consequently, the sustainability of coupled human and natural systems. The broad hypothesis driving the study is that the informal social structure that emerges from competitive interactions among fishermen reflects the particular circumstances of the natural system. In some cases, successful competition requires secretive non-cooperative behavior; in others, cooperation tends to yield better competitive results. These different outcomes have different, and not always obvious, impacts on the feasibility and effectiveness of resource management.

We think of the relevant human social process as one in which individuals compete with one another through time-consuming and costly acquisition of valuable knowledge about a complex resource. To compete successfully, individuals must balance the immediate benefits that come from exploiting knowledge they currently hold with the costly need to explore for new knowledge; additionally, when seeking new knowledge, individuals must balance the costs and benefits of acquiring knowledge through cooperation or through autonomous search. In order to model this kind of competitive process, we employ a significantly modified version of a technique borrowed from computer science called a learning classifier system (LCS). LCS uses a genetic algorithm to mimic the way an agent (here a fisherman) uses his experience to continuously refine his knowledge and decisions about his natural and social environment. The importance of LCS is that it permits simulation of the co-evolving strategic interactions of self-interested fishermen who are only partially informed about the state of the resource they are exploiting and the fishermen with whom they compete.

The problem of understanding these kinds of competitive dynamics is evident in almost all coupled natural and human systems. We apply the approach to a comparative study of three Gulf of Maine fisheries which are characterized by significantly different temporal and spatial dynamics - sea urchins, lobster and cod. Each fishery will be modeled using a biophysical simulator of the natural system and a tightly integrated multi-agent learning classifier system that simulates the learning and interactions of fishermen. The design of each model will be based in part on extensive interviews with fishermen about their knowledge of the dynamics of the fisheries in which they work. We will use these models to explore past and prospective policy problems in each fishery.

Beyond the immediate applicability of these explorations, we expect this project will provide a foundation for the wider use of multi-agent learning models in other coupled systems. Project outcomes will be transmitted regularly to industry and managers.

Principal investigators include economists, biologists, anthropologists and computer scientists. All the PIs have years of experience in the fisheries of the Gulf of Maine and have well developed relationships with individual fishermen and managers. A masters level student in marine policy, a Ph.D. student in computer or marine science and a post-doctoral researcher in computer science will be employed on the project. In addition, the project will develop an undergraduate course in complex adaptive social-ecological systems and a graduate student/faculty workshop in the same area.

Abstract

Proposal Number: IIA-1355457

Proposal Title: Maine EPSCoR: The Nexus of Coastal Social-Environmental Systems and Sustainable Ecological Aquaculture

Institution: University of Maine

Non-Technical Description
Research through the Sustainable Ecological Aquaculture Network (SEANET) will contribute to important scientific debates on how to balance the social ecological knowledge gained with local decision-making through its efforts to develop comprehensive, transdisciplinary, coastal marine science that is positioned at the knowledge interface of marine fisheries, ecosystems conservation and restoration. The project is innovative and potentially transformative displaying considerable possibilities for advancing knowledge and discovery about Sustainable Ecological Aquaculture (SEA) within Maine?s coastal social, economic, and environmental nexus. Other coastal regions face problems similar to those in Maine and results could inform sustainable solutions to coastal community and aquaculture problems on a global basis.

Maine develops research and education activities to engage a wide number of constituents. The project will increase public scientific literacy and train a modern STEM workforce. New STEM programs, mentoring, and research opportunities will specifically recruit females, Native Americans, and other minority students. Other initiatives include student internships, innovation workshops and the creation of a statewide collaborative network of faculty, postdocs, graduate and undergraduate students working together with stakeholders. Middle and secondary teachers will also be supported to engage in curriculum development. Economic development activities include the creation of Geographical Information Systems maps that identify the risks and carrying capacity for high valued commercial harvest species. The activities strengthen areas noted to be important within the State?s Science and Technology Plan such as workforce training and development in the areas of marine ecology and aquaculture.

Technical Description
This project seeks develops SEANET to determine the social and environmental carrying capacities of aquaculture systems in Maine?s coastal bioregions. SEANET is envisioned as a statewide, multi-institutional, academic research network that will advance knowledge and discovery about SEA within coastal Maine?s social, economic, and environmental nexus. SEANET capitalizes on Maine?s diverse Spcio- ecological systems contexts by comparing and contrasting three coastal bioregions, south, midcoast, and downeast, which comprise important research gradients in amounts of oceanic versus freshwater inputs, physical oceanography, degrees of urbanization and nutrient inputs, social-economic demographics, and intensities of fisheries and aquaculture developments. A suite of new buoys and sensors, industrial partnerships and models will be used with existing data to assess and monitor the carrying capacity of these areas for aquaculture species over time. Social research will engage local stakeholders using focus groups, surveys, and interviews and combined with modeling will address perceptions, attitudes and local values related to aquaculture. The project involves many of the institutes of higher education across the state including community colleges as well as local producer groups and state government agencies.