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Economics & Social Sciences Research Program

Optimal Multispecies Harvesting Targets in Biologically, Technologically, and Temporally Interdependent Fisheries

Economic and Social Science Research (ESSR) program researcher Stephen Kasperski is conducting research relating to the multispecies bioeconomic models of groundfish in the Bering Sea. Specifically, his research focuses on the biological interactions among species, technological interactions which result in catching multiple species, and temporal interactions between species as fishermen allocate their effort across multiple fisheries over the course of a year.  Results show that the impact of biological and technological interactions can substantially alter the optimal harvest policies compared with a single-species bioeconomic model.

This study uses the arrowtooth flounder, Pacific cod, and walleye pollock fisheries in the BSAI region of Alaska as a case study and finds the net present value of the three species fishery is over $20.7 billion dollars in the multispecies model, over $5.0 billion dollars more than the net present value of the single-species model. This is a function of the interdependence among species and how that affects other species’ growth. Given that arrowtooth flounder negatively impact the growth of cod and pollock, substantially increasing the harvest of arrowtooth to decrease its stock size is optimal in the multispecies model, as it leads to increased growth and, therefore, greater potential harvests of cod and pollock. The single species model does not incorporate these feedbacks among species and, therefore, assumes each species is unaffected by the stock rise or collapse of the other species. The vessels in this fishery are also known to exhibit cost anti-complementarities among species, which implies that harvesting multiple species jointly is more costly than catching them independently. As approaches for ecosystem-based fisheries management are developed, the results demonstrate the importance of focusing not only on the economically valuable species, but also on some non-harvested species, as they can affect the productivity and availability of higher value species.

By Stephen Kasperski

Extending Multi-attribute Utility Function (MAUF) Study to Develop Socioeconomic Indicators

Ecosystem-based fisheries management requires a holistic assessment of the status of fisheries by integrating ecosystem indicators for several major management objectives, such as sustainability, biodiversity, habitat quality, and socio-economic status. Scientists have already paid much attention to the first three objectives (i.e., sustainability, biodiversity, and habitat quality) and to the development of associated indicators. Although there have been some efforts to develop socio-economic indicators, most socio-economic indicators in previous studies are not firmly based on economic theory. One exception is a recent study by AFSC researcher Chang Seung (ESSR Program researcher) and collaborating researcher Chang Ik Zhang (Professor, Institute of Fisheries Science Pukyong National University, Republic of Korea).

This study uses a multi-­attribute utility function (MAUF) approach to calculate several economic indicators for the eastern Bering Sea trawl fishery and uses important economic concepts in utility function theory, such as preferential independence and utility independence, to develop nonlinear utility functions or indicators for stakeholders. This study also aggregates the individual utility functions into a social welfare function or an aggregate index measuring the overall socio-economic status of the fishery. One limitation of the study is that it assumed the opinions of the analyst reflect the preferences of the stakeholders when assessing component utility functions and those of decision makers when aggregating them.

A future study will extend Seung and Zhang’s research in two ways. First, the study will elicit stakeholders and policy maker preferences for socio-economic indicators via interviews. Second, to aggregate stakeholder preferences into a social welfare function, the study will use a more formal procedure than employed by Seung and Zhang. Once the aggregate socio-economic index is developed, it will be integrated with non-socioeconomic indicators being developed by other programs within the AFSC.

By Chang Seung

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