Growing into Interdisciplinarity: How to Converge Biology, Economics, and Social Science in Fisheries Research?
Päivi Haapasaari,
Fisheries and Environmental Management Group, Department of Environmental Sciences, University of HelsinkiSoile Kulmala,
Marine Research Centre, Finnish Environment Institute; MTT Agrifood Research, Finland; Finnish Game and Fisheries Research InstituteSakari Kuikka,
Fisheries and Environmental Management Group, Department of Environmental Sciences, University of Helsinki
DOI: http://dx.doi.org/10.5751/ES-04503-170106
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Abstract
It has been acknowledged that natural sciences alone cannot provide an adequate basis for the management of complex environmental problems. The scientific knowledge base has to be expanded in a more holistic direction by incorporating social and economic issues. As well, the multifaceted knowledge has to be summarized in a form that can support science-based decision making. This is, however, difficult. Interdisciplinary skills, practices, and methodologies are needed that enable the integration of knowledge from conceptually different disciplines. Through a focus on our research process, we analyzed how and what kind of interdisciplinarity between natural scientists, environmental economists, and social scientists grew from the need to better understand the complexity and uncertainty inherent to the Baltic salmon fisheries, and how divergent knowledge was integrated in a form that can support science-based decision making. The empirical findings suggest that interdisciplinarity is an extensive learning process that takes place on three levels: between individuals, between disciplines, and between types of knowledge. Such a learning process is facilitated by agreeing to a methodological epochè and by formulating a global question at the outset of a process.
Key words
Baltic Sea salmon fisheries; Bayesian belief networks; bioeconomic modeling; integrated model; interdisciplinarity, interdisciplinary learning; fisheries research; methodological epochè; multidisciplinarity
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