Institutional effects on ecological outcomes of community-based management of fisheries in the Amazon.

Abstract

Communities throughout the globe are increasingly being given the responsibility of resource management, making it necessary to understand the factors that lead to success in community-based management (CBM). Here, we assessed whether and how institutional design principles affect the ecological outcomes of CBM schemes for Arapaima sp., an important common-pool fishery resource of the Amazon Basin. We quantified the degree of presence of Ostrom's (Science 325:419-422, 1990) institutional design principles in 83 communities using a systematic survey, and quantitatively linked the design principles to a measure of ecological outcome (arapaima density) in a subset of 39 communities to assess their influence. To understand regional patterns of institutional capacity for CBM, we evaluated the degree of presence of each principle in all 83 communities. The principle scores were positively related to arapaima density in the 39 CBM schemes, explaining about half of the variation. Design principles related to defined boundaries and graduated sanctions exerted the strongest influence on the capacity of CBM to increase arapaima density. The degree to which most principles were present in all 83 communities was generally low, however, with the two most influential principles (defined boundaries and graduated sanctions) being the least present of all. Although the roles of the other principles (management rules, conflict resolution, collective action, and monitoring systems) are probably important, our results indicate that efforts aimed at strengthening the presence of defined boundaries and graduated sanctions in communities hold promise to improve the effectiveness of arapaima CBM regionally.

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Published Version (Please cite this version)

10.1007/s13280-021-01575-1

Publication Info

Arantes, Caroline C, Leandro Castello, Xavier Basurto, Nicole Angeli, Aby Sene-Haper and David G McGrath (2022). Institutional effects on ecological outcomes of community-based management of fisheries in the Amazon. Ambio, 51(3). pp. 678–690. 10.1007/s13280-021-01575-1 Retrieved from https://hdl.handle.net/10161/26181.

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Basurto

Xavier Basurto

Adjunct Professor in the Division of Marine Science and Conservation

I am interested in the fundamental question of how groups (human and non-human) can find ways to self-organize, cooperate, and engage in successful collective action for the benefit of the common good. To do this I strive to understand how the institutions (formal and informal rules and norms) that govern social behavior, interplay with biophysical variables to shape social-ecological systems. What kind of institutions are better able to govern complex-adaptive systems? and how can societies (large and small) develop robust institutions that provide enough flexibility for collective learning and adaptation over the long-term?

My academic and professional training is based on a deep conviction that it is through integrating different disciplinary perspectives and methods that we will be able to find solutions to challenging dilemmas in natural resources management, conservation, and environmental policy. Trained as a marine biologist, I completed a M.S in natural resources studying small-scale fisheries in the Gulf of California, Mexico. Realizing the need to bring social science theories into my work on common-pool resources sustainability, I earned an MPA and a Ph.D. in Management (with a minor in cultural anthropology) from the University of Arizona and under the supervision of Edella Schlager. Following I spent two years working with Elinor Ostrom, 2009 co-winner of the Nobel Prize in Economics, at the Workshop for Political Theory and Policy Analysis of Indiana University. Methodologically, I am familiar with a variety of quantitative and qualitative approaches and formally trained to conduct Qualitative Comparative Analysis (QCA or more recently fsQCA), that allows among other things, systematic comparisons of middle range N sample sizes and address issues of multiple-causality.


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