Evaluating the best available social science for natural resource management decision-making

Abstract

© 2017 Increasing recognition of the human dimensions of natural resource management issues, and of social and ecological sustainability and resilience as being inter-related, highlights the importance of applying social science to natural resource management decision-making. Moreover, a number of laws and regulations require natural resource management agencies to consider the “best available science” (BAS) when making decisions, including social science. Yet rarely do these laws and regulations define or identify standards for BAS, and those who have tried to fill the gap have done so from the standpoint of best available natural science. This paper proposes evaluative criteria for best available social science (BASS), explaining why a broader set of criteria than those used for natural science is needed. Although the natural and social sciences share many of the same evaluative criteria for BAS, they also exhibit some differences, especially where qualitative social science is concerned. Thus we argue that the evaluative criteria for BAS should expand to include those associated with diverse social science disciplines, particularly the qualitative social sciences. We provide one example from the USA of how a federal agency − the U.S. Forest Service − has attempted to incorporate BASS in responding to its BAS mandate associated with the national forest planning process, drawing on different types of scientific information and in light of these criteria. Greater attention to including BASS in natural resource management decision-making can contribute to better, more equitable, and more defensible management decisions and policies.

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Citation

Published Version (Please cite this version)

10.1016/j.envsci.2017.04.002

Publication Info

Charnley, S, C Carothers, T Satterfield, A Levine, MR Poe, K Norman, J Donatuto, SJ Breslow, et al. (2017). Evaluating the best available social science for natural resource management decision-making. Environmental Science and Policy, 73. pp. 80–88. 10.1016/j.envsci.2017.04.002 Retrieved from https://hdl.handle.net/10161/18608.

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Scholars@Duke

Basurto

Xavier Basurto

Truman and Nellie Semans/Alex Brown & Sons Associate Professor

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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