Resilience Thinking and Structured Decision Making in Social-Ecological Systems

Authors

Noelle M. Hart, University of Nebraska-LincolnFollow

Date of this Version

8-2015

Document Type

Article

Comments

A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Natural Resource Sciences (Adaptive Management), Under the Supervision of Professor Craig R. Allen. Lincoln, Nebraska: August, 2015

Copyright (c) 2015 Noelle M. Hart

Abstract

Natural resource management may be improved by synthesizing approaches for framing and addressing complex social-ecological issues. This dissertation examines how structured decision making processes, including adaptive management, can incorporate resilience thinking. Structured decision making is a process for establishing a solid understanding of the problem, values, management options, and potential consequences. Adaptive management is a form of structured decision making in which uncertainty is reduced for iterative decisions through designed monitoring and review. Resilience thinking can help conceptualize complex social-ecological systems and draws attention to the risks of managing for narrowly-focused objectives.

This dissertation provides practical advice to managers and can facilitate discussions regarding how to make wise decisions in complex social-ecological systems. Specifically, I explore how an iterative structured decision making process can contribute to the resilience of an oak forest in southeastern Nebraska. Chapter 2 discusses how a structured decision making process can emphasize principles of resilience thinking. I present a suite of management recommendations, drawing on information from practitioners’ guides and using oak forest conservation as a case study. Chapter 3 demonstrates how oak forest models can reflect elements of resilience thinking and be used to identify optimal policies. I quantify a state-and-transition model into a Markov decision process by establishing transition probabilities based on resilience assumptions and setting the time horizon (infinite), discount factor, and reward function. Limitations are discussed, including that the optimal policy is sensitive to uncertainty about aspects of the Markov decision process. Chapter 4 provides a practical method for incorporating adaptive management projects into State Wildlife Action Plans, in part based on experience with conservation planning in Nebraska. I present a dichotomous key for identifying when to use adaptive management and a basic introduction to developing adaptive management projects are presented. Chapter 5 describes an initial effort to reduce uncertainty for oak forest conservation in southeastern Nebraska. I use multimodel inference to explore different hypotheses about what environmental and management variables are correlated with oak seedling abundance. The results indicate that the number of large oaks is an important factor. I discuss adaptive management as a potential means for further investigating management effects. Chapter 6 synthesizes the dissertation by considering the management implications for oak forest conservation in southeastern Nebraska, identifying general challenges and limitations, presenting methods for improving the framework, and returning to the broader goal of implementing the social-ecological systems paradigm.

Adviser: Craig R. Allen