Planning for Restoration: A Decision Analysis Approach to Prioritization

Authors

  • Kendra A. Cipollini,

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      The Nature Conservancy, Ohio Conservation Science Office, Department of Biological Sciences, Wright State University, Dayton, OH 45435, U.S.A.

    • 3

      Present address: Wilmington College, Box 1287, Wilmington, OH 45177, U.S.A.

  • Aimee L. Maruyama,

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      The Nature Conservancy, Ohio Conservation Science Office, Department of Biological Sciences, Wright State University, Dayton, OH 45435, U.S.A.

    • 4

      Present address: 333 S. Stafford Street, Yellow Springs, OH 45387, U.S.A.

  • Christopher L. Zimmerman

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      The Nature Conservancy, Ohio Conservation Science Office, Department of Biological Sciences, Wright State University, Dayton, OH 45435, U.S.A.

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      Present address: The Nature Conservancy, Eastern New York Conservation Office, 200 Broadway, 3rd Floor, Troy, NY 12180, U.S.A.


Address correspondence to K. A. Cipollini, email kal143@alumni.psu.edu

Abstract

Ecological restoration often relies on the use of expert opinion to make management decisions in the face of uncertainty. The quantification of expert opinion can be difficult, especially when more than one expert is consulted and experts are not in agreement. Decision analysis can provide a framework to systematically deconstruct a complex problem and provide greater objectivity to restoration decisions. We utilized decision analysis techniques to identify restoration objectives and to quantify expert opinions to prioritize restoration activities at 112 prairie openings in the Edge of Appalachia Preserve in southern Ohio, U.S.A. We first created an objectives hierarchy to model how decision-makers decide which prairies to manage. We then determined how to measure each component of the hierarchy and sampled all prairies for percent woody cover, geology, indicator species index (an index of plant species richness), slope, aspect, and distance to nearest prairie. We modeled seven different experts’ preferences for managing prairies with varying values for each of these ecological measures. We then interviewed the same decision-makers to determine relative weights for each component of the objectives hierarchy using trade-off analysis. By combining the weights, preference relationships, and sampling data, we were able to rank each prairie and management unit based on its management priority. Experts had similar preferences except for the measure of distance to nearest prairie. We found that decision-makers gave different weights to each of the different components of the hierarchy. Generally, experts weighted percent woody cover, indicator species index, and geology more highly than slope, aspect, and distance to nearest prairie. Despite these differences, priorities for management, once all factors were weighted and combined, were similar.

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