Structured decision making as a conceptual framework to identify thresholds for conservation and management

Authors

Julien Martin, University of Florida, Gainesville & Patuxent Wildlife Research Center, United States Geological SurveyFollow
Michael C. Runge, Patuxent Wildlife Research Center, United States Geological Survey
James D. Nichols, Patuxent Wildlife Research Center, United States Geological Survey
Bruce C. Lubow, Colorado State University, Fort Collins
William L. Kendall, Patuxent Wildlife Research Center, United States Geological Survey

Date of this Version

2009

Citation

U.S. government works are not subject to copyright.

Comments

Ecological Applications, 19(5), 2009, pp. 1079-1090

Abstract

Thresholds and their relevance to conservation have become a major topic of discussion in the ecological literature. Unfortunately, in many cases the lack of a clear conceptual framework for thinking about thresholds may have led to confusion in attempts to apply the concept of thresholds to conservation decisions. Here, we advocate a framework for thinking about thresholds in terms of a structured decision making process. The purpose of this framework is to promote a logical and transparent process for making informed decisions for conservation.

Specification of such a framework leads naturally to consideration of definitions and roles of different kinds of thresholds in the process. We distinguish among three categories of thresholds. Ecological thresholds are values of system state variables at which small changes bring about substantial changes in system dynamics. Utility thresholds are components of management objectives (determined by human values) and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changing in management actions in order to reach specified management objectives. The approach that we present focuses directly on the objectives of management, with an aim to providing decisions that are optimal with respect to those objectives. This approach clearly distinguishes the components of he decision process that are inherently subjective (management objectives, potential management actions) from those that are more objective (systems models, estimates of system state). Optimization based on these components then leads to decision matrices specifying optimal actions to be taken at various values of system state variables. Values of state variables separating different actions in such matrices are viewed as decision thresholds. Utility thresholds are included in the objectives component, and ecological thresholds may be embedded in models projecting consequences of management actions. Decision thresholds are determined by the above-listed components of a structured decision process. These components may themselves vary over time, inducing variation in the decision threshold inherited from them. These dynamic decision thresholds can then be determined using adaptive management. We provide numerical examples (that are based on patch occupancy models) of structures decision process that include all three kinds of thresholds.