Bridging Data Gaps Through Modeling and Evaluation of Surrogates: Use of the Best Available Science to Protect Biological Diversity Under the National Forest Management Act
58 Pages Posted: 19 Jul 2007
The implementation of environmental law and policy typically proceeds in the face of scientific uncertainty. Despite this pervasive uncertainty, Congress has directed environmental and resource management agencies to ground their policy decisions in science. Agencies sometimes cope with the paradox of making science-based decisions in the face of uncertainty by using scientific models or other surrogacy techniques to simulate reality. Such simulation enables agencies to conform to their statutory responsibilities to base decisions on scientific considerations, even though a complete understanding of the relationships between their actions and the resulting environmental effects may be beyond their current capabilities.
This article considers the lessons that may be drawn from one federal agency's shifting approach to the use of models and surrogates. It explores the efforts by the United States Forest Service to comply with the mandate in the National Forest Management Act (NFMA) to provide for diversity of plant and animal communities in its planning processes. To minimize the uncertainty it faces in predicting what impact a particular action, such as a timber sale, will have on the biological diversity, the Forest Service has used models and surrogates. Initially, the agency used the impact of planned activities on management indicator species (MIS) as a surrogate for the impact of those activities on biodiversity in the affected area. More recently, it has relied on analysis of the impact of planned activities on the habitat of MIS as a surrogate for the affects of its activities on biodiversity. This article explores the Forest Service's shifting approach to implementation of the NFMA's diversity mandate to illustrate the benefits and disadvantages of using scientific models and surrogacy techniques to make science-based decisions in the face of uncertainty. It provides a list of criteria (including recognition of the limits of scientific knowledge, collaboration, transparency, flexibility, and accountability) for judging modeling and similar simulation techniques and assesses how the Forest Service's efforts to implement the diversity requirement fare under those criteria.
Keywords: environmental law, natural resource management, National Forest Management Act, Forest Service, biodiversity, scientific modeling
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