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Optimization of Salt Marsh Management at the Chincoteague National Wildlife Refuge, Virginia, Through Use of Structured Decision Making

Open-File Report 2019-1056
Prepared in cooperation with the U.S. Fish and Wildlife Service
By: , , , , , and 
https://doi.org/10.3133/ofr20191056

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Abstract

Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing salt marsh management decisions at the Chincoteague National Wildlife Refuge in Virginia. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of 12 salt marsh management units within the refuge and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that would be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per salt marsh management unit, that would maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to approximately $2.5 million, but that further expenditures may yield diminishing return on investment. For multiple salt marsh management units, a scenario incorporating managing grazing practices within the marsh was selected to maximize benefits while constraining total costs for the refuge at less than $2.5 million. Thin-layer deposition was predicted to increase the total management benefit substantially, but at considerable total costs ($2.5 million to $83 million). The prototype presented here provides a framework for decision making at the Chincoteague National Wildlife Refuge that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuge.

Suggested Citation

Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., and Holcomb, K.S., 2019, Optimization of salt marsh management at the Chincoteague National Wildlife Refuge, Virginia, through use of structured decision making: U.S. Geological Survey Open-File Report 2019–1056, 29 p., https://doi.org/10.3133/ofr20191056.

ISSN: 2331-1258 (online)

Study Area

Table of Contents

  • Abstract
  • Introduction
  • Regional Structured Decision-Making Framework
  • Application to the Chincoteague National Wildlife Refuge
  • Results of Constrained Optimization
  • Considerations for Optimizing Salt Marsh Management
  • References Cited
  • Appendix 1. Regional Influence Diagrams
  • Appendix 2. Utility Functions for the Chincoteague National Wildlife Refuge
Publication type Report
Publication Subtype USGS Numbered Series
Title Optimization of salt marsh management at the Chincoteague National Wildlife Refuge, Virginia, through use of structured decision making
Series title Open-File Report
Series number 2019-1056
DOI 10.3133/ofr20191056
Year Published 2019
Language English
Publisher U.S. Geological Survey
Publisher location Reston, VA
Contributing office(s) Patuxent Wildlife Research Center, Eastern Ecological Science Center
Description vi, 29 p.
Country United States
State Virginia
Other Geospatial Chincoteague Island, Chincoteague National Wildlife Refuge
Online Only (Y/N) Y
Additional Online Files (Y/N) N
Google Analytic Metrics Metrics page
Additional publication details