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Incorporating Uncertainty in Natural Resource Decision-Making

Graph of Monte Carlo sensitivity analysis results.

Results of a Monte Carlo sensitivity analysis of the Ecosystem Diagnosis and Treatment (EDT) model of salmon habitat. See Steel et al. 2009 (Fisheries) and McElhany et al. 2010

One of the greatest possible contributions of the statistical
and quantitative sciences is to provide guidance on how to
incorporate uncertainty in natural resource decision-making.

Ashley Steel
Tim Beechie and Aimee Fullerton, NOAA Northwest Fisheries Science Center

Research Description:

Projects include model sensitivity analyses that explain how best to apply modeled data in a management setting.
Models evaluated include a fisheries-habitat model (Steel et al 2009, McElhany et al. 2010) and a combined forest
vegetation and wildfire model (Hummel et al 2012).

Another approach to managing uncertainty is to use scenario-based planning. While at NOAA’s NW Fisheries
Science Center, we developed a spatially explicit, multi-model decision-support system for the Lewis River
Steel et al. 2008, Fullerton et al 2009, Fullerton et al. 2010) that evaluates multiple watershed-scale restoration

In cooperation with the FERA team, PNW Research Station, and Oregon State University extension, we have hosted
a series of three workshops on understanding models in order to better understand climate science. I have also
taught two seminars at the University of Washington, Scientific Information in Environmental Decision Making and
The Role of Habitat in Salmon Recovery Planning
that focus on incorporating uncertainty into natural resource

Additional examples of incorporating uncertainty into natural resource planning are summarized in the
following chapters:

  • Steel, E.A., T.J. Beechie, M.H. Ruckleshaus, A.H. Fullerton, P. McElhany, and P. Roni. 2009. Mind the gap:
    Uncertainty and model communication between managers and scientists.
    Pages 357-372 in E.E. Knudsen
    and J.H. Michael Jr., editors. Pacific salmon environmental and life history models: advancing science for
    sustainable salmon in the future. American Fisheries Society, Symposium 71, Bethesda, Maryland.
  • Steel, E.A., M.C. Liermann, P. McElhany, N.L. Scholz, A.C. Cullen. 2003. Uncertainty in habitat recovery planning.
    In Beechie, T. J., P. Roni, and E.A. Steel (Editors). Ecosystem Recovery Planning for Listed Salmon: Assessment
    Approaches for Salmon Habitat, NOAA Tech. Memo. NMFS-NWFSC-58, pgs 74-89. Available at http://www.nwfsc.noaa.gov/assets/25/4326_06162004_125546_tm58.pdf


Note: Most PDF files linked in the publications section of this page were not created by the USDA Forest Service, and may not be accessible to screen-reader software. Many publications are open access, and links to the html versions on the journal websites are also provided, where applicable.

Meznarich, P. 2014. Back to the future: assessing accuracy and sensitivityof a forest growth model. Science Findings 165. Portland, OR: US Department of Agriculture, Forest Service, Pacific Northwest Research Station. 6 p. Research by Susan Hummel (USFS PNW), Maureen Kennedy (Univ. WA), and Ashley Steel (USFS PNW) and others.

Hummel, S., M. Kennedy, E.A. Steel. 2013. Assessing forest vegetation and fire simulation model performance after the Cold Springs wildfire, Washington, USA. Forest Ecology and Management 287:40-52.

Fullerton, A.H., D. Jensen, E.A. Steel, D. Miller, and P. McElhany. 2010. How certain are salmon recovery forecasts? A watershed-scale sensitivity analysis. Environmental Modeling and Assessment 15(1):13-26.

McElhany, P., E. A. Steel, D. Jensen, and K. A. Avery. 2009. Uncertainty in a complex habitat model. Pages 339-356 in E. E. Knudsen and J. H. Michael Jr., editors. Pacific salmon environmental and life history models: advancing science for sustainable salmon in the future. American Fisheries Society, Symposium 71, Bethesda, Maryland.

McElhany, P., E.A. Steel, D. Jensen, K. Avery, N. Yoder, C. Busack and B. Thompson. 2010. Dealing with uncertainty in ecosystem models: lessons from a complex salmon model. Ecological Applications 20:465-482.

Steel, E.A., P. McElhany, N.J. Yoder, M.D. Purser, K. Malone, B.E. Thompsen, K.A. Avery, D. Jensen, G. Blair, C. Busack, M.D. Bowen, J. Hubble, T. Kantz, L. Mobrand. 2009. Making the best use of modeled data: Multiple approaches to sensitivity analysis. Fisheries 34 (July): 330-339.

Steel, E.A., A.H. Fullerton, Y. Caras, M.B. Sheer, P. Olson, D.W. Jensen, J. Burke, M. Maher, and P. Mcelhany. 2008. A spatially explicit decision support system for watershed-scale management of salmon. Ecology and Society 13 (2): 50. [online] URL: http://www.ecologyandsociety.org/vol13/iss2/art50/