Environmental Modeling & Assessment

, Volume 15, Issue 1, pp 13–26 | Cite as

How Certain Are Salmon Recovery Forecasts? A Watershed-scale Sensitivity Analysis

  • A. H. FullertonEmail author
  • D. Jensen
  • E. A. Steel
  • D. Miller
  • P. McElhany


Complex relationships between landscape and aquatic habitat conditions and salmon (Oncorhynchus spp.) populations make science-based management decisions both difficult and essential. Due to a paucity of empirical data, models characterizing these relationships are often used to forecast future conditions. We evaluated uncertainties in a suite of models that predict possible future habitat conditions and fish responses in the Lewis River Basin, Washington, USA. We evaluated sensitivities of predictions to uncertainty in model parameters. Results were sensitive to 60% of model parameters but substantially so (|partial regression coefficients| >0.5) to <10%. We also estimated accuracy of several predictions using field surveys. Observations mostly fell within predicted ranges for riparian shade and fine-sediment deposition, but large woody debris estimates matched only half the time. We provide suggestions to modelers for improving model accountability, and describe how managers can incorporate prediction uncertainty into decision-making, thereby improving the odds of successful salmon habitat recovery.


Uncertainty Decision Watershed restoration Conservation Land management 



We thank A. Booy, B. Burke, J. Burke, K. Campbell, Y. Caras, J. Scheurer, and M. Sheer for field assistance with stream surveys. We thank B. Burke, C. Harvey, and A. Mullan for comments on earlier versions of this manuscript. Funding was provided by NOAA Fisheries Service, and by an internal grant from the Northwest Fisheries Science Center to AHF.


  1. 1.
    Busack, C. A., & Thompson, B. E. (2006). Improving salmon habitat productivity models for setting escapement goals and prioritizing habitat recovery funding. A Final Report to the Pacific Salmon Commission by Washington Department of Fish and Wildlife. Project SF-2006-H-27.Google Scholar
  2. 2.
    Campolongo, F., Saltelli, A., Sørensen, T., & Tarantola, S. (2000). Hitchhiker’s Guide to Sensitivity Analysis. In A. Saltelli, K. Chan, & E. Scott (Eds.), Sensitivity Analysis (p. 475). New York: Wiley.Google Scholar
  3. 3.
    EDT. (2007). Level of proof data used in the Ecological Diagnosis and Treatment model for the Lewis River Basin.
  4. 4.
    Flanagan, D. C., & Livingston, S. J. (1995). USDA—Water Erosion Prediction Project NSERL Report No. 11, July 1995 National Soil Erosion Research Laboratory USDA-ARS-MWA, partners: USDA—Natural Resource Conservation Service, USDA—Forest Service, USDI—Bureau of Land Management.Google Scholar
  5. 5.
    Fogarty, M. J., Mayo, R. K., O’Brien, L., Serchuk, F. M., & Rosenberg, A. A. (1996). Assessing uncertainty and risk in exploited marine populations. Reliability Engineering & System Safety, 54, 183–195. doi: 10.1016/S0951-8320(96)00074–9.CrossRefGoogle Scholar
  6. 6.
    Goodman, D. (2002). Uncertainty, risk, and decision: the PVA example. American Fisheries Society Symposium, 27, 171–196.Google Scholar
  7. 7.
    Helton, J. C. (1993). Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal. Reliability Engineering & System Safety, 42, 327–367. doi: 10.1016/0951-8320(93)90097-I.CrossRefGoogle Scholar
  8. 8.
    Hushka, L. (2005). Reproducible, free of policy preferences. The Environmental Forum. January/February: 41–42.Google Scholar
  9. 9.
    Hyatt, T. L., & Naiman, R. J. (2001). The residence time of large woody debris in the Queets River, Washington, USA. Ecological Applications, 11, 191–202. doi: 10.1890/1051-0761(2001)011[0191:TRTOLW]2.0.CO;2.CrossRefGoogle Scholar
  10. 10.
    Jensen, D. W., Steel, E. A., Fullerton, A. H., & Pess, G. (2008). Impact of fine sediment on egg-to-fry survival of Pacific salmon: a meta-analysis of published studies. Reviews in Fisheries Science, in press.Google Scholar
  11. 11.
    LCFRB (Lower Columbia Fish Recovery Board).(2004). Lower Columbia Salmon and Steelhead Recovery and Subbasin Plan, Volume I, and Volume II, Chapters 11–13: Upper and Lower North Fork Lewis and East Fork Lewis.
  12. 12.
    Lichatowich, J. A., Mobrand, L., Lestelle, L. C., & Vogel, T. S. (1995). An approach to the diagnosis and treatment of depleted Pacific salmon populations in Pacific Northwest watersheds. Fisheries, 20, 10–18. doi: 10.1577/1548-8446(1995)020<0010:AATTDA>2.0.CO;2.CrossRefGoogle Scholar
  13. 13.
    Mace, P. M., & Sissenwine, M. P. (2002). Coping with uncertainty: evolution of the relationship between science and management. American Fisheries Society Symposium, 27, 9–28.Google Scholar
  14. 14.
    Martin, D. J., & Benda, L. E. (2001). Patterns of instream wood recruitment and transport at the watershed scale. Transactions of the American Fisheries Society, 130, 940–958. doi: 10.1577/1548-8659(2001)130<0940:POIWRA>2.0.CO;2.CrossRefGoogle Scholar
  15. 15.
    McElhany, P., Chilcote, M., Myers, J., & Beamesderfer, R. (2007). Viability Status of Oregon Salmon and Steelhead Populations in the Willamette and Lower Columbia Basins. Technical Report. NOAA Fisheries Northwest Fisheries Science Center, Oregon Department of Fish and Wildlife, and Cramer Fish Sciences.
  16. 16.
    McElhany, P., Avery, K. A., Yoder, N. J., Busack, C., & Thompson, B. (2008). Dealing with uncertainty in ecosystem models: lessons from a complex salmon model. Ecological Applications, in press.Google Scholar
  17. 17.
    Meengs, C. C., & Lackey, R. T. (2005). Estimating the size of historical Oregon salmon runs. Reviews in Fisheries Science, 13, 51–66. doi: 10.1080/10641260590921509.CrossRefGoogle Scholar
  18. 18.
    Mobrand, L., Lichatowich, J. A., Lestelle, L. C., & Vogel, T. S. (1997). An approach to describing ecosystem performance “through the eyes of salmon”. Canadian Journal of Fisheries and Aquatic Sciences, 54, 2964–2973. doi: 10.1139/cjfas-54-12-2964.CrossRefGoogle Scholar
  19. 19.
    Nehlsen, W., Williams, J. E., & Lichatowich, J. A. (1991). Pacific salmon at the crossroads: Stocks at risk from California, Oregon, Idaho, and Washington. Fisheries, 16(2), 4–21. doi: 10.1577/1548-8446(1991)016<0004:PSATCS>2.0.CO;2.CrossRefGoogle Scholar
  20. 20.
    Neter, J., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1996). Applied linear regression models (p. 720, 3rd ed.). Chicago: Times Mirror Higher Education Group.Google Scholar
  21. 21.
    NOAA Fisheries Service.(1996). Making Endangered Species Act Determinations of Effect for Individual or Grouped Actions at the Watershed Scale: Matrix of Pathways and Indicators.
  22. 22.
  23. 23.
    ODFW (Oregon Department of Fish and Wildlife).(2006). Aquatic Inventories Project: Methods for Stream Habitat Surveys. pdffiles/hmethd06-for%20website(noFishKey).pdf.
  24. 24.
    Peterson, G. D., Cumming, G. S., & Carpenter, S. R. (2003). Scenario planning: a tool for conservation in an uncertain world. Conservation Biology, 17(2), 358–366. doi: 10.1046/j.1523-1739.2003.01491.x.CrossRefGoogle Scholar
  25. 25.
    Quinn, T. P. (2005). The Behavior and Ecology of Pacific Salmon and Trout p. 320. Seattle, WA.: University of Washington Press.Google Scholar
  26. 26.
    Reeves, G. H., Burnett, K. M., & McGarry, E. V. (2003). Sources of large wood in the main stem of a fourth-order watershed in coastal Oregon. Canadian Journal of Forest Research, 33, 1363–1370. doi: 10.1139/x03-095.CrossRefGoogle Scholar
  27. 27.
    Saltelli, A., Chan, K., & Scott, M. (2000a). Sensitivity Analysis. New York: Wiley.Google Scholar
  28. 28.
    Saltelli, A., Tarantola, S., & Campolongo, F. (2000b). Sensitivity analysis as an ingredient of modeling. Statistical Science, 15, 377–395. doi: 10.1214/ss/1009213004.CrossRefGoogle Scholar
  29. 29.
    Saltelli, A., Ratto, M., Tarantola, S., & Campolongo, F. (2005). Sensitivity analysis for chemical models. Chemical Reviews, 105, 2811–2828. doi: 10.1021/cr040659d.CrossRefGoogle Scholar
  30. 30.
    Sarewitz, D., Pielke, R. A., & Byerly, R. (2000). Prediction: Science, Decision Making, and the Future of Nature. Island, 400 pgs.Google Scholar
  31. 31.
    Sedell, J. R., Reeves, G. H., & Bisson, P. A. (1997). Habitat policy for salmon in the Pacific Northwest. In D. J. Stouder, P. A. Bisson, & R. J. Naiman (Eds.), Pacific Salmon and Their Ecosystems (pp. 375–388). New York: Chapman & Hall.Google Scholar
  32. 32.
    Sobol, I. M. (1993). Sensitivity estimates for non-linear mathematical models. Mathematical Modeling and Computational Experiment, 1, 407–414.Google Scholar
  33. 33.
    Steel, E. A., Liermann, M. C., McElhany, P., Sholz, N. L., & Cullen, A. C. (2003). Managing uncertainty in habitat recovery planning. In T. Beechie, A. Steel, P. Roni, & E. Quimby (Eds.), Ecosystem recovery planning for listed salmon: An integrated assessment approach for salmon habitat (183 p.). U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-58.
  34. 34.
    Steel, A., Fullerton, A., Caras, Y., Sheer, M., Olson, P., Jensen, D., et al. (2007). Habitat Analyses for the Lewis River Case Study: Final Report. Produced for the Willamette-Lower Columbia Technical Recovery Team by the Northwest Fisheries Science Center, NOAA Fisheries. research/divisions/ec/wpg/documents/lrcs/LewisRiverCaseStudyFinalReport.pdf.
  35. 35.
    Steel, E. A., Beechie, T. J., Ruckelshaus, M., Fullerton, A. H., McElhany, P., & Roni, P. (2008a). Mind the gap: uncertainty and model communication between managers and scientists. H. Michael, C. Steward, and E. Knudsen, eds. American Fisheries Society Symposium 2005, in press.Google Scholar
  36. 36.
    Steel, A., Fullerton, A., Caras, Y., Sheer, M., Olson, P., Jensen, D., et al. (2008b). A spatially explicit decision support system for managing wide ranging species. Ecology and Society, in press.Google Scholar
  37. 37.
    Steel, E. A., McElhany, P., Yoder, N. J., Purser, M. D., Malone, K., Thompsen, B. E., et al. (2008c). Making the best use of modeled data: multiple approaches to sensitivity analysis of a fish-habitat model. Fisheries, in press.Google Scholar
  38. 38.
    Zabel, R. W., Scheuerell, M. D., McClure, M. M., & Williams, J. G. (2006). The interplay between climate variability and density dependence in the population viability of Chinook salmon. Conservation Biology, 20, 190–200. doi: 10.1111/j.1523-1739.2005.00300.x.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • A. H. Fullerton
    • 1
    Email author
  • D. Jensen
    • 1
    • 2
  • E. A. Steel
    • 1
  • D. Miller
    • 3
  • P. McElhany
    • 1
  1. 1.Northwest Fisheries Science CenterNOAA FisheriesSeattleUSA
  2. 2.EugeneUSA
  3. 3.Earth Systems InstituteSeattleUSA

Personalised recommendations