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Application of a Life Cycle Simulation Model to Evaluate Impacts of Water Management and Conservation Actions on an Endangered Population of Chinook Salmon

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Abstract

Fisheries and water resource managers are challenged to maintain stable or increasing populations of Chinook salmon in the face of increasing demand on the water resources and habitats that salmon depend on to complete their life cycle. Alternative management plans are often selected using professional opinion or piecemeal observations in place of integrated quantitative information that could reduce uncertainty in the effects of management plans on population dynamics. We developed a stochastic life cycle simulation model for an endangered population of winter-run Chinook salmon in the Sacramento River, California, USA with the goal of providing managers a tool for more effective decision making and demonstrating the utility of life cycle models for resource management. Sensitivity analysis revealed that the input parameters that influenced variation in salmon escapement were dependent on which age class was examined and their interactions with other inputs (egg mortality, Delta survival, ocean survival). Certain parameters (river migration survival, harvest) that were hypothesized to be important drivers of population dynamics were not identified in sensitivity analysis; however, there was a large amount of uncertainty in the value of these inputs and their error distributions. Thus, the model also was useful in identifying future research directions. Simulation of variation in environmental inputs indicated that escapement was significantly influenced by a 10% change in temperature whereas larger changes in other inputs would be required to influence escapement. The model presented provides an effective demonstration of the utility of life cycle simulation models for decision making and provides fisheries and water managers in the Sacramento system with a quantitative tool to compare the impact of different resource use scenarios.

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References

  1. Allendorf, F. W., Bayles, D., Bottom, D. L., Currens, K. P., Frissel, C. A., Hankin, D., et al. (1997). Prioritizing Pacific salmon stocks for conservation. Conservation Biology, 11, 140–152.

    Article  Google Scholar 

  2. Archer, G., Saltelli, A., & Sobol’, I. M. (1997). Sensitivity measures, ANOVA-like techniques and the use of the bootstrap. Journal of Statistical Computation and Simulation, 58, 99–120.

    Article  Google Scholar 

  3. Bartholow, J. M., & Heasley, J. (2006). Evaluation of Shasta dam scenarios using a salmon population model. Reston: United States Geological Survey.

    Google Scholar 

  4. Beacham, T. D., & Murray, C. B. (1989). Variation in developmental biology of sockeye salmon (Oncorhynchus nerka) and Chinook salmon (Oncorhynchus tshawytscha) in British Columbia. Canadian Journal of Zoology, 67, 2081–2089.

    Article  Google Scholar 

  5. Confalonieri, R., Bellocchi, G., Bregaglio, S., Donatelli, M., & Acutis, M. (2010). Comparison of sensitivity analysis techniques: a case study with the rice model WARM. Ecological Modelling, 221, 1897–1906.

    Article  Google Scholar 

  6. Costanza, R., Duplisea, D., & Kautsky, U. (1998). Ecological modeling on modeling ecological and economic systems with Stella. Ecological Modelling, 110, 1–4.

    Article  Google Scholar 

  7. Feist, B. E., Steel, E. A., Pess, G. R., & Bilby, R. E. (2003). The influence of scale on salmon habitat restoration priorities. Animal Conservation, 6, 271–282.

    Article  Google Scholar 

  8. Fieberg, J., & Jenkins, K. J. (2005). Assessing uncertainty in ecological systems using global sensitivity analyses: a case example of simulated wolf reintroduction effects on elk. Ecological Modelling, 187, 259–280.

    Article  Google Scholar 

  9. 59 FR 440. (1994). Final Rule: endangered and threatened species: status of Sacramento winter-run Chinook salmon. Federal Register 59,440.

    Google Scholar 

  10. Ford, A. (1999). Modeling the environment: an introduction to system dynamics modeling of environmental systems. Washington: Island Press.

    Google Scholar 

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

    Article  Google Scholar 

  12. Good, T. P., Beechie, T. J., McElhany, P., McClure, M. M., & Ruckelshaus, M. H. (2007). Recovery planning for Endangered Species Act-listed Pacific salmon: using science to inform goals and strategies. Fisheries, 32, 426–440.

    Article  Google Scholar 

  13. Grover, A., Low, A., Ward, P., Smith, J., Mohr, M., Viele, D., et al. (2004). Recommendations for developing fishery management plan conservation objectives for Sacramento River spring Chinook. SRWSC workgroup report to the Pacific Fisheries Management Council, Exhibit C.7.b. http://www.pccouncil.org/bb/2004/0304/exc7.pdf. Accessed 15 Jan 2011.

  14. Gustafson, R. G., Waples, R. S., Meyers, J. M., Weitkamp, L. A., Bryant, G. J., Johnson, O. W., et al. (2007). Pacific salmon extinctions: quantifying lost and remaining diversity. Conservation Biology, 21, 1009–1020.

    Article  Google Scholar 

  15. Lichatowich, J., Morbrand, L., & Lestelle, L. (1999). Depletion and extinction of Pacific salmon (Oncorhynchus spp.): a different perspective. ICES Journal of Marine Science, 56, 467–472.

    Article  Google Scholar 

  16. Lindley, S. T., et al. (2009). What caused the Sacramento River fall Chinook stock collapse? National Marine Fisheries Service, Santa Cruz. Available: http://www.swr.noaa.gov/media/SalmonDeclineReport.pdf. Accessed 5 Dec 2010.

  17. McClure, M. M., Holmes, E. E., Sanderson, B. L., & Jordan, C. E. (2003). A large-scale multispecies status assessment: anadromous salmonids in the Columbia River basin. Ecological Applications, 13, 964–989.

    Article  Google Scholar 

  18. Michel, C. J. (2010). River and estuarine survival and migration of yearling Sacramento River Chinook salmon (Oncorhynchus tshawytscha) smolts and the influence of environment. Masters thesis, University of California-Santa Cruz, Santa Cruz, California.

  19. Murray, C. B., & McPhail, J. D. (1988). Effect of incubation temperature on the development of five species of Pacific salmon (Oncorhynchus) embryos and alevins. Canadian Journal of Zoology, 66, 266–273.

    Article  Google Scholar 

  20. Myrick, C. A., & Cech, J. J., Jr. (2004). Temperature effects on juvenile anadromous salmonids in California’s Central Valley: what don’t we know? Reviews in Fish Biology and Fisheries, 14, 113–1233.

    Article  Google Scholar 

  21. Newman, K. B. (2003). Modeling paired release-recovery data in the presence of survival and capture heterogeneity with application to marked juvenile salmon. Statistical Modeling, 3, 157–177.

    Article  CAS  Google Scholar 

  22. Newman, K. B., & Brandes, P. L. (2010). Hierarchical modeling of juvenile Chinook salmon survival as a function of Sacramneto–San Joaquin Delta water exports. North American Journal of Fisheries Management, 30, 157–169.

    Article  Google Scholar 

  23. Perry, R. W., Skalski, J. R., Brandes, P. L., Sandstrom, P. T., Klimley, A. P., Amman, A., et al. (2010). Estimating survival and migration route probabilities of juvenile Chinook salmon in the Sacramento–San Joaquin River Delta. North American Journal of Fisheries Management, 30, 142–156.

    Article  Google Scholar 

  24. Poytress, W. R., & Carillo, F. D. (2007). Brood-year 2007 winter Chinook juvenile production indices with comparisons to juvenile production estimates derived from adult escapement. Red Bluff: United States Fish and Wildlife Service.

    Google Scholar 

  25. R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org/.

  26. Ricker, W. E. (1975). Recruitment and stock-recruitment relationships. Computation and interpretation of biological statistics of fish populations. Fisheries Research Board of Canada, Bulletin, 191, 265–296.

    Google Scholar 

  27. Rizzo, D. M., Mouser, P. J., Whitney, D. H., Mark, C. D., Magarey, R. D., & Voinov, A. A. (2006). The comparison of four dynamic systems-based software packages: translation and sensitivity analysis. Environmental Modelling and Software, 21, 1491–1502.

    Article  Google Scholar 

  28. Ruckelshaus, M. H., Levin, P., Johnson, J. B., & Kareiva, P. (2002). The Pacific salmon wars: what science brings to the challenge of recovering species. Annual Review of Ecology and Systematics, 33, 665–706.

    Article  Google Scholar 

  29. Saltelli, A., Tarantola, S., & Campolongo, F. (2000). Sensitivity analysis as an ingredient to modeling. Statistical Science, 15, 377–395.

    Article  Google Scholar 

  30. Snider, B., Reavis, B., Titus, R. G., & Hill, S. (2001.) Upper Sacramento River winter-run Chinook salmon escapement survey May–August 2001, California Department of Fish and Game Technical report 02-1.

  31. Solomon, D. J., Mawle, G. W., & Duncan, W. (2003). An integrated approach to salmonid management. Fisheries Research, 62, 229–234.

    Article  Google Scholar 

  32. United States Fish and Wildlife Service. (1999). Effects of temperature on early-life survival of Sacramento River fall-run and winter-run Chinook salmon. Final report. USFWS, Red Bluff, CA.

  33. Vogel, D. (2008). Pilot study to evaluate acoustic-tagged juvenile Chinook salmon smolt migration in the northern Sacramento-San Joaquin Delta, 2006–2007. California Department of Natural Resources Report.

  34. Wells, B. K., Grimes, C. B., & Waldvogel, J. B. (2007). Quantifying the effects of wind, upwelling, curl, sea surface temperature and sea level height on growth and maturation of a California Chinook salmon (Oncorhynchus tshawytscha). Fisheries Oceanography, 16, 363–382.

    Article  Google Scholar 

  35. Williams, J. G. (2006). Central Valley salmon: a perspective on Chinook and steelhead in the Central Valley of California. San Francisco Estuary and Watershed Science, 4, 1–416.

    Google Scholar 

  36. Yoshiyama, R. M., Gerstung, E. R., Fisher, F. W., & Moyle, P. B. (2001). Historical and present distribution of Chinook salmon in the Central Valley of California. California Department of Fish and Game Fish Bulletin, 179, 71–176.

    Google Scholar 

  37. Zeug, S. C., Albertson, L. K., Lenihan, H., Hardy, J., & Cardinale, B. (2011). Predictors of Chinook salmon extirpation in California’s Central Valley. Fisheries Management and Ecology, 18, 61–71.

    Article  Google Scholar 

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Acknowledgments

The authors thank Robert Leaf, Sean Sou, Qinqin Liu, and Aric Lester for their insightful comments on the model and manuscript. Steve Cramer was responsible for early development of the IOS model. Tommy Garrison provided valuable assistance with R code for the sensitivity analysis and Jenny Melgo put together the map of the Sacramento Basin. Funding for this project was provided by the California Department of Water Resources and The National Marine Fisheries Service (requisition no. NFFR5300-9-18382).

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Zeug, S.C., Bergman, P.S., Cavallo, B.J. et al. Application of a Life Cycle Simulation Model to Evaluate Impacts of Water Management and Conservation Actions on an Endangered Population of Chinook Salmon. Environ Model Assess 17, 455–467 (2012). https://doi.org/10.1007/s10666-012-9306-6

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