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An Initial Framework for the Environmental Risk Assessment of Synthetic Biology-Derived Organisms with a Focus on Gene Drives

  • Wayne G. LandisEmail author
  • Ethan A. Brown
  • Steven Eikenbary
Chapter
Part of the Risk, Systems and Decisions book series (RSD)

Abstract

The goal of this chapter is to present a path to estimate risk due to synthetic biology being released into the environment. Our examples are for organisms released with gene drives specifically designed to alter the fitness of specific populations that either transmit disease or are nonindigenous and pose a hazard to the ecosystem services of a specific ecological structure. We apply the structure of source-stressor-habitat-effect-impact pathway derived from the relative risk model (Landis and Wiegers 2005) and as was demonstrated to be applicable in the National Academy of Sciences, Engineering and Medicine (NASEM) 2016 report Gene Drives on the Horizon. This relative risk model is now calculated employing Bayesian networks and has been applied to forestry management (Ayre and Landis 2012), infectious disease (Ayre et al. 2014), invasive species (Herring et al. 2015), contaminated sites (Landis et al. 2017a; Johns et al. 2017), and watershed management (Hines and Landis 2014; Graham et al. 2019).

Bibliography

  1. Ayre, K. K., & Landis, W. G. (2012). A Bayesian approach to landscape ecological risk assessment applied to the Upper Grande Ronde Watershed, Oregon. Human and Ecological Risk Assessment., 18, 946–970.CrossRefGoogle Scholar
  2. Ayre, K. K., Caldwell, C. A., Stinson, J., & Landis, W. G. (2014). Analysis of regional scale risk to whirling disease in populations of Colorado and Rio Grande cutthroat trout using a Bayesian belief network model. Risk Analysis, 34, 1589–1605.CrossRefGoogle Scholar
  3. Backus, G. A., & Gross, K. (2016). Genetic engineering to eradicate invasive mice on islands: Modeling the efficiency and ecological impacts. Ecosphere, 7(12), 1–14.CrossRefGoogle Scholar
  4. CDC. Centers for Disease Control and Prevention. (2017). About Zika. Retrieved from https://www.cdc.gov/zika/about/index.html
  5. CDC. Centers for Disease Control and Prevention. (2019). Dengue. Retrieved from https://www.cdc.gov/dengue/entomologyecology/index.html
  6. Graham, S. E., Chariton, A. A., & Landis, W. G. (2019). Using Bayesian networks to predict risk to estuary water quality and patterns of benthic environmental DNA in Queensland. Integrated Environmental Assessment and Management, 15, 93–111.CrossRefGoogle Scholar
  7. Herring, C. E., Stinson, J., & Landis, W. G. (2015). Evaluating non-indigenous species management in a Bayesian networks derived relative risk framework for Padilla Bay, Washington. Integrated Environmental Assessment and Management, 11, 640–652.CrossRefGoogle Scholar
  8. Hines, E. E., & Landis, W. G. (2014). Regional risk assessment of the Puyallup River watershed and the evaluation of low impact development in meeting management goals. Integrated Environmental Assessment and Management, 10, 269–278.CrossRefGoogle Scholar
  9. Johns, A. F., Graham, S. E., Harris, M. J., Markiewicz, A. J., Stinson, J. M., & Landis, W. G. (2017). Using the Bayesian network relative risk model risk assessment process to evaluate management alternatives for the South River and Upper Shenandoah River, Virginia. Integrated Environmental Assessment and Management, 13, 100–114.CrossRefGoogle Scholar
  10. Landis, W. G. (2004). Ecological risk assessment conceptual model formulation for nonindigenous species. Risk Analysis, 24, 847–858.CrossRefGoogle Scholar
  11. Landis, W. G., & Wiegers, J. (2005). Introduction to the regional risk assessment using the relative risk model. In W. G. Landis (Ed.), Regional scale ecological risk assessment using the relative risk model (pp. 11–36). Boca Raton: CRC Press.Google Scholar
  12. Landis, W. G., Ayre, K. K., Johns, A. F., Summers, H. M., Stinson, J., Harris, M. J., Herring, C. E., & Markiewicz, A. J. (2017a). The multiple stressor ecological risk assessment for the mercury contaminated South River and Upper Shenandoah River using the Bayesian network-relative risk model. Integrated Environmental Assessment and Management, 13, 85–99.CrossRefGoogle Scholar
  13. Landis, W. G., Markiewicz, A. J., Ayre, K. K., Johns, A. F., Harris, M. J., Stinson, J. M., & Summers, H. M. (2017b). A general risk-based adaptive management scheme incorporating the Bayesian network Relative Risk Model with the South River, Virginia, as case study. Integrated Environmental Assessment and Management, 13, 115–126.CrossRefGoogle Scholar
  14. Matysiak, M., & Roess, A. (2017). Interrelationship between climatic, ecological, social, and cultural determinants affecting dengue emergence and transmission in Puerto Rico and their implications for zika response. Journal of Tropical Medicine, 2017.  https://doi.org/10.1155/2017/8947067.CrossRefGoogle Scholar
  15. Mitchell, C., Chu, V. R., Harris, M. J., Landis, W. G., von Stackelberg, K. E., & Stark, J. D. (2018). Using metapopulation models to estimate the effects of pesticides and environmental stressors to Spring Chinook salmon in the Yakima River Basin, WA. https://cedar.wwu.edu/ssec/2018ssec/allsessions/146/. Accessed 11 Feb 2019.
  16. National Academies of Sciences, Engineering, and Medicine (NASEM). (2016). Gene drives on the horizon: Advancing science, navigating uncertainty, and aligning research with public values. Washington, DC: National Academies Press.Google Scholar
  17. Noble, C., Adlam, B., Church, G. M., Esvelt, K. M., & Nowak, M. A. (2018). Current CRISPR gene drive systems are likely to be highly invasive in wild populations. eLife, 7, e33423.CrossRefGoogle Scholar
  18. Thomas, B. W., & Taylor, R. H. (2002). A history of ground-based rodent eradication techniques developed in New Zealand, 1959-1993. In C. R. Veitch & M. N. Clout (Eds.), Turning the tide: The eradication of invasive species (pp. 301–310). Auckland: IUCN.Google Scholar
  19. Trump, B. D., Foran, C., Rycroft, T., Wood, M. D., Bandolin, N., Cains, M., et al. (2018). Development of community of practice to support quantitative risk assessment for synthetic biology products: Contaminant bioremediation and invasive carp control as cases. Environment Systems and Decisions, 38(4), 517–527.CrossRefGoogle Scholar
  20. Unckless, R. L., Clark, A. G., & Messer, P. W. (2017). Evolution of resistance against CRISPR/Cas9 gene drive. Genetics, 205(2), 827–841.CrossRefGoogle Scholar
  21. USEPA. (1998). U.S. EPA guidelines for ecological risk assessment. EPA/630/R-95/002F. Published on May 14, 1998, Federal Register 63(93):26846–26924). U. S. Environmental Protection Agency, Washington, DC, USA.Google Scholar
  22. USFWS. United States Fisheries and Wildlife Service. (2019). Retrieved from https://www.fws.gov/refuge/farallon_islands/. 24 Apr 2019.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Wayne G. Landis
    • 1
    Email author
  • Ethan A. Brown
    • 1
  • Steven Eikenbary
    • 1
  1. 1.Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington UniversityBellinghamUSA

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