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Spatial Stochastic Simulation Offers Potential as a Quantitative Method for Pest Risk Analysis

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  • Trond Rafoss

Abstract

Pest risk analysis represents an emerging field of risk analysis that evaluates the potential risks of the introduction and establishment of plant pests into a new geographic location and then assesses the management options to reduce those potential risks. Development of new and adapted methodology is required to answer questions concerning pest risk analysis of exotic plant pests. This research describes a new method for predicting the potential establishment and spread of a plant pest into new areas using a case study, Ralstonia solanacearum, a bacterial disease of potato. This method combines current quantitative methodologies, stochastic simulation, and geographic information systems with knowledge of pest biology and environmental data to derive new information about pest establishment potential in a geographical region where a pest had not been introduced. This proposed method extends an existing methodology for matching pest characteristics with environmental conditions by modeling and simulating dissemination behavior of a pest organism. Issues related to integrating spatial variables into risk analysis models are further discussed in this article.

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  • Trond Rafoss, 2003. "Spatial Stochastic Simulation Offers Potential as a Quantitative Method for Pest Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 23(4), pages 651-661, August.
    • Handle: RePEc:wly:riskan:v:23:y:2003:i:4:p:651-661
    DOI: 10.1111/1539-6924.00344
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    References listed on IDEAS

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    1. Adrian R. Harris & Raymond L. Correll & Paul G. Adkins, 1999. "A Risk Assessment Method for Biological Introductions," Risk Analysis, John Wiley & Sons, vol. 19(3), pages 327-334, June.
    2. Andrew A. Lovett & Julian P. Parfitt & Julii S. Brainard, 1997. "Using GIS in Risk Analysis: A Case Study of Hazardous Waste Transport," Risk Analysis, John Wiley & Sons, vol. 17(5), pages 625-633, October.
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    Cited by:

    1. Frank H. Koch & Denys Yemshanov & Daniel W. McKenney & William D. Smith, 2009. "Evaluating Critical Uncertainty Thresholds in a Spatial Model of Forest Pest Invasion Risk," Risk Analysis, John Wiley & Sons, vol. 29(9), pages 1227-1241, September.
    2. Liu, Shuang & Aurambout, Jean-Philippe & Villalta, Oscar & Edwards, Jacqueline & De Barro, Paul & Kriticos, Darren J. & Cook, David C., 2015. "A structured war-gaming framework for managing extreme risks," Ecological Economics, Elsevier, vol. 116(C), pages 369-377.
    3. David Makowski & Murthy Narasimha Mittinty, 2010. "Comparison of Scoring Systems for Invasive Pests Using ROC Analysis and Monte Carlo Simulations," Risk Analysis, John Wiley & Sons, vol. 30(6), pages 906-915, June.
    4. Yemshanov, Denys & Haight, Robert G. & Koch, Frank H. & Lu, Bo & Venette, Robert & Fournier, Ronald E. & Turgeon, Jean J., 2017. "Robust Surveillance and Control of Invasive Species Using a Scenario Optimization Approach," Ecological Economics, Elsevier, vol. 133(C), pages 86-98.
    5. Allison C. Reilly & Andrea Staid & Michael Gao & Seth D. Guikema, 2016. "Tutorial: Parallel Computing of Simulation Models for Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1844-1854, October.
    6. Denys Yemshanov & Frank H. Koch & Yakov Ben‐Haim & William D. Smith, 2010. "Robustness of Risk Maps and Survey Networks to Knowledge Gaps About a New Invasive Pest," Risk Analysis, John Wiley & Sons, vol. 30(2), pages 261-276, February.
    7. Denys Yemshanov & Frank H. Koch & Daniel W. McKenney & Marla C. Downing & Frank Sapio, 2009. "Mapping Invasive Species Risks with Stochastic Models: A Cross‐Border United States‐Canada Application for Sirex noctilio Fabricius," Risk Analysis, John Wiley & Sons, vol. 29(6), pages 868-884, June.
    8. David C. Cook & Shuang Liu & Brendan Murphy & W. Mark Lonsdale, 2010. "Adaptive Approaches to Biosecurity Governance," Risk Analysis, John Wiley & Sons, vol. 30(9), pages 1303-1314, September.

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