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

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.

[1]  S. K. Jenson,et al.  Extracting topographic structure from digital elevation data for geographic information-system analysis , 1988 .

[2]  Emilio Chuvieco,et al.  Mapping the Spatial Distribution of Forest Fire Danger Using GIS , 1996, Int. J. Geogr. Inf. Sci..

[3]  Brian Huntley,et al.  Climate and the distribution of Fallopia japonica: use of an introduced species to test the predictive capacity of response surfaces , 1995 .

[4]  David Howard,et al.  Interface Design for Geographic Visualization: Tools for Representing Reliability , 1996 .

[5]  J. D. Janse,et al.  Potato brown rot in western Europe – history, present occurrence and some remarks on possible origin, epidemiology and control strategies , 1996 .

[6]  Andrew A. Lovett,et al.  Assessing Hazardous Waste Transport Risks Using a GIS , 1996, Int. J. Geogr. Inf. Sci..

[7]  Stan Openshaw,et al.  Error simulation in vector GIS using neural computing methods , 1994 .

[8]  R. C. Seem Geographic Information Systems for localized pest predictions1 , 1993 .

[9]  Derek Karssenberg,et al.  Environmental Modelling in GIS , 1998 .

[10]  P. Müller,et al.  Investigation and control of potato brown rot in Germany, especially in Bayern1 , 1998 .

[11]  Richard H. A. Baker,et al.  Developing a European pest risk mapping system 1 , 1996 .

[12]  P. Reichenbach,et al.  GIS techniques and statistical models in evaluating landslide hazard , 1991 .

[13]  J. Stockwell,et al.  The U.S. EPA Geographic Information System for mapping environmental releases of Toxic Chemical Release Inventory (TRI) chemicals. , 1993, Risk analysis : an official publication of the Society for Risk Analysis.

[14]  I. M. Smith,et al.  Situation of Ralstonia solanacearum in the EPPO region in 1997 , 1998 .

[15]  Roger Bivand,et al.  Using the R statistical data analysis language on GRASS 5 , 2000 .

[16]  F. William Ravlin,et al.  Landscape framework to predict phenological events for gypsy moth (Lepidoptera: Lymantriidae) management programs , 1995 .

[17]  A A Lovett,et al.  Using GIS in Risk Analysis: A Case Study of Hazardous Waste Transport , 1997, Risk analysis : an official publication of the Society for Risk Analysis.

[18]  Susan P. Worner,et al.  Ecoclimatic assessment of potential establishment of exotic pests. , 1988 .

[19]  Philip C. Emmi,et al.  A GIS-Based Assessment of Earthquake Property Damage and Casualty Risk: Salt Lake County, Utah , 1993 .

[20]  Raymond L. Correll,et al.  A Risk Assessment Method for Biological Introductions , 1999 .

[21]  Alfred Stein,et al.  Interactive GIS for Environmental Risk Assessment , 1995, Int. J. Geogr. Inf. Sci..

[22]  Wayne L. Myers,et al.  A GIS-based approach to evaluating regional groundwater pollution potential with DRASTIC , 1990 .

[23]  C.P.J.M. van Elzakker,et al.  The visualization of GIS generated information quality , 1992 .

[24]  C.P.J.M. van Elzakker,et al.  The use of colour in the cartographic representation of information quality generated by a GIS , 1993 .

[25]  Ralph L. Keeney,et al.  Principles for Conduct of Pest Risk Analyses: Report of an Expert Workshop , 1998 .

[26]  K. Olsson,et al.  Overwintering of Pseudomonas solanacearum in Sweden. , 1976 .

[27]  R. W. Sutherst,et al.  A computerised system for matching climates in ecology , 1985 .

[28]  M. H. Royer,et al.  Application of high-resolution weather data to pest risk assessment1 , 1991 .

[29]  Matt Duckham,et al.  Assessment of error in digital vector data using fractal geometry , 2000, Int. J. Geogr. Inf. Sci..

[30]  J. V. van Elsas,et al.  Survival of Ralstonia solanacearum Biovar 2, the Causative Agent of Potato Brown Rot, in Field and Microcosm Soils in Temperate Climates. , 2000, Phytopathology.

[31]  Howard Veregin,et al.  Developing and Testing of an Error Propagation Model for GIS Overlay Operations , 1995, Int. J. Geogr. Inf. Sci..

[32]  Yue-Hong Chou,et al.  Management of wildfires with a geographical information system , 1992, Int. J. Geogr. Inf. Sci..

[33]  Philip C. Emmi,et al.  A Monte Carlo Simulation of Error Propagation in a GIS-Based Assessment of Seismic Risk , 1995, Int. J. Geogr. Inf. Sci..