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A Spatio‐Temporal Exposure‐Hazard Model for Assessing Biological Risk and Impact

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  • Emily Walker
  • Melen Leclerc
  • Jean‐François Rey
  • Rémy Beaudouin
  • Samuel Soubeyrand
  • Antoine Messéan

Abstract

We developed a simulation model for quantifying the spatio‐temporal distribution of contaminants (e.g., xenobiotics) and assessing the risk of exposed populations at the landscape level. The model is a spatio‐temporal exposure‐hazard model based on (i) tools of stochastic geometry (marked polygon and point processes) for structuring the landscape and describing the exposed individuals, (ii) a dispersal kernel describing the dissemination of contaminants from polygon sources, and (iii) an (eco)toxicological equation describing the toxicokinetics and dynamics of contaminants in affected individuals. The model was implemented in the briskaR package (biological risk assessment with R) of the R software. This article presents the model background, the use of the package in an illustrative example, namely, the effect of genetically modified maize pollen on nontarget Lepidoptera, and typical comparisons of landscape configurations that can be carried out with our model (different configurations lead to different mortality rates in the treated example). In real case studies, parameters and parametric functions encountered in the model will have to be precisely specified to obtain realistic measures of risk and impact and accurate comparisons of landscape configurations. Our modeling framework could be applied to study other risks related to agriculture, for instance, pathogen spread in crops or livestock, and could be adapted to cope with other hazards such as toxic emissions from industrial areas having health effects on surrounding populations. Moreover, the R package has the potential to help risk managers in running quantitative risk assessments and testing management strategies.

Suggested Citation

  • Emily Walker & Melen Leclerc & Jean‐François Rey & Rémy Beaudouin & Samuel Soubeyrand & Antoine Messéan, 2019. "A Spatio‐Temporal Exposure‐Hazard Model for Assessing Biological Risk and Impact," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 54-70, January.
    • Handle: RePEc:wly:riskan:v:39:y:2019:i:1:p:54-70
    DOI: 10.1111/risa.12941
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    References listed on IDEAS

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    1. Holst, Niels & Lang, Andreas & Lövei, Gabor & Otto, Mathias, 2013. "Increased mortality is predicted of Inachis io larvae caused by Bt-maize pollen in European farmland," Ecological Modelling, Elsevier, vol. 250(C), pages 126-133.
    2. Theresa Garvin, 2001. "Analytical Paradigms: The Epistemological Distances between Scientists, Policy Makers, and the Public," Risk Analysis, John Wiley & Sons, vol. 21(3), pages 443-456, June.
    3. Davies, Tilman M. & Hazelton, Martin L. & Marshall, Jonathan. C, 2011. "sparr: Analyzing Spatial Relative Risk Using Fixed and Adaptive Kernel Density Estimation in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i01).
    4. L. Robin Keller & Yitong Wang, 2017. "Information Presentation in Decision and Risk Analysis: Answered, Partly Answered, and Unanswered Questions," Risk Analysis, John Wiley & Sons, vol. 37(6), pages 1132-1145, June.
    5. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
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    Cited by:

    1. Nikolaos Argyris & Valentina Ferretti & Simon French & Seth Guikema & Gilberto Montibeller, 2019. "Advances in Spatial Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 1-8, January.

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