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FinnPRIO: a model for ranking invasive plant pests based on risk

Abstract

The number of plant pests that may be transported to new areas with international trade is too great for subjecting all of them to a full scale pest risk assessment. There is therefore a need for a quick risk assessment procedure that also ranks the pests according to their risk. The FinnPRIO model can be used to assess the risk of alien plant pests for Finland. It follows the basic structure of a full scale pest risk assessment, i.e. it can be used to separately estimate the probabilities of entry, establishment (including spread), and the likely impacts. The model also includes a section for assessing preventability and controllability of a pest invasion. The model consists of multiple-choice questions with answer options yielding a different number of points. For each question the most likely answer option and the plausible minimum and maximum options are chosen. The total risk score is simulated using a PERT distribution, providing a scale of potential risk for each pest and indicating the level of uncertainty associated with the assessment. The model is accompanied by a guide for the interpretation of the questions and answer options. The model’s functionality has been tested through simulations, and it has been validated by comparing pest rankings produced using the model to those obtained in expert workshops. To date, 95 pests have been assessed with the model. The results indicate that the model is well capable of differentiating pests based on their estimated risk.

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Acknowledgments

This study was funded by the Finnish Ministry of Agriculture and Forestry.

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Correspondence to Jaakko Heikkilä.

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Heikkilä, J., Tuomola, J., Pouta, E. et al. FinnPRIO: a model for ranking invasive plant pests based on risk. Biol Invasions 18, 1827–1842 (2016). https://doi.org/10.1007/s10530-016-1123-4

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  • DOI: https://doi.org/10.1007/s10530-016-1123-4

Keywords

  • Invasive alien species
  • Plant health
  • Prioritisation
  • Ranking
  • Risk assessment
  • Expert judgment