Biological Invasions

, Volume 20, Issue 3, pp 679–694 | Cite as

Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States

  • Roger Magarey
  • Leslie Newton
  • Seung Cheon Hong
  • Yu Takeuchi
  • David Christie
  • Catherine S. Jarnevich
  • Lisa Kohl
  • Martin Damus
  • Steven I. Higgins
  • Leah Millar
  • Karen Castro
  • Amanda West
  • John Hastings
  • Gericke Cook
  • John Kartesz
  • Anthony L. Koop
Original Paper


This study compares four models for predicting the potential distribution of non-indigenous weed species in the conterminous U.S. The comparison focused on evaluating modeling tools and protocols as currently used for weed risk assessment or for predicting the potential distribution of invasive weeds. We used six weed species (three highly invasive and three less invasive non-indigenous species) that have been established in the U.S. for more than 75 years. The experiment involved providing non-U. S. location data to users familiar with one of the four evaluated techniques, who then developed predictive models that were applied to the United States without knowing the identity of the species or its U.S. distribution. We compared a simple GIS climate matching technique known as Proto3, a simple climate matching tool CLIMEX Match Climates, the correlative model MaxEnt, and a process model known as the Thornley Transport Resistance (TTR) model. Two experienced users ran each modeling tool except TTR, which had one user. Models were trained with global species distribution data excluding any U.S. data, and then were evaluated using the current known U.S. distribution. The influence of weed species identity and modeling tool on prevalence and sensitivity effects was compared using a generalized linear mixed model. Each modeling tool itself had a low statistical significance, while weed species alone accounted for 69.1 and 48.5% of the variance for prevalence and sensitivity, respectively. These results suggest that simple modeling tools might perform as well as complex ones in the case of predicting potential distribution for a weed not yet present in the United States. Considerations of model accuracy should also be balanced with those of reproducibility and ease of use. More important than the choice of modeling tool is the construction of robust protocols and testing both new and experienced users under blind test conditions that approximate operational conditions.


Weed risk assessment Climate Modeling Invasive species 



We thank USDA-APHIS-PPQ for funding. We thank Dr. Jose Lopez-Collado, Department of Tropical Agroecosystems, Colegio de Postgraduados; Dr. Tony Arthur, Department of Agriculture, Australia; and Dr. Anna Szyniszewska, Rothamsted Research, UK for commenting on model results as part of the Model Inter-Comparison Focus group from the Seventh International Pest Risk Mapping Workgroup (now the International Pest Risk Research Group) held in Raleigh, NC, October, 2013. The first author would like to acknowledge USDA-NIFA AFRI Competitive Grants Program Food Security Challenge Area grant 2015-68004-23179. We thanks Nicholas Young of Colorado State University for reviewing the manuscript. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. We also would like to thank two anonymous reviewers for their valuable comments.

Supplementary material

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Supplementary material 1 (DOCX 28 kb)
10530_2017_1567_MOESM2_ESM.pdf (1.6 mb)
Supplementary material 2 (PDF 1604 kb)
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Supplementary material 3 (PDF 1095 kb)


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Roger Magarey
    • 1
  • Leslie Newton
    • 2
  • Seung Cheon Hong
    • 1
  • Yu Takeuchi
    • 1
  • David Christie
    • 1
  • Catherine S. Jarnevich
    • 3
  • Lisa Kohl
    • 2
    • 9
  • Martin Damus
    • 4
  • Steven I. Higgins
    • 5
  • Leah Millar
    • 2
  • Karen Castro
    • 4
  • Amanda West
    • 6
  • John Hastings
    • 1
  • Gericke Cook
    • 7
  • John Kartesz
    • 8
  • Anthony L. Koop
    • 2
  1. 1.Center for IPMNorth Carolina State UniversityRaleighUSA
  3. 3.U.S. Geological Survey, Fort Collins Science CenterFort CollinsUSA
  4. 4.Canadian Food Inspection AgencyOttawaCanada
  5. 5.Plant EcologyUniversity of BayreuthBayreuthGermany
  6. 6.Natural Resource and Ecology LaboratoryColorado State UniversityFort CollinsUSA
  7. 7.USDA-APHIS-CPHSTFort CollinsUSA
  8. 8.Biota of North America ProgramChapel HillUSA
  9. 9.USDA-APHIS-PIMRiverdaleUSA

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