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Development and validation of a weed screening tool for the United States

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Abstract

The Australian weed risk assessment has been promoted as a simple and effective screening tool that can help prevent the entry of weeds and invasive plants into new areas. On average, the Australian model identifies major-invaders more accurately than it does non-invaders (90% vs. 70% accuracy). While this difference in performance emphasizes protection, the overall accuracy of the model will be determined by its performance with non-invaders because the frequency of invasive species among new plant introductions is relatively low. In this study, we develop a new weed risk assessment model for the entire United States that increases non-invader accuracy. The new screening tool uses two elements of risk, establishment/spread potential and impact potential, in a logistic regression model to evaluate the invasive/weedy potential of a species. We selected 204 non-invaders, minor-invaders, and major-invaders to develop and validate the new model, and compare its performance to the Australian model using the same set of species. Performing better than the Australian model, our new model accurately identified 94.1% of major-invaders and 97.1% of non-invaders, without committing any false positives or false negatives. The new secondary screening tool we developed reduced the number of species requiring secondary evaluation from 22 to 12%. We expect that the new weed risk assessment model should significantly enhance the United State’s timeliness and accuracy in regulating potential weeds.

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Notes

  1. The terms “weed” and “invader” have been defined and used in a variety of different ways in the literature. It is beyond the scope of this paper to review their usage or to bring some clarity to their confounded meanings. We use these terms rather loosely and interchangeably to refer to non-native plants capable of spreading across natural or artificial landscapes and causing some type of economic or environmental harm.

  2. We follow Richardson et al.’s (2000) definition of “naturalized” as alien plants that reproduce consistently and sustain populations over many life cycles without direct human intervention in natural or human-made ecosystems. This definition is consistent with the IPPC’s (2009) definition of “established.”

  3. ROC curve analysis is an analytical tool that helps evaluate the sensitivity and specificity of a diagnostic test over a range of decision thresholds (Bewick et al. 2004; Fluss et al. 2005). It is typically used to estimate the overall predictive ability of a test and to evaluate the best decision threshold for the particular system (e.g., Nishida et al. 2009).

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Acknowledgments

We would like to thank the following risk analysts who contributed to the assessment of the 204 species: Stephen McLean, Lisa Kohl, Sharon Talley, Sarah Marnell, Stephanie Dubon, Robert Schall, and Sherrie Emerine. Edward Jones provided guidance on statistical analyses. Doria Gordon, Curtis Daehler, and Tomoko Nishida shared their data from their tests of the Australian WRA. Doria Gordon also provided some guidance on modification of the Australian WRA for use in the entire United States and shared the guidance that she and others developed for implementing the Australian WRA. We would like to thank Rob Ahern, Ashley Jackson, and Al Tasker, who provided comments on earlier versions of the manuscript and two anonymous reviewers who provided useful comments.

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Authors and Affiliations

  1. United States Department of Agriculture, Plant Protection and Quarantine, Plant Epidemiology and Risk Analysis Laboratory, 1730 Varsity Drive, Suite 300, Raleigh, NC, 27606-5202, USA

    Anthony L. Koop, Larry Fowler, Leslie P. Newton & Barney P. Caton

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  1. Anthony L. Koop
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Correspondence to Anthony L. Koop.

Appendices

Appendix 1

Risk scores and model results after secondary screening for species used in the U.S. tests of the Australian and PPQ weed risk assessments. ES and Imp represent the Establishment/Spread and Impact risk elements of the PPQ model. Model results are accept, evaluate further, or reject for the Australian WRA, and low risk, evaluate further, or high risk for the PPQ WRA. Results based on the secondary screening are indicated with a superscripted “SS” after the result (Tables 4 and 5).

Table 4 Scores and results from the two models for species in the developmental dataset
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Table 5 Scores and results from the two models for species in the validation dataset
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Appendix 2

See Table 6.

Table 6 Questions and scoring used in the final PPQ weed risk assessment
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Koop, A.L., Fowler, L., Newton, L.P. et al. Development and validation of a weed screening tool for the United States. Biol Invasions 14, 273–294 (2012). https://doi.org/10.1007/s10530-011-0061-4

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