The Development of a Plant Risk Evaluation (PRE) Tool for Assessing the Invasive Potential of Ornamental Plants |
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| Authors: | Christiana Conser Lizbeth Seebacher David W Fujino Sarah Reichard Joseph M DiTomaso |
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| Institution: | 1. Department of Plant Sciences, University of California Davis, Davis, California, United States of America.; 2. Washington State Department of Ecology, Lacey, Washington, United States of America.; 3. University of California Center for Urban Horticulture, Davis, California, United States of America.; 4. University of Washington Botanical Gardens, Seattle, Washington, United States of America.; Shandong University, CHINA, |
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| Abstract: | Weed Risk Assessment (WRA) methods for evaluating invasiveness in plants have evolved rapidly in the last two decades. Many WRA tools exist, but none were specifically designed to screen ornamental plants prior to being released into the environment. To be accepted as a tool to evaluate ornamental plants for the nursery industry, it is critical that a WRA tool accurately predicts non-invasiveness without falsely categorizing them as invasive. We developed a new Plant Risk Evaluation (PRE) tool for ornamental plants. The 19 questions in the final PRE tool were narrowed down from 56 original questions from existing WRA tools. We evaluated the 56 WRA questions by screening 21 known invasive and 14 known non-invasive ornamental plants. After statistically comparing the predictability of each question and the frequency the question could be answered for both invasive and non-invasive species, we eliminated questions that provided no predictive power, were irrelevant in our current model, or could not be answered reliably at a high enough percentage. We also combined many similar questions. The final 19 remaining PRE questions were further tested for accuracy using 56 additional known invasive plants and 36 known non-invasive ornamental species. The resulting evaluation demonstrated that when “needs further evaluation” classifications were not included, the accuracy of the model was 100% for both predicting invasiveness and non-invasiveness. When “needs further evaluation” classifications were included as either false positive or false negative, the model was still 93% accurate in predicting invasiveness and 97% accurate in predicting non-invasiveness, with an overall accuracy of 95%. We conclude that the PRE tool should not only provide growers with a method to accurately screen their current stock and potential new introductions, but also increase the probability of the tool being accepted for use by the industry as the basis for a nursery certification program. |
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