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A Test of Four Models to Predict the Risk of Naturalization of Non-Native Woody Plants in the Chicago Region

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A Test of Four Models to Predict the Risk of Naturalization of Non-Native Woody Plants in the Chicago Region North Central Regional Plant Introduction NCRPIS Publications and Papers Station 12-2009 A Test of Four Models to Predict the Risk of Naturalization of Non- native Woody Plants in the Chicago Region Mark P. Widrlechner United States Department of Agriculture, [email protected] Janette R. Thompson Iowa State University, [email protected] Emily J. Kapler Iowa State University Kristen Kordecki Chicago Botanic Garden Philip M. Dixon Iowa State University, [email protected] See next page for additional authors Follow this and additional works at: https://lib.dr.iastate.edu/ncrpis_pubs Part of the Agriculture Commons, Applied Statistics Commons, Biostatistics Commons, Horticulture Commons, and the Natural Resources Management and Policy Commons The complete bibliographic information for this item can be found at https://lib.dr.iastate.edu/ ncrpis_pubs/23. For information on how to cite this item, please visit http://lib.dr.iastate.edu/ howtocite.html. This Article is brought to you for free and open access by the North Central Regional Plant Introduction Station at Iowa State University Digital Repository. It has been accepted for inclusion in NCRPIS Publications and Papers by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. A Test of Four Models to Predict the Risk of Naturalization of Non-native Woody Plants in the Chicago Region Abstract Accurate methods to predict the naturalization of non-native woody plants are key components of risk- management programs being considered by nursery and landscape professionals. The objective of this study was to evaluate four decision-tree models to predict naturalization (fi rst tested in Iowa) on two new sets of data for non-native woody plants cultivated in the Chicago region. We identifi ed life-history traits and native ranges for 193 species (52 known to naturalize and 141 not known to naturalize) in two study areas within the Chicago region. We used these datasets to test four models (one continental-scale and three regional-scale) as a form of external validation. Application of the continental-scale model resulted in classifi cation rates of 72–76%, horticulturally limiting (false positive) error rates of 20–24%, and biologically signifi cant (false negative) error rates of 5–6%. Two regional modifi cations to the continental model gave increased classifi cation rates (85–93%) and generally lower horticulturally limiting error rates (16–22%), but similar biologically signifi cant error rates (5–8%). A simpler method, the CART model developed from the Iowa data, resulted in lower classifi cation rates (70–72%) and higher biologically signifi cant error rates (8–10%), but, to its credit, it also had much lower horticulturally limiting error rates (5–10%). A combination of models to capture both high classifi cation rates and low error rates will likely be the most effective until improved protocols based on multiple regional datasets can be developed and validated. Keywords exotic plant, invasive plants, life history, native range, risk assessment, shrub, tree, validation, Natural Resource Ecology and Management, Statistics Disciplines Agriculture | Applied Statistics | Biostatistics | Horticulture | Natural Resources Management and Policy | Plant Sciences Comments This article is from Journal of Environmental Horticulture 27, no. 4 (December 2009): 241–250. Authors Mark P. Widrlechner, Janette R. Thompson, Emily J. Kapler, Kristen Kordecki, Philip M. Dixon, and Galen Gates This article is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/ncrpis_pubs/23 A Test of Four Models to Predict the Risk of Naturalization of Non-native Woody Plants in the Chicago Region1 Mark P. Widrlechner2, Janette R. Thompson3, Emily J. Kapler4, Kristen Kordecki5, Philip M. Dixon6, and Galen Gates7 United States Department of Agriculture — Agricultural Research Service North Central Regional Plant Introduction Station Departments of Agronomy and Horticulture Iowa State University, Ames, Iowa 50011 Abstract Accurate methods to predict the naturalization of non-native woody plants are key components of risk-management programs being considered by nursery and landscape professionals. The objective of this study was to evaluate four decision-tree models to predict naturalization (fi rst tested in Iowa) on two new sets of data for non-native woody plants cultivated in the Chicago region. We identifi ed life-history traits and native ranges for 193 species (52 known to naturalize and 141 not known to naturalize) in two study areas within the Chicago region. We used these datasets to test four models (one continental-scale and three regional-scale) as a form of external validation. Application of the continental-scale model resulted in classifi cation rates of 72–76%, horticulturally limiting (false positive) error rates of 20–24%, and biologically signifi cant (false negative) error rates of 5–6%. Two regional modifi cations to the continental model gave increased classifi cation rates (85–93%) and generally lower horticulturally limiting error rates (16–22%), but similar biologically signifi cant error rates (5–8%). A simpler method, the CART model developed from the Iowa data, resulted in lower classifi cation rates (70–72%) and higher biologically signifi cant error rates (8–10%), but, to its credit, it also had much lower horticulturally limiting error rates (5–10%). A combination of models to capture both high classifi cation rates and low error rates will likely be the most effective until improved protocols based on multiple regional datasets can be developed and validated. Index words: exotic plant, invasive plants, life history, native range, risk assessment, shrub, tree, validation. Signifi cance to the Nursery Industry periods before new woody plants are released for introduction Nursery and landscape professionals continue to search is the use of predictive modeling to assess risk associated for new woody landscape plants that will provide consumers with certain plants. Use of models that predict plant inva- with visual interest and diversity in a variety of managed siveness (which classify species into categories of ‘accept’, settings. Interest in identifying and propagating additional ‘reject’, or ‘analyze further’) could allow time-consuming native species helps to address consumer demand for new and expensive fi eld screening to be focused only on species plant materials. At the same time, broader use of new, non- of greatest concern or those for which basic information is native species is likely to continue. Expanding the palette of lacking. This paper describes efforts to fi nd accurate, rapid, both native and non-native species used in the landscape is and relatively easy-to-use methods for identifying species of concern to scientists, managers of natural areas, horticul- that could become invasive pests. Our results suggest that turists, and some members of the general public due to the the power and accuracy of regional-scale models that include potential for widely used introductions to become invasive both life-history and geographic-risk components make pests. Although relatively few of these non-native woody them very useful as part of an overall risk-management species will naturalize, and even fewer are likely to become program. invasive, invasions can have signifi cant impacts and their control is diffi cult and costly. One alternative to lengthy test Introduction Many useful, non-native species of woody plants have been imported and disseminated in the United States for commercial, conservation, and ornamental purposes (15, 1Received for publication May 13, 2009; in revised form August 3, 2009. Journal paper of the Iowa Agriculture and Home Economics Experiment 22, 23). Although relatively few of these species have be- Station, Ames, IA, and supported by Hatch Act, McIntire-Stennis, and come naturalized, and fewer still can be defi ned as invasive, State of Iowa funds. The authors thank Candice Gardner, Doria Gordon, controlling the spread of those that have disrupted natural Jeffery Iles, Peter Bristol, and two anonymous reviewers for their valuable plant communities or agricultural systems is very diffi cult critiques. We are also grateful for fi nancial support received from USDA- ARS through the Floral and Nursery Crops Research Initiative. Mention of and often costly (28). A high percentage of invaders were commercial brand names does not constitute an endorsement of any product originally introduced for horticultural use (1), giving horti- by the U.S. Department of Agriculture or cooperating agencies. culturists a critical role in the prevention of future invasive 2USDA-ARS Horticulturist and Assistant Professor (Collaborator), Depart- threats. For all parties involved in the introduction of new ments of Agronomy and Horticulture, Iowa State University. landscape plants, this responsibility is particularly salient 3Associate Professor, Department of Natural Resource Ecology and Man- given the growth of global trade and high demand for new agement, Iowa State University. cultivars and the importation of new taxa (4, 15). 4Graduate Research Assistant, Department of Natural Resource Ecology and Management, Iowa State University. Identifying a reliable method for accurately predicting 5Woody Plants Specialist, Chicago Botanic Garden, Glencoe, IL; current potential invasiveness for otherwise desirable exotic species affi liation: Watershed Coordinator, Acadiana Resource Conservation and is a rapidly
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