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

dc.contributor.author Widrlechner, Mark
dc.contributor.author Thompson, Janette
dc.contributor.author Kapler, Emily
dc.contributor.author Kordecki, Kristen
dc.contributor.author Gates, Galen
dc.contributor.author Dixon, Philip
dc.contributor.department Natural Resource Ecology and Management
dc.contributor.department Agronomy
dc.contributor.department Statistics
dc.contributor.department North Central Regional Plant Introduction Station
dc.contributor.department Horticulture
dc.date 2018-02-13T06:01:34.000
dc.date.accessioned 2020-06-30T06:10:41Z
dc.date.available 2020-06-30T06:10:41Z
dc.date.embargo 2013-02-09
dc.date.issued 2009-12-01
dc.description.abstract <p>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.</p>
dc.description.comments <p>This article is from <em>Journal of Environmental Horticulture </em>27, no. 4 (December 2009): 241–250.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/ncrpis_pubs/23/
dc.identifier.articleid 1018
dc.identifier.contextkey 3674057
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ncrpis_pubs/23
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/56001
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ncrpis_pubs/23/JEH_2027_4_241_250.pdf|||Fri Jan 14 22:45:17 UTC 2022
dc.subject.disciplines Agriculture
dc.subject.disciplines Applied Statistics
dc.subject.disciplines Biostatistics
dc.subject.disciplines Horticulture
dc.subject.disciplines Natural Resources Management and Policy
dc.subject.disciplines Plant Sciences
dc.subject.keywords exotic plant
dc.subject.keywords invasive plants
dc.subject.keywords life history
dc.subject.keywords native range
dc.subject.keywords risk assessment
dc.subject.keywords shrub
dc.subject.keywords tree
dc.subject.keywords validation
dc.subject.keywords Natural Resource Ecology and Management
dc.subject.keywords Statistics
dc.title A Test of Four Models to Predict the Risk of Naturalization of Non-native Woody Plants in the Chicago Region
dc.type article
dc.type.genre article
dspace.entity.type Publication
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