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

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/reference/07317e2c-abb0-4ec8-8d32-e0edcf69d82a
Bibliographic fields
reftype Journal Article
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.
Author Koop, Anthony L.; Fowler, Larry; Newton, Leslie P.; Caton, Barney P.
DOI 10.1007/s10530-011-0061-4
Date February 01
ISSN 1573-1464
Issue 2
Journal Biological Invasions
Pages 273-294
Title Development and validation of a weed screening tool for the United States
Type of Article journal article
Volume 14
Year 2012
Bibliographic identifiers
_record_number 23397
_uuid 07317e2c-abb0-4ec8-8d32-e0edcf69d82a