Application and evaluation of classification trees for screening unwanted plants

Caley, P and Kuhnert, PM (2006) Application and evaluation of classification trees for screening unwanted plants. Austral Ecology, 31 5: 647-655. doi:10.1111/j.1442-9993.2006.01617.x


Author Caley, P
Kuhnert, PM
Title Application and evaluation of classification trees for screening unwanted plants
Journal name Austral Ecology   Check publisher's open access policy
ISSN 1442-9985
Publication date 2006
Sub-type Article (original research)
DOI 10.1111/j.1442-9993.2006.01617.x
Volume 31
Issue 5
Start page 647
End page 655
Total pages 9
Place of publication Oxford
Publisher Blackwell Publishing
Collection year 2006
Language eng
Subject C1
270799 Ecology and Evolution not elsewhere classified
770703 Living resources (flora and fauna)
Abstract Risk assessment systems for introduced species are being developed and applied globally, but methods for rigorously evaluating them are still in their infancy. We explore classification and regression tree models as an alternative to the current Australian Weed Risk Assessment system, and demonstrate how the performance of screening tests for unwanted alien species may be quantitatively compared using receiver operating characteristic (ROC) curve analysis. The optimal classification tree model for predicting weediness included just four out of a possible 44 attributes of introduced plants examined, namely: (i) intentional human dispersal of propagules; (ii) evidence of naturalization beyond native range; (iii) evidence of being a weed elsewhere; and (iv) a high level of domestication. Intentional human dispersal of propagules in combination with evidence of naturalization beyond a plants native range led to the strongest prediction of weediness. A high level of domestication in combination with no evidence of naturalization mitigated the likelihood of an introduced plant becoming a weed resulting from intentional human dispersal of propagules. Unlikely intentional human dispersal of propagules combined with no evidence of being a weed elsewhere led to the lowest predicted probability of weediness. The failure to include intrinsic plant attributes in the model suggests that either these attributes are not useful general predictors of weediness, or data and analysis were inadequate to elucidate the underlying relationship(s). This concurs with the historical pessimism that we will ever be able to accurately predict invasive plants. Given the apparent importance of propagule pressure (the number of individuals of an species released), future attempts at evaluating screening model performance for identifying unwanted plants need to account for propagule pressure when collating and/or analysing datasets. The classification tree had a cross-validated sensitivity of 93.6% and specificity of 36.7%. Based on the area under the ROC curve, the performance of the classification tree in correctly classifying plants as weeds or non-weeds was slightly inferior (Area under ROC curve = 0.83 +/- 0.021 (+/- SE)) to that of the current risk assessment system in use (Area under ROC curve = 0.89 +/- 0.018 (+/- SE)), although requires many fewer questions to be answered.
Keyword Classification
Invasive Screening
Roc Curve
Tree Models
Weed Risk Assessment
Ecology
Risk-assessment
Invasive Plants
Propagule Size
North-america
Success
Weed
Introductions
Predictions
Invaders
Release
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2007 Higher Education Research Data Collection
Ecology Centre Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 39 times in Thomson Reuters Web of Science Article | Citations
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Created: Wed, 15 Aug 2007, 08:26:09 EST