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Spatial model for predicting the presence of cinnamon fungus (Phytophthora cinnamomi) in sclerophyll vegetation communities in south-eastern Australia

Wilson, Barbara, Lewis, Adam and Aberton, John 2003, Spatial model for predicting the presence of cinnamon fungus (Phytophthora cinnamomi) in sclerophyll vegetation communities in south-eastern Australia, Austral ecology, vol. 28, no. 2, pp. 108-115, doi: 10.1046/j.1442-9993.2003.01253.x.

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Title Spatial model for predicting the presence of cinnamon fungus (Phytophthora cinnamomi) in sclerophyll vegetation communities in south-eastern Australia
Formatted title Spatial model for predicting the presence of cinnamon fungus (Phytophthora cinnamomi) in sclerophyll vegetation communities in south-eastern Australia
Author(s) Wilson, Barbara
Lewis, Adam
Aberton, John
Journal name Austral ecology
Volume number 28
Issue number 2
Start page 108
End page 115
Publisher Blackwell Publishing Asia
Place of publication Carlton, Vic.
Publication date 2003
ISSN 1442-9985
1442-9993
Keyword(s) Australia
geographical information system (GIS)
landscape
logistic regression
pathogen distribution
Phytophthora cinnamomi
predictive model
Summary The pathogen Phytophthora cinnamomi causes extensive 'dieback' of Australian native vegetation. This study investigated the distribution of infection in an area of significant sclerophyll vegetation in Australia. It aimed to determine the relationship of infection to site variables and to develop a predictive model of infection. Site variables recorded at 50 study sites included aspect, slope, altitude, proximity to road and road characteristics, soil profile characteristics and vegetation attributes. Soil and plant tissues were assayed for the presence of the pathogen. A geographical information systyem (GIS) was employed to provide accurate estimations of spatial variables and develop a predictive model for the distribution of P. cinnamomi. The pathogen was isolated from 76% of the study sites. Of the 17 site variables initially investigated during the study a logistic regression model identified only two, elevation and sun-index, as significant in determining the probability of infection. The presence of P. cinnamomi infection was negatively associated with elevation and positively associated with sun-index. The model predicted that up to 74% of the study area (11 875 ha) had a high probability of being affected by P. cinnamomi. However, the present areas of infection were small, providing an opportunity for management to minimize spread into highly susceptible uninvaded areas.
Language eng
DOI 10.1046/j.1442-9993.2003.01253.x
Field of Research 060704 Plant Pathology
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2003
Persistent URL http://hdl.handle.net/10536/DRO/DU:30002047

Document type: Journal Article
Collection: School of Ecology and Environment
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