Wilson, B.A., Lewis, A., and Aberton, J. (2003) Spatial model for predicting the presence of cinnamon fungus (Phytophthora cinnamomi) in sclerophyll vegetation communities in south-eastern Australia. Austral Ecology, 28 (2). pp. 108-115.

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Abstract

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.

Item ID:	13713
Item Type:	Article (Research - C1)
ISSN:	1442-9993
Keywords:	Australia; goegraphical information system (GIS); landscape; logistic regression; pathogen distribution; predictive model; Pytophthora cinnamomi
Additional Information:	No current JCU email address available.
Date Deposited:	29 Nov 2010 04:03
FoR Codes:	05 ENVIRONMENTAL SCIENCES > 0599 Other Environmental Sciences > 059999 Environmental Sciences not elsewhere classified @ 100%
SEO Codes:	96 ENVIRONMENT > 9604 Control of Pests, Diseases and Exotic Species > 960499 Control of Pests, Diseases and Exotic Species not elsewhere classified @ 100%
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