In the Footsteps of Cows: Using Livestock Tracing Technology to Trace and Predict New Weed Incursions of Tropical Soda Apple (Solanum viarum) in Australia. (March 2016)

• Record Type: Journal Article
• Authors: Charlton, Scott; Henderson, Robyn; McGowen, Ian; Dominiak, Bernie; Michelmore, Michael
• Is Part Of: Invasive plant science and management. Volume 9:Number 1(2016)
• Page Start: 55
• Page End: 59
• Publication Date: 2016-03
• Subjects: Tropical soda apple, Solanum viarum Dunal; Data warehousing, -- national livestock identification system, -- NLIS, -- pathway analysis, -- tracing; Plants, Protection of -- Periodicals; Invasive plants -- Periodicals; 632.5
• DOI: 10.1614/IPSM-D-14-00079.1
• Languages: English
• ISSNs: 1939-7291

Abstract

Abstract: Tropical soda apple is an aggressive prickly perennial shrub growing up to 2 m high. It invades open to semishaded areas, including pastures, forests, riparian zones, roadsides, recreational areas, and horticultural and cropping areas. In Australia during August 2010, the weed was identified on the New South Wales Mid North Coast. It is believed that tropical soda apple has been present in this area for a number of years and both systematic and ad hoc surveys have found the weed in other satellite locations. The discovery of tropical soda apple at several cattle handling facilities indicated that cattle are a significant vector for the weed. The aim of this project was to use the National Livestock Identification System (NLIS) data to trace cattle movements from affected properties throughout New South Wales and into other Australian states. This has proved advantageous, as there are few other nonecological mechanisms to systematically trace significant weed movement. We have been able to conduct a pathway analysis of where this weed is likely to occur across New South Wales through the use of NLIS. Importantly, we can use this information to pinpoint surveillance activities for local managers, thus ensuring better use of resources. We have also been able to create a stochastic model for incursions at these sites using information gleaned from the NLIS data.