ABSTRACT
Surveillance for newly detected invasive plants is expensive and is often done on an “ad hoc” basis due to limited resources. A novel way to improve surveillance efŽciency is to concentrate surveillance in areas that are more likely to contain the weed, or are more susceptible to invasion, by replicating the various dispersal syndromes and plant life history factors that in¬uence the spread of invasive plants. These areas can then be targeted for surveillance, potentially improving the success of containment and eradication efforts. Management and control actions such as eradication scenarios can then be applied to the simulated invasion and evaluated for effectiveness. This chapter presents a geographic information system (GIS) toolbox for surveillance decision support, discusses design elements of the toolbox, and demonstrates its application to Chilean needle grass (CNG) (Nassella neesiana), a highly invasive perennial tussock grass that has spread across agricultural areas of Australia. The toolbox consists of Python functions that replicate elements of invasive plant establishment, growth, and dispersal and also simulate surveillance and eradication strategies. These elements are placed in an iterative loop so the annual cycle of establishment, growth, dispersal, and management strategies can be modeled for multiple years. The toolbox is integrated with ArcGIS for direct application to known spatial information on invasive plant incursions. Simulation outputs are written to the GIS for spatial interrogation and map preparation for rapid communication to land managers. The toolbox has been designed as a decision support tool for managers and researchers of serious plant invasions and is being used in Australia for this purpose. It is available for free download at https://lir.gpa.uq.edu.au/weedtoolbox/. Tutorial information is also available at this Web site.
