Using geospatial intelligence to assess the invasive threat of Chilean needle grass (Nassella neesiana).
Chilean needle grass (CNG, Nassella neesiana) is an exotic perennial tussock grass that favours temperate regions with high rainfall. It is a Weed of National Significance as it is threatening native grasslands and productive pastures. CNG was discovered in the Clifton area on the Southern Darling Downs region of Queensland. It was believed to have originated from the show grounds in 1998. An emergency response to contain the outbreak included an aggressive surveillance program and eradication campaign. This summary outlines a research project for a weed surveillance decision support system using the above outbreak as a case study. From a surveillance perspective the aim is to demonstrate that discovery and control efforts pay off to efficiently: (i) contain the spread of the weed, or (ii) eradicate the weed. A risk assessment approach with a qualitative model constructed for spatial attributes using a Bayesian Belief Network (BBN) and a geographical information system was adopted. To date the findings for the model show the causal relationship between susceptibility and the extent of invasion, but poorly captures relationships to management practices. Further data is needed to understand surveillance practices and the relationship between detection effort and discovery success. Future efforts will focus on incorporating this relationship in the BBN model.