Australia undertakes Import Risk Analysis (IRA) to determine the phytosanitary risks associated with trade commodities. In order to first demonstrate absence and then estimate probabilities of entry, establishment, spread and impact for potentially thousands of exotic plant pests not currently in Australia, spatial layers are required describing the distributions of existing pests, surveillance and response effort, points of entry (pathways), commodity movements (end use, waste points), transport networks, geographic barriers to spread, soils, climate, host vegetation layers, etc.
Because of the often substantial lag between incursion and detection, the ability to attribute an incursion to a particular trade event also requires data to be time-stamped. We argue that capturing such data layers and running risk analysis from a spatial database platform would allow a more robust, rapid and consistent approach with other distinct benefits. For example, climate matching, population dynamics, economic impact and post-detection hindcast models can be vertically integrated and tested under different scale/time scenarios; surveillance and response activities can optimise resource allocation by improved spatial understanding of risk, and data performance including capture, gaps, value, flow (including feedback loops) and sensitivity can be modelled by abandoning the traditional biosecurity ‘continuum’ (pre-border, border, post border) and applying small world network theory to biosecurity nodes (e.g. pre-border, border, surveillance, response, diagnostics, risk analysis). Although the feasibility and limits to such an approach need further exploration, it offers a potentially elegant solution to the Beale Review conundrum of recommending improved biosecurity data capture and management, but not indicating how this might be done or where effort should be apportioned.