Quantitative horizon scanning to identify species with the potential to become plant pests in new regions

The international spread of emerging and novel plant pests is increasing rapidly due to the expansion of global trade and poses substantial economic, socio-cultural, and environmental risks across ecosystems worldwide. Horizon scanning offers a proactive means of identifying emerging pests before they establish in new regions, creating valuable opportunities for prevention and risk mitigation. However, current horizon scanning approaches are largely qualitative, relying on extensive literature reviews and expert consultation that are both time-consuming and resource intensive. The Euphresco project presented here aims to strengthen and accelerate traditional horizon scanning by introducing a quantitative, highly automated modelling framework capable of screening large numbers of potential plant pests and identifying and prioritising those posing the greatest risk. The approach integrates multiple analytical components, including automated data retrieval from databases and web sources, high-throughput climate-suitability and ecological modelling, machine learning-assisted screening and analysis for informing and quantifying risk criteria, quantitative prioritisation of species according to risk, and sensitivity analysis to test the robustness of the ranking. The new approach will support evidence-based triage, enabling the most concerning pests to be rapidly identified for formal pest risk assessment and targeted surveillance. Ultimately, the project seeks to develop an open-source, robust, and maintainable toolkit that enables efficient, scalable, and automated horizon scanning for any country or region, supporting more effective integration of quantitative risk intelligence into phytosanitary decision-making.