Science to practice: Machine learning-based risk analyses for optimising bark beetle management in Austrian forestry

Bark beetle outbreaks, particularly by the European spruce bark beetle (Ips typographus), represent the most significant biotic disturbances in Austrian forests, with serious ecological and economic consequences. In view of the increasing damage recorded across Europe over the last two decades, there is a great demand for practical risk assessment and early warning systems to support bark beetle management. The aim of the proposed project is to further develop the Austrian bark beetle dashboard, which was published in May 2024, using machine learning-based risk assessment and prediction models. These models are intended to predict the expected extent of damage at the forest district level (impact model) and the probability of damage at a high resolution of 10 metres for the whole of Austria (probability model). Through the integration of modern remote sensing technologies and machine learning methods, the bark beetle dashboard is to be further developed in the medium term into a holistic risk assessment system that can also function as an early warning system. This will provide efficient support to the forestry industry in mitigating damage caused by the European spruce bark beetle and other bark beetles, adapting European forests to climate change by converting them into mixed forests, and preserving important ecosystem services for future generations.