Invasive species pose significant threats to biodiversity, ecosystem services, and local economies, necessitating tools that can anticipate spread and evaluate management strategies. The Asian long-horned beetle (Anoplophora glabripennis; ALB) is a destructive wood-boring insect that has caused major concern in Ontario due to its ability to kill a wide range of hardwood tree species, leading to costly eradication efforts and urban canopy loss. We developed a spatially explicit state-and-transition simulation model (STSM) to forecast ALB invasion dynamics across heterogeneous landscapes in Ontario, Canada. Using the STSM framework within SyncroSim, we simulated a historical infestation in Mississauga and a hypothetical invasion in Muskoka under alternative management scenarios.

The model represents invasion as transitions among discrete landscape states, capturing infestation progression, detection, dispersal, and management. Simulations show that early detection combined with aggressive removal can achieve rapid eradication but with short-term loss of host tree cover. In contrast, delayed or absent intervention leads to widespread infestation and near-complete loss of susceptible host species. Landscape context influenced outcomes, with more connected, host-rich environments amplifying spread and impact.

These results highlight trade-offs between immediate management costs and long-term ecological consequences, emphasizing the importance of early intervention to mitigate biodiversity loss. Our work demonstrates how spatial forecasting tools can support invasive species management by quantifying risks, evaluating interventions, and identifying vulnerable landscapes, providing a transferable framework for biodiversity conservation under increasing invasion pressure.

Keywords: Ecological forecasting, invasive species, state-and-transition models, biodiversity conservation, decision support