Detecting dispersal pathways is important both for understanding species range expansion and for managing nuisance species. However, direct detection is difficult. Here, we propose detecting these crucial pathways using a virtual ecology approach, simulating species dynamics using models, and virtual observations. As a case study, we developed a dispersal model based on cellular automata for the pest insect Stenotus rubrovittatus and simulated its expansion. We tested models for species expansion based on four landscape parameters as candidate pathways; these are river density, road density, area of paddy fields, and area of abandoned farmland, and validated their accuracy. We found that both road density and abandoned area models had prediction accuracy. The simulation requires simple data only to have predictive power, allowing for fast modeling and swift establishment of management plans.
Keywords: Agricultural risk; Management strategy; Range expansion; Risk management; Risk mapping; Virtual population.