Predicting the invasion potential of unintroduced alien plants in Korea using connectivity indices and ecological niche models

Invasive alien plants increasingly threaten biodiversity, ecosystems, and human well-being, with globalization and expanding trade networks facilitating their long-distance dispersal. To proactively mitigate these risks, Republic of Korea has implemented a management strategy that emphasizes preventing the introduction of potentially invasive species prior to their entry into the country. As part of this initiative, 853 alert alien species have been designated, including 310 plant species. Despite the increasing necessity for early intervention, quantitative assessments founded on empirical data remain insufficient in the evaluation of alert alien species. This study improves predictive risk modeling for 310 unintroduced alien plants in Republic of Korea by combining ecological niche models (ENMs) with air connectivity and maritime trade connectivity based on specific plant-related product categories such as agricultural goods, live plants, and feed. Introduction risk was estimated using air network (number of air routes) and product-based trade connectivity, species prevalence, and climatic similarity between source regions and Republic of Korea. Establishment potential was evaluated using Maxent-based ENMs with global occurrence and bioclimatic data. Results revealed that air network connectivity was a strong invasion risk predictor, emphasizing the need for surveillance at airports and air-cargo hubs. Combining connectivity indices and ENMs enabled the construction of an invasion risk matrix, identifying priority species with high introduction probability and habitat suitability. Overall, these findings highlight the utility of quantitative, pathway-based frameworks for informing proactive biosecurity policies and early warning systems. This approach supports evidence-based prioritization of monitoring and management for high-risk alien plants in Republic of Korea’s evolving biosecurity landscape.