Identifying cost-effective conservation priorities for inland migratory shorebirds in South Korea using ensemble species distribution models

Effective conservation of migratory shorebirds requires spatial prioritization that considers both ecological suitability and socio-economic constraints. In East Asia, conservation efforts have traditionally focused on tidal flats and coastal wetlands, yet inland agricultural landscapes are increasingly recognized as important habitats during stopover and non-breeding periods. Despite this growing recognition, inland areas remain underrepresented in national conservation strategies. In this study, we identified high-priority inland conservation areas for eight migratory shorebird species in South Korea by integrating ensemble species distribution models with a human population-based cost layer. The models showed good discrimination (mean AUC = 0.80–0.92), indicating robust predictive performance. Among 229 administrative districts analyzed, 27 unprotected districts were identified as inland priority areas and classified into four priority classes. Most richness hotspots were concentrated in the central and southwestern lowlands, particularly around the Geum River Basin. Several Class 1 districts, including Gimje, Gangseo (Busan), and Yeonggwang, exhibited high species richness with low development pressure, making them strategic targets for inland–coastal connectivity. The proposed framework reveals a spatial mismatch between biodiversity hotspots and existing protected areas and provides a cost-effective, scalable approach for guiding inland conservation planning. These findings can inform agri-environmental programs and national biodiversity strategies, particularly under limited resources and increasing anthropogenic pressures along the East Asian–Australasian Flyway. • Inland hotspots in South Korea via ensemble SDMs + log(population density) cost. • Priorities cluster in lowland plains and inland–coastal transitions. • Rice paddies and wetland extent emerged as key environmental predictors. • The unprotected districts by stacked richness and richness-to-cost (classes 1–4). • Framework reveals gaps in protection and guides cost-effective conservation.