Prediction of Potential Habitat for <em>Luciola unmunsana</em> Using Species Distribution Models

This study aimed to predict the potential habitats of Luciola unmunsana using a species distribution model. Luciola unmunsana is a species found only in South Korea, and its dis-tribution and conservation are relatively poorly studied because females lack wings and are difficult to collect owing to their low mobility. Therefore, we predicted the potential habitats of Luciola unmunsana across South Korea using a single model, maximum entropy (MaxEnt), and a multi-model ensemble model. The points of emergence were based on public data and previous studies from the Jeonbuk Green Environment Support Center (JGESC), Global Biodiversity Information Facility (GBIF), and National Institute of Biolog-ical Resources (NIBR). Among the input variables, the ecoclimate index built through the Shared Socioeconomic Pathways (SSP) scenario-based detailed climate change data was utilized for climate variables, and non-climate variables were built to reflect the ecological characteristics of Luciola unmunsana, such as topography, land cover, and Enhanced Vege-tation Index (EVI). The main findings of this study are summarized below. First, EVI, hy-drological network analysis, land cover, and annual precipitation (Bio12) were found to be influential in predicting potential habitats for Luciola unmunsana in both models. Second, by overlaying the predicted potential habitats and highly significant variables, we found that areas with high vegetation vigor within the forest, proximity to water systems, and relatively high annual precipitation, which can maintain stable humidity, are potential habitats for Luciola unmunsana. Third, field visits and literature surveys to predicted poten-tial habitat sites, including Geumsan-gun, Chungcheongnam-do, Yeongam-gun, Jeolla-buk-do, Mudeungsan, Gwangju-si, Korea, and Gijang-gun, Busan-si, Korea, confirmed the occurrence of Luciola unmunsana. This study is significant because it is the first to construct a national-level species distribution model for Luciola unmunsana, which is declining due to industrialization and urbanization, and to predict potential habitats by applying vari-ous environmental variables reflecting ecological characteristics, thus providing basic da-ta for the conservation and utilization of this emotional insect and environmental indica-tor species. In this study, the spatial resolution of the model was 1 × 1 km for nation-al-level studies. It is necessary to increase the accuracy of the model by including variables with higher spatial resolution when conducting regional-level studies in the future.