Climate change is recognized as a major threat to global biodiversity and has already caused extensive regional extinction. In particular danger are the plant habitats in subalpine zones, which are more vulnerable to climate change. Evergreen coniferous trees in South Korean subalpine zones are currently designated as a species that need special care given their conservation value, but the reason for their decline and its seriousness remains unclear. This research estimates the potential land suitability (LS) of the subalpine zones in South Korea for six coniferous species vulnerable to climate change in the current time (1970–2000) and two future periods, the 2050s (2041–2060) and the 2070s (2061–2080). We analyze the ensemble-averaged loss of currently suitable habitats in the future, using nine species distribution models (SDMs). Korean arborvitae (Thuja koraiensis) and Khingan fir (Abies nephrolepis) are two species expected to experience significant habitat losses in 2050 (−59.5% under Representative Concentration Pathway (RCP) 4.5 to −65.9% under RCP 8.5 and −56.3% under RCP 4.5 to −57.7% under RCP 8.5, respectively). High extinction risks are estimated for these species, due to the difficulty of finding other suitable habitats with high LS. The current habitat of Korean fir (Abies koreana), listed as a threatened species on the International Union for Conservation of Nature (IUCN) Red List, is expected to decrease by −23.9% (RCP 4.5) to −28.4% (RCP 8.5) and −36.5% (RCP 4.5) to −36.7% (RCP 8.5) in the 2050s and 2070s, respectively. Still, its suitable habitats are also estimated to expand geographically toward the northern part of the Baekdudaegan mountain range. In the context of forest management and adaptation planning, the multi-model ensemble approach to mapping future shifts in the range of subalpine tree species under climate change provides robust information about the potential distribution of threatened and endanger

The consistent demand for development of forest lands in South Korea has resulted in the need for a new approach to estimate environmental impacts in order to sustainably manage forests. In this study, two types of development were selected: golf courses and industrial complexes. Using FRAGSTATS ver. 4.2 (University of Massachussetts, Amherst, MA, USA), the fragmentation effects of each development type were analyzed based on forest area within project sites and buffer zones ranging up to 2000 m. Each type had representative landscape metrics reflecting the average impact ranges by forest area: “Number of Patches”, “Patch Density” and “Total Edge Length” for golf courses; “Number of Patches”, “Patch Density” and “Connectance Index” for industrial complexes. Golf courses with the smallest forest area had a larger impact range than those with larger forest areas. For industrial complexes, the impact range increased with forest area. Although individual sites exhibited some variation in impact range, they were generally consistent with the overall patterns observed. Investigating tree growth by buffer zone showed the ecological effect of development. To comprehensively manage development of forest lands, further research on other development types is needed. These results could be useful for creating a decision-making system with regard to development on forest lands.

Potential shifts in the suitable habitat of limestone-dwelling plant species due to climate change are of considerable biogeographical significance. The species distribution was projected using maximum entropy (MaxEnt) modeling, a widely recognized approach based on machine learning and statistical analysis. This study utilizes the MaxEnt model to develop a potential distribution map for Rhododendron micranthum, examining its spatial patterns to forecast potential impacts under both present and future climate scenarios. The model exhibited robust performance, with a notably high average area under the curve (AUC), underscoring its reliability and accuracy. Its effectiveness in predicting species distributions based on climate datasets provides critical insights into the potential habitats of limestone regions. A significant contribution of this model lies in its capacity to inform future field surveys, monitoring activities, and management strategies. The findings of this study indicate significant variability in results depending on the climate dataset utilized, underscoring the importance of meticulous selection of climate variables. Ultimately, the application of this model deepens our understanding of regional ecological dynamics and climate behavior, offering a solid scientific basis for biodiversity conservation. Focused conservation efforts in climate-sensitive areas, particularly for geographically isolated species, could accelerate research and lead to more effective species management approaches.