The Siberian elm (Ulmus pumila L.) is one of the most commonly found tree species in arid areas of northeast Asia. To understand the morphological and physiological characteristics of Siberian elms in arid regions, we analyzed leaves from seven study sites (five arid or semi-arid and two mesic) in China, Mongolia and the Republic of Korea, which covered a wide range of average annual precipitation (232 mm·year⁻¹ to 1304 mm·year⁻¹) under various aridity indexes (AI) and four different microenvironments: sand dune, steppe, riverside and forest. The traits of Siberian elms varied widely along different annual precipitation (P) and AI gradients. Tree height (H), leaf size (LS) and stomatal area per unit leaf area (A_S/A_L) decreased with increasing AI, whereas leaf mass per unit leaf area (LMA) and water-use efficiency (WUE) increased significantly. In addition, trees at the five arid sites showed significant differences in LS, LMA and A_S/A_L but not in H and WUE. Thus, our study indicated that indigenous Siberian elm trees in arid areas have substantially altered their morphological and physiological characteristics to avoid heat stress and increase water conservation in comparison to mesic areas. However, their changes differed depending on the surrounding microenvironment even in arid areas. Trees in sand dunes had a smaller LS, higher LMA, thicker leaf cuticle layer and higher stomatal density and A_S than those in steppes and near a riverside.

The distribution shift of forest species due to the fact of climate change may negatively affect ecosystem services including the production of medicinal resources. Climate change impact assessments of habitat range changes are essential to sustainably manage forest resources. A change in the habitat suitability due to the fact of climate change was predicted for Angelica gigas, which has high economic value among forest medicinal resources in South Korea. The habitat suitability was predicted by an ensemble species distribution model that combined the results of nine single algorithm models using the committee averaging method. A total 168 occurrence data and 10 environmental variables were used for the single algorithm models. The area under the receiver operation characteristic curve (AUC) and true skill statistic (TSS) were applied to evaluate the models’ performance, and the contribution of the environmental variables was calculated as an important value for each single algorithm model. Climate change scenarios were projected to predict future habitat suitability. The future suitable habitat for A. gigas was gradually reduced to the high mountain regions of the eastern part of South Korea regardless of the climate change scenarios. The main environmental variable was the annual mean temperature, and the rise in temperature due to the fact of climate change was found to have a negative effect on the habitat suitability for A. gigas. The decline in the habitat suitability for A. gigas, a major forest medicinal resource, is expected to result in the reduction in its production. Therefore, it is required to establish adaptation measures to mitigate the negative impact of this decrease, such as protecting the natural habitats of A. gigas.

This study was conducted to investigate the effects of elevated air temperature and atmospheric CO₂ concentration on the growth and physiological characteristics of two-year-old seedlings of three representative species (Abies koreana, Pinus densiflora, and Quercus serrata) in Korea based on a climate change scenario. Our study used a temperature gradient chamber (TGC; aC-eT) and CO₂-temperature gradient chamber (CTGC; eC-eT) to simulate climate change conditions under ambient and elevated CO₂ concentration and air temperature. Net photosynthetic rate (Pn) and biomass were measured and carboxylation efficiency (CE) and biomass allocation were calculated to examine the physiological responses of the seedlings. A. koreana and P. densiflora were negatively impacted by warming but Pn and CE increased under the eC-eT condition while Q. serrata showed decreased Pn and CE during the second year. There were significant differences in total biomass in P. densiflora, but showed no differences in Q. serrata. Shoot/root ratio in Q. serrata showed a significant increase under the aC-eT condition. Our results highlight that further investigation of species‐specific physiological responses and carbon allocation under artificial climate conditions is needed.

The subalpine evergreen coniferous forest in Republic of Korea is a representative vulnerable forest facing risk such as area reduction and massive death due to warming temperature and extreme weather caused by climate change. Multiple policy and research efforts exist to conserve biodiversity in the vulnerable subalpine evergreen conifer forest. However, due to a lack of linkages between scientific information and practical management on the site, progressive implementation of adaptation management in the subalpine evergreen conifer forest has been limited. This study aims to suggest a tentative framework for utilizing scientific information on the impact of climate change on subalpine evergreen coniferous forests to discover and apply adaptation measures for local conservation. The status of information on the impact of climate change on subalpine evergreen coniferous species was reviewed by dividing it into field monitoring, physiological response experiments, and projection of changes in potential habitat according to climate change scenarios. Based on the understanding of this information, it was proposed a stepwised approach to the discovery of adaptation measures for in-situ conservation, including problem identification, target site selection, screening of potential measures, and feasibility review. This approach is expected to contribute to decision-making and establishment of adaptation strategies based on scientific information on the impact of climate change for the sustainable conservation of subalpine evergreen coniferous forests.

Climate change is rapidly altering the distribution and abundance of species, with significant impacts on regional ecosystems, including reduced ecosystem services and the loss of biodiversity. Accurately predicting changes in the distribution and abundance of taxa under future climate scenarios is, therefore, crucial. In South Korea, beetle data collected via pitfall traps from approximately 300 forest sites between 2007 and 2009 (30 families, 4 genera, and 150 species) were used to forecast changes in their abundance and distribution under climate change scenarios RCP 4.5 and 8.5. This study evaluated the accuracy of those predictions using data from a subsequent survey conducted between 2017 and 2019. We compared species richness, abundance, changes in abundance (i.e., number of individuals), and occurrence (i.e., number of occupied sites) using data from 273 sites that were surveyed in both the initial (2007–2009) and follow-up (2017–2019) periods. All four parameters were found to be significantly influenced by the identifiers. This identifier bias was attributed to the omission of morphologically similar species in the initial survey or the loss of individuals during the preparation process of dry specimens. As a result, increases in abundance and distribution appear to have been affected by identification errors, whereas decreases more closely reflect actual ecological changes. When the comparison between predicted and observed results was restricted to taxa with reduced abundance and distribution, the number of taxa that matched the predictions was significantly higher than that of those that did not. Based on ease of identification, abundance, and sensitivity to climate change, we selected a set of indicator taxa (four families, two genera, and seven species) for climate change monitoring.