The microbial community interacts with the environment and the health and immune function of its host both directly and indirectly. However, very few studies about microbial communities have considered habitat and external environmental variables. This study examined environmental influences on the microbial community of Pachygrontha antennata, which is found in various habitats (e.g., urban, forested, and agricultural areas). The results demonstrated that the composition of the microbial community differed according to land use, while the bacterial diversity did not. In urban areas with high environmental heterogeneity, microbial community diversity tended to be high. Furthermore, bacteria in forests and agricultural areas (e.g., Paraburkholderia, Burkholderia) have been found to be highly correlated with habitat variables. Therefore, we suggest that habitat variables should be considered in future symbiotic studies.

Geosmin is a major concern in the management of water sources worldwide. Thus, we predicted concentration categories of geosmin at three different depths of lakes (i.e., surface, middle, and bottom), and analyzed relationships between geosmin concentration and factors such as phytoplankton abundance and environmental variables. Data were collected monthly from three major lakes (Uiam, Cheongpyeong, and Paldang lakes) in Korea from May 2014 to December 2015. Before predicting geosmin concentration, we categorized it into four groups based on the boxplot method, and multivariate adaptive regression splines, classification and regression trees, and random forest (RF) were applied to identify the most appropriate modelling to predict geosmin concentration. Overall, using environmental variables was more accurate than using phytoplankton abundance to predict the four categories of geosmin concentration based on AUC and accuracy in all three models as well as each layer. The RF model had the highest predictive power among the three SDMs. When predicting geosmin in the three water layers, the relative importance of environmental variables and phytoplankton abundance in the sensitivity analysis was different for each layer. Water temperature and abundance of Cyanophyceae were the most important factors for predicting geosmin concentration categories in the surface layer, whereas total abundance of phytoplankton exhibited relatively higher importance in the bottom layer.

This study presents a comprehensive dataset of soil temperature and respiration measurements from Mt. Namsan, South Korea, collected between 2009 and 2010, with estimated values for 2011 to 2020 and projections for 2021 to 2100 under Shared Socioeconomic Pathways (SSP) climate scenarios ranging from SSP1-2.6 to SSP5-8.5. Soil respiration (mg CO₂ m⁻² h⁻¹) and temperature (°C) were measured using automated sensors and the closed chamber method. Regression models were developed to estimate soil temperature for 2011-2020 and predict future trends based on SSP scenarios. The results demonstrate significant increases in soil respiration rates under SSP5-8.5, highlighting the temperature sensitivity of soil carbon fluxes. This dataset provides valuable insights for ecological modeling, long-term carbon budget analysis, and climate change adaptation studies in temperate forest ecosystems.

The distribution of organisms is governed by their habitat condition. We analyzed bacterial communities in the gut of the blackworm Lumbriculus variegatus by pyrosequencing of the extracted intestinal metagenomic DNA. Blackworms were collected from two sampling sites with differences in irradiance and riparian vegetation, where site GP7 was covered by riparian vegetation and site GP8 was exposed to sunlight. We obtained the filtered 6414 reads from three samples of each site. At GP7, 271 OTUs were identified, including 32 OTUs unique to the site, whereas at GP8, 238 OTUs were identified, including 22 unique OTUs. Among them, 18 OTUs were shared between both sites. The phylum Proteobacteria was a major component contributing 67.84% and 64.05% of sequences at sites GP7 and GP8, respectively, while each remaining phylum contributed less than 10% at both sites. The two sites differed in microbial community composition and KEGG-indicated biochemical pathways. Community indices such as species richness and Shannon diversity were higher at site GP7 than at GP8. Meanwhile, the abundance of Cyanobacteria was significantly higher at site GP8, while site GP7 showed a greater proportion of genes for membrane transport and carbohydrate metabolism, reflecting differences in food resources.