Isolation of reference DNA templates for quantitative real-time PCR assays is an expensive, labor-intensive and time-consuming process if they are not readily available. Two artificial DNA templates with multiple probe sites were designed for quantifying methanogens and their 10 subgroups, based on the methyl coenzyme M reductase gene (mcrA). Their standards were comparable to each other. PCR amplification efficiencies (cycle vs. cumulative fluorescence) of the artificial DNAs were also comparable to those of the observed methanogen groups from anaerobic digesters. The artificial templates can be alternatives to the actual references.

This study examined the structural and ecological drivers of burn severity during the March 2025 wildfire in Uiseong County, Republic of Korea, with a focus on developing a predictive framework using the differenced Normalized Burn Ratio (dNBR). Seventeen candidate variables were evaluated, among which the forest type, stand age, tree height, diameter at breast height (DBH), and Normalized Difference Vegetation Index (NDVI) were consistently identified as the most influential predictors. Burn severity increased across all forest types up to the 4th–5th age classes before declining in older stands. Coniferous forests exhibited the highest severity at the 5th age class (mean dNBR = 0.3069), followed by mixed forests (0.2771) and broadleaf forests (0.2194). Structural factors reinforced this pattern, as coniferous and mixed forests recorded maximum severity within the 5–11 m height range, while broadleaf forests showed relatively stable severity across 3–21 m but declined thereafter. In the final prediction model, NDVI emerged as the dominant variable, integrating canopy density, vegetation vigor, and moisture conditions. Notably, NDVI exhibited a positive correlation with burn severity in coniferous stands during this early-spring event, diverging from the generally negative relationship reported in previous studies. This seasonal anomaly underscores the need to interpret NDVI flexibly in relation to the forest type, stand age, and phenological stage. Overall, the model results demonstrate that mid-aged stands with moderate heights and dense canopy cover are the most fire-prone, whereas older, taller stands show reduced susceptibility. By integrating NDVI with structural attributes, this modeling approach provides a scalable tool for the spatial prediction of wildfire severity and supports resilience-based forest management under climate change.

Monitoring and preserving natural habitats has become an essential activity in many countries today. As a native tree species in Korea, Paulownia coreana has periodically been surveyed in national ecological surveys and was identified as an important target for conservation as well as habitat monitoring and management. This study explores habitat suitability models (HSMs) for Paulownia coreana in conjunction with national ecological survey data and various environmental factors. Together with environmental variables, the national ecological survey data were run through machine learning algorithms such as Artificial Neural Network and Decision Tree & Rules, which were used to identify the impact of individual variables and create HSMs for Paulownia coreana, respectively. Unlike other studies, which used remote sensing data to create HSMs, this study employed periodical on-site survey data for enhanced validity. Moreover, localized environmental resources such as topography, soil, and rainfall were taken into account to project habitat suitability. Among the environment variables used, the study identified critical attributes that affect the habitat conditions of Paulownia coreana. Therefore, the habitat suitability modelling methods employed in this study could play key roles in planning, monitoring, and managing plants species in regional and national levels. Furthermore, it could shed light on existing challenges and future research needs.

Understanding long-term changes in mammal communities is essential for evaluating ecosystem dynamics under large-scale environmental transformation. This study analyzed nationwide data from the 3rd (2006–2013), 4th (2014–2018), and 5th (2019–2023) National Ecosystem Surveys to assess temporal shifts in the abundance and dominance structure of medium- and large-sized mammals across the Korean Peninsula. Using standardized survey data, we compared changes in detection frequency, dominance rank, and diversity indices among survey phases. Results revealed a significant restructuring of dominance from the 3rd to 4th survey, followed by stabilization in the 5th phase, indicating a gradual simplification of community structure. The water deer remained the most dominant species throughout all surveys, while the wild boar showed a consistent increase in relative frequency. In contrast, smaller or specialist species such as the Korean hare exhibited notable declines. These patterns correspond to ecological changes driven by forest recovery and the absence of apex predators, which have facilitated the proliferation of adaptable generalist species. The findings suggest that ongoing habitat recovery, though beneficial for biomass and productivity, may simultaneously reduce functional diversity and community balance. This study highlights the value of long-term, standardized monitoring for identifying structural transitions in wildlife communities and understanding their broader implications for biodiversity conservation on the Korean Peninsula.

Citizen participation data has different characteristics from expert. Understanding the unique attributes of citizen participation data and considering the disparities between such data and that collected by experts is of paramount importance to harness citizen-generated data effectively. In this regard, location information was presented for five taxa of plants, insects, birds, amphibians, and mammals nationwide surveyed by citizens in 2022, excluding endangered species. Additionally, species information and administrative district information are included. It is anticipated that the integration of citizen participation survey results with expert research findings will enhance the efficacy of natural ecological monitoring. By directly collecting ecological data, which becomes the basic data for natural environment policy, opportunities for public participation in natural environment policy will be expanded, and through this, it will be possible to raise awareness of natural environment conservation.