The increasing global consumption of seafood has led to increased trade among nations, accompanied by mislabeling and fraudulent practices that have rendered authentication crucial. The multi-isotope ratio analysis is considered as applicable tool for evaluating geographical authentications but requires information and experience to select target elements such as isotopes, through a distinction method based on differences in habitat and physiology due to origin. The present study examined recombination conditions of multi-elements that facilitated geographically distinct classifications of the clams to sort out appropriate elements. Briefly, linear discriminant analysis (LDA) analysis was performed according to several combinations of five stable isotopes (carbon (δ13C), nitrogen (δ15N), oxygen (δ18O), hydrogen (δD), and sulfur (δ34S)) and two radiogenic elements (strontium (87Sr/86Sr) and neodymium (143Nd/144Nd)), and the geographical classification results of the Manila clam Ruditapes philippinarum from Democratic People’s Republic of Korea (DPR Korea), Korea and China were compared. In conclusion, linear discriminant analysis (LDA) with at least four elements (C, N, O, and S) including S revealed a remarkable cluster distribution of the clams. These findings expanded the application of systematic multi-elements analyses, including stable and radiogenic isotopes, to trace the origins of R. philippinarum collected from the Korea, China, and DPR Korea.

-The diatoms are an ecologically important group of algae that have been extensively studied by ecologists and taxonomists. However, the large-scale patterns of diatom distribution and the factors underlying this distribution are largely unknown. The aims of this study were to identify the large-scale spatial patterns of benthic diatom assemblages in Korean streams and rivers, and to assess the importance of numerous environmental factors on diatom distribution. We classified 720 study sites based on diatom flora. Benthic diatoms, water chemistry, altitude, and riparian land cover and use were characterized by multivariate analyses, Monte Carlo permutation tests, and indicator species analysis. In total, we identified 531 diatom taxa. Diatom assemblages were mostly dominated by species of the genera Achnanthes, Navicula, Nitzschia, Cocconeis, Fragilaria (Synedra included), Cymbella, Gomphonema, and Melosira. Cluster analysis partitioned all 720 sites into eight groups based on diatom species composition. Canonical correspondence analysis indicated that altitude, land cover and use, current velocity, electrical conductivity, and nutrient levels explained a significant amount of the variation in the composition of assemblages of benthic diatoms. At the national scale, a downstream ecological gradient was apparent, from fast-flowing, mostly oligotrophic highland streams to slow-flowing, mostly eutrophic lowland rivers. Our data suggest that spatial factors explain some of the variation in diatom distribution. The present investigation of the spatial patterns of benthic diatoms, the ecological determinants of diatom occurrence, and the identification of diatom indicator species contributes to development of a program for assessing the biological integrity of lotic ecosystems in Korea.

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Forest fire modeling often requires estimates of fuel moisture status. Among the various fuel variables used for fire modeling studies, the 10-h fuel moisture content (10-h FMC) is a promising predictor since it can be automatically measured in real time at study sites, yielding more information for fire models. Here, the performance of 10-h FMC models based on three different approaches, including regression (MREG), machine learning algorithms (MML) with random forest and support vector machine, and a process-based model (MFSMM), were compared. In addition, whole-year models of each type were compared with their respective seasonal models to explore whether the development of separate seasonal models yielded better estimates. Meteorological conditions and 10-h FMC were measured each minute for 18 months in and near a forest site and used for constructing and examining the 10-h FMC models. In the assessments, MML showed the best performance (R2 = 0.77–0.82 and root mean squared error [RMSE] = 2.05–2.84%). The introduction of the correction coefficient into MREG improved its estimates (R2 improved from 0.56–0.58 to 0.68–0.70 and RMSE improved from 3.13–3.85% to 2.64–3.27%) by reducing the errors associated with high 10-h FMC values. MFSMM showed the worst performance (R2 = 0.41–0.43 and RMSE = 3.70–4.39%), which could possibly be attributed to the lack of radiation input from the study sites as well as the particular fuel moisture stick sensor that was used. Whole-year models and seasonal models showed almost equal performance because 10-h FMC varied in response to atmospheric moisture conditions rather than specific seasonal patterns. The adoption of a hybrid modeling approach that blends machine-learning and process-based approaches may yield better predictability and interpretability. This study provides additional evidence of the lagged response of 10-h FMC after rainfall, and suggests a new way of accounting for this response in a regression model. Our approach using comparisons among models can be utilized for other fire modeling studies, including those involving fire danger ratings.

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