In this research, the marine medaka Oryzias javanicus underwent a 96 h exposure to two concentrations of the red tide dinoflagellate Karenia mikimotoi (1,000 and 5,000 cells mL-1), and the temporal variations in biochemical responses related to antioxidant and immunity parameters were assessed in the liver tissue. The study revealed a significant increase in ichthyotoxicity with elevated cell concentrations of K. mikimotoi, especially evident at 96 h in marine medaka exposed to 5,000 cells mL-1. At 1,000 cells mL-1 of K. mikimotoi, the opercular respiratory rate showed a significant increase, whereas exposure to 5,000 cells mL-1 resulted in a lowered rate. The intracellular malondialdehyde content was significantly elevated in response to both cell concentrations at 96 h. Regarding glutathione content, levels were significantly increased by exposure to both cell concentrations. Catalase and superoxide dismutase enzymatic activities experienced an increase at 1,000 cells mL-1 of K. mikimotoi, while their activities were reduced at 5,000 cells mL-1 at 96 h. The analysis of two immunity parameters, alternative complement pathway and lysozyme, demonstrated significantly reduced activities in the liver tissue exposed to 5,000 cells mL-1 of K. mikimotoi. These findings aim to enhance the understanding of K. mikimotoi toxicity in marine fish by offering insights into biochemical responses associated with harmful algal blooms.

Throughfall (TF)—as a diffusive hydrological water flux—significantly affects ecohydrological and biogeochemical processes within forest ecosystems. Recent investigations have revealed the impact on TF generation processes within unmanaged coniferous plantations of under-canopy structures, particularly those laden with dead branches, as well as upper-canopy structures. However, spatiotemporal variations in TF in such plantations remain unexplored. We investigated these variations in TF in a 33-year-old unmanaged Japanese cypress (Chamaecyparis obtusa Endl.) plantation, laden with dead branches, with a high stand density (SD) of 2,500 stems ha−1. Over a two-year period (May 2017 to May 2019), we conducted weekly TF measurements using 28 manual-type TF collectors. We compared the present TF ratio and canopy water storage capacity (S) with those of previous investigations conducted on Japanese cypress plantations. Moreover, we assessed key indices contributing to spatiotemporal TF variations (canopy cover: CC and distance to the nearest stem: TFd) and potentially influential dead branch indices (number of dead branches: TFdb and vertical spacing length on a stem: TFs) to elucidate TF spatial patterns. The results showed that the TF ratio was notably lower than that in previous studies (n = 13), with SD (r = –0.92, p < 0.001) and S (r = –0.87, p < 0.001) emerging as key influential factors among other stand-structure parameters. Spatial TF patterns exhibited a decreasing trend as the gross rainfall (GR) increased. Temporal stability was not significantly associated with CC (r = 0.120, p = 0.544), TFd (r = 0.068, p = 0.731), TFdb (r = 0.211, p = 0.281), or TFs (r = 0.206, p = 0.292) for any of the TF collectors. These findings underscore the important role of GR in determining the spatial variation of TF. Collectively, our results contribute to an enhanced understanding of TF spatiotemporal heterogeneity in unmanaged Japanese cypress plantations with dead branches.

The effect of the surrounding vegetation on the seed germination and growth of mountain-cultivated ginseng (MCG, Panax ginseng C.A. Meyer) was investigated. Seed germination rate and growth were tested for allelopathy effects on four forest tree species after treatment with fallen leaves and leaf extracts. In the case of soil treatment through fallen leaves and crushed leaves, the germination rate was lower in the Quercus myrsinifolia treatments, and the average germination time was slower when Chamaecyparis obtusa was treated. In the case of Pinus densiflora and Quercus variabilis, which are used in most of the MCG cultivation areas, they did not have a significant effect on seed germination. In the fallen leaves treatments, the stem showed a tendency to lengthen. The hot water extract treatment showed a higher germination percentage than the cold water extract treatment. The extract treatment showed a deficient germination percentage of some MCG seeds. However, in the case of the treatments except for this, the germination percentage was similar to that of the control treatment. However, the Mean Germination Time, germination rate, and germination value were faster and higher than the control treatment. As a result of calculating the allelopathic index (AI) of MCG according to the extract treatment of 4 species, most had a negative effect on germination, and P. densiflora and Q. variabilis extracts showed the most significant effect. The ginsenoside content was higher in the fallen leaves treatment than in the control. The above results will help select and manage MCG plantations.

The ectoparasitic mite, Varroa destructor is one of the most destructive pests of the honeybee (Apis mellifera) leading to the collapse honey bee colony in many regions of the world. RNA interference (RNAi) is a novel approach recently proposed for insect pest control. However, the efficiency of RNAi in insects is low due to the lack of effective delivery methods for dsRNA and sensitivity to nuclease degradation. Therefore, the success of RNAi technology largely depends on the stability of dsRNA. To explore the possibility of using RNAi to control varroa mite, we determined the effects of dsRNA targeting a subunit of the cytoplasmic coatomer protein complex B2, D, and E subunits on target gene expression for varroa mite. We observe that dsRNA ingested by bees is transferred to the varroa mite, resulting in knockdown of COPB2 expression. Furthermore, we demonstrate that chitosan nanoparticles-dsRNA complexes were more stable for 7 days in honeybee tissue fluids. The dsRNA-conjugated with chitosan was protected from degradation in hemolymph, fat body, and midgut extracts collected from the honeybee. These results possibly suggest that nanoparticles-dsRNA complexes might be horizontally transferred from treated honeybee to varroa mite, in which case the honeybees could serve as RNAi vectors. We confirmed, moreover, dsRNA fed nontarget insects, honeybee, were unaffected, and no toxicity was observed for honeybee. Overall, these data suggest that dsRNA-conjugated with chitosan help escape effectively from degradation by honeybee tissue fluids and could improve RNAi efficiency in varroa mite.

This study was conducted to calculate the damage of Italian ryegrass (IRG) by abnormal climate using machine learning and present the damage through the map. The IRG data collected 1,384. The climate data was collected from the Korea Meteorological Administration Meteorological data open portal.The machine learning model called xDeepFM was used to detect IRG damage. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The calculation of damage was the difference between the Dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of IRG data (1986~2020). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization (WMO) standard. The DMYnormal was ranged from 5,678 to 15,188 kg/ha. The damage of IRG differed according to region and level of abnormal climate with abnormal temperature, precipitation, and wind speed from -1,380 to 1,176, -3 to 2,465, and -830 to 962 kg/ha, respectively. The maximum damage was 1,176 kg/ha when the abnormal temperature was -2 level (+1.04℃), 2,465 kg/ha when the abnormal precipitation was all level and 962 kg/ha when the abnormal wind speed was -2 level (+1.60 ㎧). The damage calculated through the WMO method was presented as an map using QGIS. There was some blank area because there was no climate data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).