Over four years (2020–2023), 305 traps were strategically placed across South Korea to collect data on Vespidae species. Our findings showed that Vespula flaviceps, Vespa crabro, and Vespula koreensis were the most frequently encountered species. Vespa velutina was also widespread, suggesting its successful integration into local ecosystems. The ARL analysis, using the “apriori” algorithm, identified significant co-occurrence patterns and potential interactions. The rules generated indicated both competitive and coexistent relationships with varying levels of association strength across different regions. Clustering analyses, including hierarchical and k-means clustering, grouped species based on their occurrence similarities. The distinct clusters formed in the analysis highlighted the unique ecological roles and interactions of V. velutina and other Vespidae species in South Korean ecosystems.

The biggest jewel beetle in Korea, Chrysochroa coreana, has been nominated as the Natural Monument No. 496 and also classified as Category I of Endangered Species by the Red Data Book. Due to the invisible feature of a saproxylic larval hood inside the host tree for years, the whole life history was hitherto been unknown to the academic world. In order to clarify the period of larval-hood and record images of the process of the final stage of emergence, we obtained sample eggs from two mated couples of adults that emerged from a dead tree of Celtis sinensis on Wando Island, which is well-known as the habitat of C. coreana. Larvae were hatched on four pieces of timber (Celtis aurantiaca) in July 2018 and kept in a growth chamber under the conditions of 25°C, 65% humidity, and in a 12-hour light/dark cycle. The development of larvae was monitored via the non-destructive C/T method every month. Six adults were emerged between February and March 2024. As a result, we obtained the fact that the larval period of C. coreana is minimum 66 months (5.5 years) under lab conditions.

Density survey should be carried out for applying integrated pest management strategies, but it is labor-intensive, time- and cost-consuming. Therefore, binomial sampling plans are developed for estimating and classifying the population density of whiteflies late larvae based on the relationship between the mean density per sample unit (7 leaflets) and the proportion of leaflets infested with less than T whiteflies ( ). In this study, models were examined using tally thresholds ranging from 1 to 5 late larvae per 7 leaflets. Regardless of tally thresholds, increasing the sample size had little effect on the precision of the binomial sampling plan. Based on the precision of the model, T=3 was the best tally threshold for estimating the densities of late larvae. Models developed using T=3 validated by Resampling Validation for Sampling Plan program. Above all, the binomial model with T=3 performed well in estimating the mean density of whiteflies in greenhouse tomato.

The Korea Forest Service has designated seven alpine tree species—Abies koreana, A. nephrolepis, Juniperus chinensis, Picea jezoensis, Pinus pumila, Taxus cuspidata, and Thuja koraiensis—as threatened with extinction in Korea. In 2023, we conducted a study on the seasonal occurrence of insect pests, focusing mainly on two coleopteran taxa (Cerambycidae and Scolytinae) and two hemipteran taxa (Aphrophoridae and Cicadellidae) in subalpine forests dominated by A. koreana, A. nephrolepis, Picea jezoensis, Thuja koraiensis, and Taxus cuspidata. We utilized three types of traps—Malaise trap, Lindgren funnel trap, and window trap—in eight investigation locations in Korea. In this presentation, we present the study results and discuss the effects of insect pests on alpine coniferous trees in Korea.

Ecosystems provide various ecosystem services based on biodiversity. However, biodiversity is facing crises due to anthropogenic factors such as pollution, land use change, and climate change. Threats to biodiversity can significantly impact the provision and stability of ecosystem services, extending beyond simple species decline. To address threats to biodiversity, it is crucial to evaluate how anthropogenic factors affect not only biodiversity but also ecosystem services. This study aims to investigate the energy flux in a post-mining area based on the biodiversity of soil ecosystems and assess its suitability as an evaluation metric. It was observed that as the concentration of the primary pollutant, arsenic, increased, both the biomass of soil organisms and energy flux decreased. Furthermore, soil ecosystem multifunctionality may be negatively affected by pollution. These findings contribute to understanding the impact of pollution on soil ecosystem biodiversity and energy flux in post-mining areas and provide important information for more effective conservation and management of ecosystem services.

특정작물의 연작재배가 만연한 국내 경작지 중, 특히 인삼재배지는 인삼뿌리썩음병균, 시설재배지는 선충에 의한 연작피해가 매우 심각하며, 주로 화학·생물학 약제로 방제하지만 효과가 낮고 토양오염과 약제저항성 등의 부작용을 유발하고 있음. 모든 살아 있는 병해충은 고온에 저항성이 없는 장점에 착안하여 마이크로파(915MHz) 전력밀도 균일화 응용으로 경작지 토양 30cm 이상 깊이까지 100℃ 이상 침투 가열하는 마이크로파 방제장치 및 방제기술을 개발하여 토양 속에 존재하는 선충, 개미, 인삼뿌리썩음병균에 적용한 결과, 선충은 60℃, 개미는 50℃에서 완전사멸 되었으며, 인삼뿌리썩음병균은 80℃에서 연작 가능한 수치까지 떨어지는 방제 효과를 나타 냄에 따라 농약을 대체하는 방제기술로 평가된다.

Recent advances in artificial intelligence and machine learning, such as the use of convolutional neural networks (CNNs) for image recognition, have emerged as a promising modality with the capability to visually differentiate between mosquito species. Here we present the first performance metrics of IDX, Vectech’s system for AI mosquito identification, as part of Maryland’s mosquito control program in the USA. Specimens were collected over fourteen weeks from twelve CDC gravid trap collection sites, identified morphologically by an entomologist, and imaged using the IDX system. By comparing entomologist identification to the algorithm output by IDX, we are able to calculate the accuracy of the system across species. Over the study period, 2,591 specimens were collected and imaged representing 14 species, 10 of which were available in the identification algorithm on the device during the study period. The micro average accuracy was 94.9%. Of these 10 species, 7 species consisted of less than 30 samples. The macro average accuracy when including these species was 79%, while the macro average when excluding these species was 93%. In the next iteration of this technology, Vectech is translating the vector identification capabilities of IDX into systems capable of processing greater numbers of specimens at large public health facilities, and remote sensing systems that will allow public health organizations to monitor vector abundance and diversity from the office. These advances demonstrate the utility of artificial intelligence in entomology and its potential to support vector surveillance and control programs around the world.

단백질의 구조 예측은 생명 과학 및 의약학 분야의 핵심적인 연구 주제 중 하나로, 단백질의 기능 및 상호작용을 이해하기 위한 주요 정보를 제공할 수 있어 다양한 연구가 수행되고 있다. 이러한 연구의 일환으로 최근 Google DeepMind의 AlphaFold2가 등장하였으며, 단백질 구조 예측 성능을 대폭 향상시켜 CASP(Critical Assessment of Protein Structure Prediction)에서 뛰어난 평가점수를 받아 단백질 구조 예측 분야의 최신 기술을 크게 향상시켰다. 이러한 컴퓨터 기반의 단백질의 구조 예측 방법은, 고전적인 방법을 사용하여 직접 단백질 구조를 결정하는 방법 에 비해 매우 정확하고 빠르며 경제적인 비용으로 수행될 수 있어 단백질 구조 예측 및 생리학 연구를 수행하는 연구자들에게 유용한 방법론이 될 것으로 사료된다. 따라서 본 연구소에서는 곤충을 포함한 무척추 자생동물을 연구하는 연구자들을 위해 단백질 구조 예측을 수행할 수 있도록 64Core/128Threads의 CPU, 256GB의 RAM과 6장의 GeForce RTX 3090으로 이루어진 GPU(Graphical Processing Unit) 고성능 컴퓨터 시스템에 AlphaFold2 program을 구축하였다. 최근 인간을 대상으로 한 단백질 구조 예측 연구는 상당한 진전을 보이고 있지만, 곤충을 포함한 자연계의 동물을 대상으로 한 연구는 여전히 미비한 상황이다. 이러한 자생동물자원연구의 확대를 위해 본 연구소에서 구축한 GPU 시스템 및 생물정보학적 분석 방법이 많이 활용되어야 하며, 이를 위해서는 연구자들 의 협력과 참여가 필요하다.

In the Republic of Korea, public health centers conduct vector mosquito control in accordance with infectious disease prevention laws. However, most public health centers have traditionally conducted periodic, uniform vector control across their respective regions without considering specific information on vector occurrence. In 2021, The Korea Diseases Control and Prevention Agency(KDCA) launched a control project to shift the paradigm toward mosquito control strategy based on mosquito surveillance data. In 2024, 18 local public health centers will participate in this project, which will progressively expanding so that it can be used countrywide. Local public health centers evaluate mosquito monitoring data using data gathered from Daily Mosquito Monitoring System(DMS), which enables them to pinpoint the best times and locations for vector control. Vector control activities carried out by local public health centers are computerized utilizing Vector Control Geographic Information System(VCGIS). Using the new control strategy with mosquito surveillance, the number of mosquito occurrences, number of control activities, and amount of insecticides have decreased compared to the periodic control activities. Based on mosquito surveillance data, it is anticipated that evidence-based mosquito vector control will offer a more efficient and effective means of mosquito control.

Due to climate change and the rise in international transportation, there is an emerging potential for outbreaks of mosquito-borne diseases such as malaria, dengue, and chikungunya. Consequently, the rapid detection of vector mosquito species, including those in the Aedes, Anopheles, and Culex genera, is crucial for effective vector control. Currently, mosquito population monitoring is manually conducted by experts, consuming significant time and labor, especially during peak seasons where it can take at least seven days. To address this challenge, we introduce an automated mosquito monitoring system designed for wild environments. Our method is threefold: It includes an imaging trap device for the automatic collection of mosquito data, the training of deep-learning models for mosquito identification, and an integrated management system to oversee multiple trap devices situated in various locations. Using the well-known Faster-RCNN detector with a ResNet50 backbone, we’ve achieved mAP (@IoU=0.50) of up to 81.63% in detecting Aedes albopictus, Anopheles spp., and Culex pipiens. As we continue our research, our goal is to gather more data from diverse regions. This not only aims to improve our model’s ability to detect different species but also to enhance environmental monitoring capabilities by incorporating gas sensors.