This research explores the complex interdependencies and dynamic interactions governing the adaptation of species co-evolving within the framework of Eco-Evo-Neuro-Devo, using the fascinating fig-wasp mutualism as a model system. Figs, which have many flowers blooming inside the fruit, are completely dependent on fig wasps as pollinators. The fig-wasp mutualism is a symbiotic relationship where fig trees provide a habitat and food source for specific wasp species, while the wasps, in turn, facilitate pollination and seed dispersal for the fig tree. The fig wasp is born and grows inside the fig fruit and becomes an adult. After mating, the female fig wasp is covered with pollen and enters another fruit, pollinates it and lays eggs there. In addition to the fig-wasp mutualism, additional interactions among hyperparasitic wasps, called non-pollinating fig wasp (NPFW), and flies, nematodes are frequently observed, shaping the co-evolution of the symbionts in the fig community. This study aims to identify the symbionts that make up the symbiotic system, their ecological interactions and genome evolution, and build a model to explore the mechanisms of symbiotic evolution at the molecular level. Sampling and identifying figs and fig-related insects through fieldwork in Korea, various techniques including GC-MS, in situ staining, electrophysiology, and diverse omics tools including WGS, RNA-seq will be implemented to investigate how chemical ecologies of the fig community shape the chemosensory systems of species. Additionally, we will build a cross-platform with Drosophila to narrow the distance between model species and non-model species, facilitating comparative studies across different model organism systems.

Our study examined a total of 60 dead wood samples (Quercus spp.), collecting 30 samples each in summer and winter, and measured various environmental variables in the field. These samples were classified into three decay classes: fresh, intermediate, well-decayed. We sampled saproxylic beetles within the collected dead wood using emergence traps. Conducted a cluster analysis to explore their relationships of saproxylic beetle and environmental variables. Our results revealed that season and decay class were significant environmental variables affecting saproxylic beetle communities. These results highlight the sensitivity of saproxylic beetles to fluctuations of environment and climate. In summary, our study highlights the intricate relationships between environmental variables and saproxylic beetles and suggests that different types of dead wood should be maintained in forests.

깡충거미과의 모습을 의태하여 시각적 포식자인 깡충거미로부터 자신을 보호하는 전략은 여러 나비목 곤충 에서 진화하였다. 하지만 나비목 곤충이 깡충거미 간의 의사소통 신호를 의태한다는 가설은 제안된 바 없다. 본 연구에서는 깡충거미과의 구애/영역성 행동을 의태하는 것으로 추정되는 붉은꼬마꼭지나방(Atkinsonia ignipicta)의 빗살 모양 더듬이와 추켜 올린 다리가 포식을 단념시키는 의태 신호로 작동하는지 실험적으로 검증 하였다. 깡충거미 의태라 추측되는 붉은꼬마꼭지나방의 형태적 및 행동적 특성들을 단계적으로 제외한 뒤 포식 자(흰눈썹깡충거미, Evarcha albaria)와의 조우 시 생존을 비교하였다. 연구 결과, 붉은꼬마꼭지나방은 비슷한 크기의 비-의태 나방에 비해 포식자의 공격을 덜 받았으며, 더듬이나 다리 중 하나라도 결여되었을 때 흰눈썹깡충 거미의 공격이 증가하였다. 이는 붉은꼬마꼭지나방의 더듬이와 다리 모두가 시각적 방어의 중요한 신호로 작동 한다는 것을 시사한다. 하지만 흰눈썹깡충거미가 붉은꼬마꼭지나방을 향해 구애/영역성 행동을 보이지 않았다 는 점에서 깡충거미의 낮은 공격 빈도가 같은 깡충거미 종류로 오인해서인지 다른 이유에서인지는 불투명하다. 그러나 사층깡충거미(Marpissa pulla)는 붉은꼬마꼭지나방을 상대로 영역성 행동을 잦은 빈도로 보였으며, 이는 붉은꼬마꼭지나방의 형태가 깡충거미 의태 효과를 가진다는 것을 보여준다.

Distractive marking, as conceptualized by Abbott H. Thayer, refers to diminutive patterns of contrasting colors on an animal’s body. Thayer hypothesized that these patterns augment camouflage by diverting predatory focus from the outline of the prey, however, the evidence was insufficient. In this study, we verified the hypothesis that the presence of distractive markings confers a survival advantage under specific conditions. Specifically, the experiment aimed to ascertain whether the existence of lichens on trees hinders the visual detection of prey, given that lichens resemble distractive markings. The experimental design involved human subjects as predators and artificial moth images on a monitor as prey. The survival of moths with and without distractive markings was compared, also considering the influence of the presence of lichens in the background. As an analysis result, the survival likelihood of moths was statistically significantly hindered when the distractive marking was present. This result contradicts Thayer‘s hypothesis and implies the presence of a function distinct other than the enhancement of camouflage.

Vespa mandarinia (Vespidae: Hymenoptera) is one of the two largest true hornets known to science. The species is a noted predator of social Hymenoptera and a significant pest of managed honey bees in its native range, but is also known to feed on a wide variety of other species when available. Most of the prey records for V. mandarinia are derived from visual observations in Japan, with sparse observations from other parts of its native range. A population of V. mandarinia was detected in North America in 2019 and five nests were removed between 2019 and 2021. We extracted DNA from larval meconia from four nests collected in Washington State, USA, and amplified the CO1 region to determine the potential prey base. We compared these with sequences generated from three nests in the Republic of Korea, and with prey pellets collected from foraging hornets at several locations in Korea. Results indicate that the prey base was much wider in the ROK than the USA, although social Hymenoptera were the most abundant and common prey items in both regions. Prey range seems to be bound by an intersection of organism size and local biodiversity, with little evidence to suggest that the latter is a limiting factor in colony success.

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.

Climate change has made outbreaks of insect-transmitted plant viruses increasingly unpredictable. Understanding spatio-temporal dynamics of insect vector migration can help forecast virus outbreaks, but the relationship is often poorly characterized. The incidence of Beet curly top virus (BCTV) was examined in 2,196 tomato fields in California from 2013-2022. In addition, we experimentally showed dispersal of the beet leafhopper, the only known vector of BCTV is negatively correlated with plant greenness, and we estimated spring migration timing using a vegetation greenness-based model. Potential environmental factors and spring migration time of beet leafhoppers were associated with BCTV incidence. We found BCTV incidence is strongly associated with spring migration timing rather than environmental factors themselves. In addition, the vegetation greenness-based model was able to accurately predict the severe BCTV outbreaks in 2013 and 2021 in California. The predictive model for spring migration time was implemented into a web-based mapping system, serving as a decision support tool for management purposes.

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.