Environmental changes play a significant role in the introduction, dispersal, and establishment of invasive species. This study aims to predict the habitat suitability of the newly invasive pest P. absoluta in South Korea by thoroughly considering key environmental factors, including climate and land cover changes. First, the MaxEnt model was developed to simulate changes in habitat suitability using global occurrence data and future climate change scenarios. Subsequently, potential risk areas (PRAs) for P. absoluta within agricultural regions were analyzed based on land cover changes. The results indicated that under all Shared Socioeconomic Pathway (SSP) scenario combinations, the PRA for SSP1 and SSP3 in 2055 were similar, with values of 47.85% and 48.62%, respectively. However, by 2085, these areas showed a marked decrease to 39.28% and 28.52%, respectively. These findings suggest that the PRA for P. absoluta is expected to be most critical in the near future as climate and land-use changes continue to progress. This study emphasizes the urgent need for ongoing monitoring and management to prevent further invasion and spread of P. absoluta into new regions of South Korea. Additionally, it provides scientific evidence to support the development of effective control and management strategies. By thoroughly evaluating the impact of climate and land cover changes on invasive species management, this research presents a foundational framework for predicting the spread and risks of P. absoluta under future climate scenarios.

고추온실에서 꽃노랑총채벌레(Frankliniella occidentalis)의 이항표본추출법을 개발하였다. 무작위로 선택된 고추를 상, 중 하단으로 나누어, 각 구간에서 세 개의 꽃에 있는 꽃노랑총채벌레를 70% 에탄올이 든 바이알에 털어서 채취한뒤 실체현미경 하에서 밀도를 확인하였다. 꽃노랑총채 벌레의 평균밀도와 3가지 Tally threshold (1, 3, 5) 밀도 이상인 감염비율(PT)간의 이항관계 모형을 평가하여 개발하고 검증하였다. 꽃노랑총채벌 레의 이항표본조사를 위한 최적의 Tally threshold는 3으로 나타났다. 개발된 이항표본추출법의 검증은 이항표본추출법 개발에 사용되지 않은 독 립적 자료를 사용해서 RVSP (Resampling Validation for Sampling Plan) 프로그램으로 진행했으며, 고정 표본 크기(FSS)와 Wald의 순차적 확률비 검정(SPRT)을 사용했다. FSS는 Tally threshold를 3으로, SPRT는 상한값 0.55와 하한값 0.32로 설정해 1000번의 시뮬레이션을 수행했 다. 시뮬레이션 결과 실제 평균과 예측 평균 간의 차이가 없었으므로 개발된 이항표본조사법이 효과적인 것으로 나타났다.

In agricultural ecosystems, the relationship between insect pests and hosts is important, as insect pests can invade hosts, increasing insect pest density that threatens the hosts’ health. Insect pests and hosts are negatively correlated and affect the environment around them. i.e., host health, environment, and insect pest density are causally related, and the environment affects insect pest density. Deep learning is method of machine learning based on neural network theory. This approach enables handling uncertain environmental factors that simultaneously impact the density of F. occidentalis. Environmental factors affecting the density fluctuation of F. occidentalis selected atmosphere factors, soil factors, and host factors. This study aims to F. occidentalis monitoring using deep learning models inputting environmental factors.

As climate changes and global trade volume increases, the spread of invasive alien species accelerates. Early prevention before occurrence is crucial for invasive pest control. Therefore, this study modeled the current and future potential distribution of the tomato leafminer(Tuta absoluta) (Meyrick) (Lepidoptera: Gelechiidae), the most significant pest affecting tomatoes, in Korea. This pest primarily feeds on Solanaceae crops and can cause extensive damage, resulting in 50-100% loss of crops in greenhouses or fields. While previously unreported in Korea, it invaded China in 2017, indicating a potential threat to Korea. The potential distribution of the tomato leafminer in Korea under current and three future climate scenarios (SSP1-26, SSP3-70, SSP5-85) was predicted using the MaxEnt model. Additionally, elevation and land cover were incorporated as abiotic factors considering the ecological characteristics of the pest.

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.

담배가루이(Bemisia tabaci)는 광식성 해충으로 토마토황화잎말림바이러스(Tomato Yellow Leaf Curl Virus; TYLCV), 카사바갈색줄무늬병 (Cassava Brown Streak Disease; CBSD)를 매개하는 해충이다. 담배가루이 방제를 위해 화학적 방제가 주로 시행되지만 저항성으로 인한 한계로 인 해 종합적해충방제를 위한 고정정확도를 이용한 표본조사법(Fixed precision sampling plan)을 개발하였다. 표본추출은 토마토 식물이 50 cm 높이의 레일 위에 위치한 배지를 이용해 재배되고 있어 배지로부터 130 cm 이상(지상에서 180 cm 이상)을 상단, 70 cm~100 cm (지상에서 120 cm~150 cm)를 중단, 50 cm 이하(지상에서 100 cm 이하)를 하단으로 나누어 각 위치별 토마토 7엽의 잎 뒷면에서 관찰된 담배가루이 노숙 유충 마리 수를 조사 하였다. 담배가루이 노숙유충은 이동성이 거의 없어 알에서 우화한 뒤 고착화하여 용과 성충 단계를 거치기 때문에 중단, 하단에 밀도가 높았다. 공간분 포분석은 Taylor’s power law (TPL)를 이용하여 도출된 TPL의 회귀계수를 통해 분석하였고, TPL 계수의 차이는 공분산분석(ANCOVA)하여 차이 가 없어 자료를 통합(pooling)하여 계산된 새로운 TPL 계수를 이용하여 표본추출정지선과 방제의사결정법을 개발하였다. 개발된 표본추출법의 적합 성을 판단하기 위해 분석에 사용하지 않은 독립된 자료를 이용하여 Resampling Validation for Sampling Plan (RVSP) 프로그램으로 평가하였다.

A causality exists between insect density and plant health, where plant health is affected by both the plant’s potential and environmental factors. In other words, causality is possible between insect density and environmental factors, allowing for the analysis of insect density based on these environmental factors. Machine learning enables studying insect density alongside environmental factors, providing insights into the causality between insects, the environment, and plant health. Machine learning is a methodology that involves the design of models by learning patterns from input data. This study aims to predict F. occidentalis density by sampling environmental factors and applying them to machine learning models.

For effective control of Frankliniella occidentalis, one of polyphagous pests with resistance to insecticides, necessitates the implementation of an integrated pest management strategy. Therefore, estimation of pest density is essential and this is achieved through the application of spatial statistical analysis methods. Because traditional methods often overlook the correlation between sampling locations and data, geostatistical analysis using variogram and kriging is introduced. Variogram provides information on the independent distance between data points. Kriging is a spatial interpolation technique for estimating the values at unsampled locations. For assessing model fitness, cross-validation is used by comparing predicted values with actual observations. This study focuses on the application of geostatistical techniques to estimate F. occidentalis density in hot pepper greenhouse, thereby contributing to making decision.

Since the importance of integrated pest management to minimize environmental damage and maximize pest control effectiveness has emerged, efforts to put it into practice have continued. To implement IPM, it is necessary to estimate the economic injury level to determine the control method by identifying pests and weeds that damage the quantity and quality of crops in the field, investigating the occurrence level, and calculating the ratio of cost and effectiveness. Also, damage to host plants caused by increased density of insect pests appears to change plant’s health that key factor for managing crops. Therefore, understanding the relationship between the density of pests and the damage to the host plants is necessary. This study aims to analyze the causal relationship between the density of insect pests and damage to the host plants for estimating the economic injury level of insect pests on the host plants and investigating the possibility of pest control decision-making using plant health status.

Climate change and biological invasions are the greatest threats to biodiversity, agriculture, health and the global economy. Tomato leafminer(Tuta absoluta) (Meyrick) (Lepidoptera: Gelechiidae) is one of the most important threats to agriculture worldwide. This pest is characterized by rapid reproduction, strong dispersal ability, and highly overlapping of generations. Plants are damaged by direct feeding on leaves, stems, buds, calyces, young ripe fruits and by the invasion of secondary pathogens which enter through the wounds made by the pest. Since it invaded Spain in 2006, it has spread to Europe, the Mediterranean region, and, in 2010, to some countries in Central Asia and Southeast Asia. In East Asia, Tomato leafminer was first detected in China in Yili, Xinjiang Uygur Autonomous Region, in 2017. There is a possibility that this pest will invade South Korea as well. This study provides this by the use of MaxEnt algorithm for modelling the potential geographical distribution of Tomato Leafminer in South Korea Using presence-only data.

Among migratory insect pests, Mythimna seperata and Cnaphalocrocis medinalis are invasive pests introduced into South Korea through westerlies from southern China. M. seperata and C. medinalis are insect pests that use rice as a host. They injure rice leaves and inhibit rice growth. To understand the distribution of M. seperata and C. medinalis, it is important to understand environmental factors such as temperature and humidity of their habitat. This study predicted current and future habitat suitability models for understanding the distribution of M. seperata and C. medinalis. Occurrence data, SSPs (Shared Socio-economic Pathways) scenario, and RCP (Representative Concentration Pathway) were applied to MaxEnt (Maximum Entropy), a machine learning model among SDM (Species Distribution Model). As a result, M. seperata and C. medinalis are aggregated on the west and south coasts where they have a host after migration from China. As a result of MaxEnt analysis, the contribution was high in the order of Land-cover data and DEM (Digital Elevation Model). In bioclimatic variables, BIO_4 (Temperature seasonality) was high in M. seperata and BIO_2 (Mean Diurnal Range) was found in C. medinalis. The habitat suitability model predicted that M. seperata and C. medinalis could inhabit most rice paddies.

Frankliniella occidentalis is an invasive pest insect, which affects over 500 different species of host plants and transmits viruses (tomato spotted wilt virus; TSWV). Despite their efficiency in controling insect pests, pesticides are limited by residence, cost and environmental burden. Therefore, a fixed-precision level sampling plan was developed. The sampling method for F. occidentalis adults in pepper greenhouses consists of spatial distribution analysis, sampling stop line, and control decision making. For sampling, the plant was divided into the upper part (180 cm above ground), middle part (120-160 cm above ground), and lower part (70-110 cm above ground). Through ANCOVA, the P values of intercept and slope were estimated to be 0.94 and 0.87, respectively, which meant there were no significant differences between values of all the levels of the pepper plant. In spatial distribution analysis, the coefficients were derived from Taylor’s power law (TPL) at pooling data of each level in the plant, based on the 3-flowers sampling unit. F. occidentalis adults showed aggregated distribution in greenhouse peppers. TPL coefficients were used to develop a fixed-precision sampling stop line. For control decision making, the pre-referred action thresholds were set at 3 and 18. With two action thresholds, Nmax values were calculated at 97 and 1149, respectively. Using the Resampling Validation for Sampling Program (RVSP) and the results gained from the greenhouses, the simulated validation of our sampling method showed a reasonable level of precision.

형질전환 벼와 일반 벼의 환경위해성 평가 기초자료를 위해 경상남도 사천시에서 2013~2017년, 경상북도 군위군에서 2015~2016년 7월 말부터 10월 초까지 벼에 발생하는 곤충상을 조사하고 군집특성을 분석하여 비교하였다. 사천시에서 형질전환 벼는 경상대학교 LMO (Living genetically Modified Organism) 격리포장 내에서 재배되었고, 일반 벼는 경상남도 사천시 사천읍 두량리에서 재배되었다. 군위군에서 형질전환 벼는 경북대학교 LMO 격리포장 내에서 재배되었고, 일반 벼는 경상북도 군위군 효령면 화계리에서 재배되었다. 채집방법으로 육안 조사, 포충망 조사, 유아등 트랩, 끈끈이 트랩을 이용하였다. 5년 동안 사천시에서 총 15목 123과 464종 37,941개체가 채집되었고, 2년 동안 군위군에서 총 13목 111과 366종 10,030개체가 채집되었다. 다년간 LMO포장과 일반포장을 비교해본 결과 매년 상위 주요 목은 우점 순서를 제외하면 거의 같은 목이 나타나는 것을 보아 아직까지는 LMO포장과 일반포장의 차이가 뚜렷하게 나타나지 않았다고 사료된다. 그리고 LMO포장과 일반포장 사이의 유사도 지수와 일반포장 사이의 유사도 지수가 차이가 없는 것으로 보아 LMO로 인한 차이가 아닌 환경에 의한 차이로 사료된다.

비래해충 개체군은 중국 남쪽에서 제트기류를 타고 한국으로 유입되는 해충이다. 애멸구(Laodelphax striatellus), 벼멸구(Nilaparvata lugens), 흰등멸구(Sogatella furcifera), 멸강나방(Mythimna separata), 혹명나방(Cnaphalocrocis medinalis)은 주요 비래해충 5종으로 주요 작물인 벼에 피해를 주기에 중요하다. 이 연구는 2016년 7월 하순에서 9월 상순, 2017년 7월에서 8월까지 전라도의 벼논에서 하였다. 멸강나방과 혹명나방은 페로몬 트랩을 사용하여 채집하였고, 벼멸구, 흰등멸구, 애멸구는 육안 조사, 포충망 조사, 끈끈이 트랩을 사용하여 채집하였다. 비래해충을 분석 하기 위해 공간통계학 중 SADIE (Spatial Analysis by Distance IndicEs)를 사용하였다. SADIE는 사용하여 공간분포 및 집중지수 Ia를 분석 하였고, 클러스터지수 Vi, Vj를 사용하여 공간분포를 조사하였다. 또한, 클러스터지수는 red-blue plot를 사용하여 지도 위에 나타내었다. 멸강나 방과 혹명나방은 SADIE 공간 집중 분석, red-blue plot 분석에서 다른 분포를 보였다. 애멸구와 다른 멸구의 초기 공간분포는 표본 추출 위치와 시간이 다르게 나타났다.