본 연구는 미국흰불나방(Hyphantria cunea)의 국내 잠재 서식지 변화를 분석하기 위해 기후 변화와 토지 피복 변화라는 주요 환경 요인을 종합 적으로 고려하여 서식 적합도를 예측하였다. 먼저, 전 지구적 출현 데이터를 바탕으로 MaxEnt 모델을 구축하여 기후 변화 시나리오에 따른 국내 서식 적합도 변화를 모의하였다. 이후, 토지 피복 변화에 따른 산림 및 시가지 내 미국흰불나방의 PRA를 분석하였다. 연구 결과, 미국흰불나방의 적합 서식지는 태백산맥과 한라산 고산지대를 제외한 한국 대부분 지역에 분포할 것으로 예측되었다. SSP에 기반한 통합 기후-토지 피복 시나리오 에서 미국흰불나방의 PRA는 SSP1-2.6 시나리오에서는 2055s, 2085s가 각각 66,934 km2에서 67,363 km2로 증가한 반면, SSP3-7.0에서는 PRA는 66,676 km2에서 59,696 km2로 크게 감소하는 결과가 나타났다. 그러나 모든 시나리오에서 미국흰불나방 PRA 백분율이 여전히 전 국토 면적의 80%를 초과하기 때문에, 미국흰불나방에 대한 지속적인 방제 및 관리가 필요함을 시사한다. 본 연구는 국내에 광범위하게 퍼진 미국흰불나 방 개체군의 지속적인 관리가 필요함을 강조하며, 이를 토대로 미국흰불나방의 모니터링, 조기 경보, 예방 및 통제, 관리를 위한 기초 자료를 제공한 다. 또한, 기후 변화와 토지 이용 변화가 미국흰불나방의 서식 적합도에 미치는 영향을 종합적으로 분석함으로써, 효과적인 방제 및 관리 전략 수립 에 기여할 것으로 기대된다.

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.

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.