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Since the first report on its occurrence in 2010, Ricania shantungensis Chou & Lu in Korea has quickly spread. This pest population in agricultural areas has increased by over 100% each year and has caused serious economic damage in the last few years. This study was conducted to predict the potential habitat and the current and future distribution of R. shantungensis in Korea using CLIMEX and the Maximum Entropy Model (MaxEnt), and to suggest a new parameter selection method for both modeling programs. Weights of variables used in CLIMEX and those used in MaxEnt were determined using spatial association indices of spatial analysis by distance indices (SADIE). Weather data of Zhejiang province in China and those of all Korean territories were compared with Climate Matching in CLIMEX. MaxEnt was applied and evaluated with 295 data points on the presence and absence of R. shantungensis and eight environmental variables that were preselected by spatial and correlation tests. In MaxEnt, maximum temperature of the warmest month, annual mean temperature, mean temperature of the coldest month, and precipitation of the driest month were determined to be the most important variables affecting the distribution of R. shantungensis in Korea. The results of this study indicated that R. shantungensis had a higher probability of occurrence in western areas than in eastern areas of Korea, and showed great potential to spread eastward. These results are expected to be helpful for managing R. shantungensis in Korea and selecting relevant environmental variables for species distribution modeling. did not explain how the variables selected in the model affected its occurrence. Moreover, to date, the future distribution of R. shantungensis has not been predicted.MaxEnt estimates the distribution of a species by finding the distribution that is closest to the geographically uniform distribution, which is determined by the environmental characteristics of locations where it is present with given environmental variables [5]. The characteristics of MaxEnt are the use of data on presence and pseudo-absence, the determination of environmental and/or biological factors related to target species' distribution, and a correlative parameter selection process [5][6][7]. Its correlative parameter selection process has received major criticisms: ecologically unrelated variable selection with target species distribution [8], a relatively low prediction ability in spreading species [9], and highly biased results in unsampled areas [10].Due to the inherent issues with correlative methods for species distribution models (SDMs), deterministic methods have also been used for SDMs. Among the deterministic methods, CLIMEX is one of the most popular methods due to its good prediction of target species' distribution and relative ease of use [11][12][13]. CLIMEX estimates the potential distribution and risk of a species in relation to climate by using a deterministic method that requires pre-determined parameters regardless of target species distribution in ...

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The sweet potato whitefly, Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a major agricultural pest that causes economic damages worldwide. In particular, B . tabaci MED (Mediterranean) has resulted in serious economic losses in tomato production of Korea. In this study, 1,145 B . tabaci MED females from 35 tomato greenhouses in different geographic regions were collected from 2016 to 2018 (17 populations in 2016, 13 in 2017, and five in 2018) and analyzed to investigate their population genetic structures using eight microsatellite markers. The average number of alleles per population ( N A ) ranged from 2.000 to 5.875, the expected heterozygosity ( H E ) ranged from 0.218 to 0.600, the observed heterozygosity ( H O ) ranged from 0.061 to 0.580, and the fixation index inbreeding coefficient ( F IS ) ranged from -0.391 to 0.872 over the three years of the study. Some significant correlation ( p < 0.05) was present between genetic differentiations ( F ST ) and geographical distance, and a comparatively high proportion of variation was found among the B . tabaci MED populations. The B . tabaci MED populations were divided into two well-differentiated genetic clusters within different geographic regions. Interestingly, its genetic structures converged into one genetic cluster during just one year. The reasons for this genetic change were speculated to arise from different fitness, insecticide resistance, and insect movement by human activities.