Current and Future Distribution of Ricania shantungensis (Hemiptera: Ricaniidae) in Korea: Application of Spatial Analysis to Select Relevant Environmental Variables for MaxEnt and CLIMEX Modeling

Abstract

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 ...

A Bioclimate-Based Maximum Entropy Model for Comperiella calauanica Barrion, Almarinez and Amalin (Hymenoptera: Encyrtidae) in the Philippines

“…In comparison, variables pertaining to temperature or its variations were found to have significant influence on the predicted distributions of insect pest species in temperate regions, namely Dacus spp. [21] and Lobesia botrana [22] in China, Ricania shantungensis in Korea [24], Hylobius abietis and Cameraria ohridella in Europe [19], and six species of tephritid fruit flies in Europe [20].…”

“…Particular species of insects have their own characteristic tolerance to climatic factors, and changes in such factors can lead to potential changes in distribution [17]. In view of this, Maxent modeling has been used to predict the current and potential distributions of invasive species [18], as well as those of a variety of forest and agricultural insect pests which include: the large pine weevil, Hylobius abietis L., and the horse-chestnut leaf miner, Cameraria ohridella Deschka and Dimič [19], and six tephritid fruit flies [20] in Europe; three species of tephritid flies under genus Dacus Fabricius [21] and the European grapevine moth, Lobesia botrana Denis and Schiffermüller, in China [22]; the ricaniid planthopper, Ricania shantungensis Chou and Lu in Korea [23,24]; the cotton mealybug, Phenacoccus solenopsis Tinsley, in India [25] and worldwide [26]; and the invasive European paper wasp, Polistes dominula Christ in the southern hemisphere [13].…”

Monitoring, Assessment and Management of Forest Insect Pests and Diseases

“…They reported that the pitfall traps with attractants of pine twigs and ethanol are useful for monitoring and controlling H. abietis. Baek et al [29] evaluated the current and future distribution of Ricania shantungensis (Chou and Lu), which is an invasive planthopper and an important pest in agriculture and forestry in Korea [30], using CLIMEX and the Maximum Entropy Model (MaxEnt). In the MaxEnt, the most important variables determining the distribution of R. shantungensis were the annual mean temperature, mean temperature of the coldest month, maximum temperature in the warmest month, and precipitation of the driest month.…”

“…List of papers included in the special issue. Pine twigs are better than artificial lures for trapping of pine weevils in pitfall traps Lalik et al [28] Insect Current and future distribution of Ricania shantungensis (Hemiptera: Ricaniidae) in Korea: Application of spatial analysis to select relevant environmental variables for MaxEnt and CLIMEX modelingBaek et al[29] Insect Occurrence prediction of the citrus Flatid planthopper (Metcalfa pruinosa (Say, 1830)) in South Korea using a random forest model Lee et al[31] …”

A Bioclimate-Based Maximum Entropy Model for ComperiellacalauanicaBarrion, Almarinez & Amalin (Hymenoptera: Encyrtidae), Parasitoid of Aspidiotusrigidus Reyne, in the Philippines

“…Models produced using MaxEnt can be easily understood and interpreted and provide valuable insights on distribution and habitat suitability for a species [9,10]. MaxEnt modeling has been used to predict the current and potential distributions of invasive species [11], as well as those of a variety of forest and agricultural insect pests which include: the large pine weevil, Hylobius abietis L., and the horse-chestnut leaf miner, Cameraria ohridella Deschka and Dimič [12], and six tephritid fruit flies [13] [16,17]; and the cotton mealybug, Phenacoccus solenopsis Tinsley, in India [18] and worldwide [19] *Not covered by surveys in the current study. [20].…”