Predicting potential current distribution of Lycorma delicatula (Hemiptera: Fulgoridae) using MaxEnt model in South Korea

Abstract

Lycorma delicatula (Hemiptera: Fulgoridae) is invasive insect in Korea which causes plant damages by sucking and sooty molds. Lycorma delicatula was first detected in South Korea in 2004, where its introduction and spreading possibly were affected by human activityrelated factors. Here, we used MaxEnt to describe current distribution of L. delicatula in Korea and tried to find out the impact of human influences for distribution. We used 143 sites of occurrence data, 19 bioclimatic variables, duration of temperature below -11°C, average daily minimum temperature in January, cumulative thermal unit variable, the distribution of grape orchard variable and human footprint to create models. These models were estimated by two sets of 24 candidates with feature combinations and regularization multipliers. In addition, these two sets were created as models with and without footprint for how human influence affect to distribution. Model selection for optimal model was performed by selecting a model with a lowest sum of each rank in small sample-size corrected Akaike's information criterion and difference between training and test AUC. Model of LQ10 parameter combinations was selected as optimal models for both model sets. Consequently, both of distribution maps from these models showed similar patterns of presence probability for L. delicatula. Both models expected that low altitude regions were relatively more suitable than mountain areas in Korea.

Tracing the Origin of Korean Invasive Populations of the Spotted Lanternfly, Lycorma delicatula (Hemiptera: Fulgoridae)

“…As with the recent prediction model study [18], the spatial distribution and spread of SLF in Korea are closely related to anthropogenic factors (i.e., human footprint). The prevalence of the initial population seen in the results of the MAXENT analyses [18] was relatively high by the human footprint including transporting activity although it was not a naturally environmental variable. Because of these artificial factors, the rapid spread and spatial distribution of SLF would have been possible despite a short period of invasion, so it is supported that IBD correlation was not significant in Korea in our results.…”

“…As the ecological feature of SLF is overwintering through egg masses on the surface of host plants or even artifacts, the artificial spread is possible through vehicle transport, as in the case of the spread of boll weevils in the United States [55]. As with the recent prediction model study [18], the spatial distribution and spread of SLF in Korea are closely related to anthropogenic factors (i.e., human footprint). The prevalence of the initial population seen in the results of the MAXENT analyses [18] was relatively high by the human footprint including transporting activity although it was not a naturally environmental variable.…”

“…Until 2009, this species was found mostly in Seoul and metropolitan areas [15,16]. However, this species is currently distributed everywhere in mainland Korea [18]. Subsequently, SLF is causing great damages to agriculture as well as wild forests [10,19].…”

“…In particular, continuing outbreaks brought large damages to cultivated grapes. In the initial five years of the invasion, SLF damaged about one ha of vineyards, but in 2010, the damage increased to 8478 ha [18]. The successful settlement of this species is thought to be due to the success of overwintering and is considered to be related to the recent increase in winter temperatures in Korea [17].…”

Modelling Potential Distribution of Snow Leopards in Pamir, Northern Pakistan: Implications for Human–Snow Leopard Conflicts

“…MaxEnt is one of the most widely used SDMs for predicting the potential distribution of a given species [36][37][38]. MaxEnt uses presence-only data and eco-geographical variables (EGV) to model the habitat suitability for a given species [39][40][41]. A previous study by Fonderflick, et al [42] reveals that the MaxEnt model is very suitable for elusive species for which absence data are unavailable or unreliable due to their rarity and thin density in their ranges.…”