Model-based assessment of changes in the potential distribution of Solenopsis geminata (Hymenoptera: Formicidae) according to climate change scenarios

Predicting the Global Distribution of Solenopsis geminata (Hymenoptera: Formicidae) under Climate Change Using the MaxEnt Model

“…In addition, the MaxEnt model showed more conservative results than the CLIMEX model in the prediction of an agricultural insect pest with global climatic suitability [25]. Furthermore, the study of Byeon et al [7] lacked information related to the influence of environmental variables on the prediction of S. geminata, whereas our model showed that the annual mean temperature, annual precipitation, and precipitation in the driest quarter were the key factors influencing the distribution of S. geminata.…”

“…The model then generates relative habitat suitability for a target species [ 26 ]. The MaxEnt model outperforms the majority of existing correlative modeling approaches and has been widely used to predict the potential distribution of insect pests [ 7 , 25 , 27 ].…”

“…Among them, Solenopsis geminata, the Bioclimate data of the CliMond climate dataset [31] were used as explanatory variables for both current and future climate conditions with a spatial resolution of 10 arc minutes. The CliMond climate data, consisting of 40 variables, have been widely used for predicting the current and future distributions of target species [7,25]. Among them, 19 core variables (Bio 1 to Bio 19) were used in this study.…”

“…Ants are a particularly conspicuous invasive species, with more than 200 species having spread outside their native range [ 5 , 6 ]. Among them, Solenopsis geminata , the tropical fire ant, is one of the worst invasive species, which causes damage to human health, plants, animals, and artificial equipment [ 7 , 8 , 9 ]. S. geminata is native to tropical and temperate regions of the American continent [ 10 ]; however, this ant has spread around the world, to Africa, south and southeast Asia, and Australia [ 9 , 11 , 12 ], through international trade and human activities.…”

“…Various ENMs have exhibited good performance for diverse species; for example, generalized linear models have been used to evaluate habitat conditions for lynx restoration [ 21 ], machine learning methods such as random forest and support vector machine have been used to predict the occurrence of insects and plants [ 20 , 22 ], and a boosted regression tree has been used for the potential global distribution of red imported fire ant [ 23 ]. Furthermore, CLIMEX, which is a mechanistic niche model [ 24 ], has been used to predict the distribution of various species according to the organism’s physiological tolerance parameters [ 7 , 25 ]. Among the many available ENM methods, the maximum entropy (MaxEnt) algorithm is considered an excellent tool with high prediction performance that has been widely used for various species [ 18 , 26 , 27 ].…”