Evaluating the invasion risk of longhorn crazy ants (Paratrechina longicornis) in South Korea using spatial distribution model

Agri and Forest Entomology

et al. 2021

Self Cite

Anoplophora glabripennis (Motschulsky) (Coleoptera: Cerambycidae) is a forest pest that damages a wide range of trees in areas where it has recently been introduced, demanding a proactive evaluation of its possible future distribution. 2 This study aimed to project the potential distribution of A. glabripennis using species distribution modelling and constructed an ensemble map for evaluating global risk areas. 3 We used CLIMEX and MaxEnt to evaluate the potential distribution of A. glabripennis as a function of current and future climates. 4 The results showed that the models predicted a high probability of A. glabripennis distribution where this species is currently found, and the suitable climate was shifted northward due to climate change. 5 The projected area differed between the models because of different modelling algorithm and climate change scenario; thus, an ensemble map projecting the consensus areas from two models was constructed to identify the risk areas that corresponded to the eastern United States, Europe, and native countries, Korea and China, and nearby Japan. 6 From the perspective of ensemble modelling for evaluating species distributions with reduced uncertainties, this study will enhance the model reliability for defining areas at risk of A. glabripennis occurrence.

Section: Operating the Maxent Modelmentioning

confidence: 99%

Climate‐based ensemble modelling to evaluate the global distribution of Anoplophora glabripennis (Motschulsky)

Byeon

Agri and Forest Entomology

et al. 2021

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“…To apply the climate change scenario, the Shared Socioeconomic Pathway (SSP) 245 for years 2081–2100, generated by the MIROC-6 model, was obtained from WorldClim ( ) (accessed on 15 March 2021) with a resolution of 30 s [ 47 , 55 ]. We used SSP245 to build the models by assuming a moderate emission scenario to avoid extreme simulation, and it predicted that the temperature would increase up to 4.2 °C, CO₂ emission at 550 ppm, and the precipitation also will be varied [ 56 , 57 ]. We selected 2081–2100 for a suitable period, not too short or too long to observe the change in future pest distribution.…”

Section: Methodsmentioning

confidence: 99%

“…We evaluated the performance of the model using three metrics that are generally used for the presence-only model: area under the receiver operating characteristic curve (AUC), true skill statistic (TSS), and omission rate 10% (OR10%). In general, an AUC of 0.5 means that model prediction is not better than random, values of >0.7 signify reasonable model prediction, and values of >0.9 indicate high performance [ 57 , 58 , 59 ]. TSS is a more practical and realistic metric for model performance compared with the AUC; it evaluates a model based on the selected threshold [ 60 ].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Spatial Distribution of Three Major Leptocorisa (Hemiptera: Alydidae) Pests Using MaxEnt Model

Hwang

Yoon

et al. 2022

Insects

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We targeted three major Leptocorisa species (L. chinensis, L. acuta, and L. oratoria) and evaluated their potential distributions using MaxEnt. The results showed that most Asian countries and northern Australia would be suitable for at least one of these pest species, and climate change will expand their habitat northward. All of the developed models were evaluated to be excellent with AUC, TSS, and OR10%. Most of the recorded regions of the Leptocorisa species are consistent with the result of potential distributions predicted in this study. The results confirmed that the minimum temperature of the coldest month mainly influences the three Leptocorisa species distributions. The potential distributions of the three species cover major rice cultivation areas regardless of climate change, suggesting that it would be necessary to establish a sustainable control strategy for the pests.

“…2020; Lee et al . 2021). For the reason that abiotic factors affect survival and colony formation (Abril et al .…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the CLIMEX model minimizes the effect of sampling bias, which is the main concern in other species distribution models that highly depend on presence data (Kramer-Schadt et al 2013;Kriticos et al 2015). Thus, CLIMEX has been used to predict the climatically favorable locations of various species (Worner 1988;Hughes & Maywald 1990;Sutherst & Maywald 2005), including some notable studies on notorious invasive ant species (Sutherst & Maywald 2005;Jung et al 2017;Byeon et al 2020;Lee et al 2021). For the reason that abiotic factors affect survival and colony formation (Abril et al 2008(Abril et al , 2010, we aimed to predict the spatial potential distribution of Argentine ants based on climatic suitability by developing the CLIMEX model.…”

Section: Introductionmentioning

confidence: 99%

Spatial and climatic analyses for predicting potential distribution of an invasive ant, Linepithema humile (Hymenoptera: Formicidae)

et al. 2022

Entomological Science

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The Argentine ant (Linepithema humile) is an invasive species that rapidly enters into new areas, causing worldwide ecological concern. Early assessment of its potential habitat could indicate areas that we need to pay attention to in advance, to prevent its invasion; therefore, in this study we aimed to predict the potential spatial distribution of the Argentine ant and analyze the climatic characteristics of its occurrence sites. The CLIMEX model was used to predict the spatial distribution of this ant species, while the probability density function was employed to extract climatic preferences in places where their main habitats are located. High climatic suitability was predicted in the eastern United States, eastern South America, central Africa, eastern Australia, and a few regions in India and China, suggesting the high possibility of its invasion worldwide. The frequency of occurrence was highest at approximately 20 C monthly average maximum temperature, 8 C monthly average minimum temperature, and 10 mm monthly precipitation. In addition, the occurrence records of Argentine ants were mostly shown to be above sub-zero temperatures. We expect that these results can be used to identify new areas exposed to the risk of Argentine ant invasion and for further application to establish monitoring strategies in advance.