[1]

Rawat A, Kumar D, Khati BS. 2024. A review on climate change impacts, models, and its consequences on different sectors: a systematic approach. Journal of Water and Climate Change 15:104−126

doi: 10.2166/wcc.2023.536
[2]

Walter JA, Ives AR, Tooker JF, Johnson DM. 2018. Life history and habitat explain variation among insect pest populations subject to global change. Ecosphere 9:e02274

doi: 10.1002/ecs2.2274
[3]

Marini L, Ayres MP, Jactel H. 2022. Impact of stand and landscape management on forest pest damage. Annual Review of Entomology 67:181−199

doi: 10.1146/annurev-ento-062321-065511
[4]

Oliveira CM, Auad AM, Mendes SM, Frizzas MR. 2013. Economic impact of exotic insect pests in Brazilian agriculture. Journal of Applied Entomology 137:1−15

doi: 10.1111/jen.12018
[5]

Feng X, Zhao Z, Wang Y, Zhang Y. 2025. Potentially suitable areas for Ricania speculum (Hemiptera: Ricaniidae) in China under climate change. Frontiers in Ecology and Evolution 13:1549532

doi: 10.3389/fevo.2025.1549532
[6]

Mazza G, Pennacchio F, Gargani E, Franceschini I, Roversi PF, et al. 2014. First report of Ricania speculum (Walker, 1851) in Europe (Hemiptera: Fulgoromorpha: Ricaniidae). Zootaxa 3861:297−300

doi: 10.11646/zootaxa.3861.3.7
[7]

Rossi E, Lucchi A. 2015. The Asian planthopper Ricania speculum (Walker) (Homoptera: Ricaniidae) on several crops in Italy: a potential threat to the EPPO region? EPPO Bulletin 45:119−122

doi: 10.1111/epp.12175
[8]

Wilson SW, Rossi E, Lucchi A. 2016. Descriptions of the adult genitalia and immatures of the Asian planthopper Ricania speculum (Hemiptera: Fulgoroidea: Ricaniidae) recently introduced to Italy. Annals of the Entomological Society of America 109:899−905

doi: 10.1093/aesa/saw056
[9]

Heather NW, Hallman GJ. 2008. Pest Management and Phytosanitary Trade Barriers. Wallingford, UK: CABI doi: 10.1079/9781845933432.0001
[10]

Skendžić S, Zovko M, Živković IP, Lešić V, Lemić D. 2021. The impact of climate change on agricultural insect pests. Insects 12:440

doi: 10.3390/insects12050440
[11]

Hulme PE. 2017. Climate change and biological invasions: evidence, expectations, and response options. Biological Reviews 92:1297−1313

doi: 10.1111/brv.12282
[12]

Elith J, Leathwick JR. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40:677−697

doi: 10.1146/annurev.ecolsys.110308.120159
[13]

Kriticos DJ, Kean JM, Phillips CB, Senay SD, Acosta H, et al. 2017. The potential global distribution of the brown marmorated stink bug, Halyomorpha halys, a critical threat to plant biosecurity. Journal of Pest Science 90:1033−1043

doi: 10.1007/s10340-017-0869-5
[14]

Phillips SJ, Dudík M. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161−175

doi: 10.1111/j.0906-7590.2008.5203.x
[15]

Byeon DH, Jung S, Lee WH. 2018. Review of CLIMEX and MaxEnt for studying species distribution in South Korea. Journal of Asia-Pacific Biodiversity 11:325−333

doi: 10.1016/j.japb.2018.06.002
[16]

Bradie J, Leung B. 2017. A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. Journal of Biogeography 44:1344−1361

doi: 10.1111/jbi.12894
[17]

Kim GY, Lee WH. 2025. Prediction of the spatial distribution of vine weevil under climate change using multiple variable selection methods. Scientific Reports 15:7845

doi: 10.1038/s41598-025-91058-0
[18]

Yoon S, Lee WH. 2021. Methodological analysis of bioclimatic variable selection in species distribution modeling with application to agricultural pests (Metcalfa pruinosa and Spodoptera litura). Computers and Electronics in Agriculture 190:106430

doi: 10.1016/j.compag.2021.106430
[19]

Franklin J. 2023. Species distribution modelling supports the study of past, present and future biogeographies. Journal of Biogeography 50:1533−1545

doi: 10.1111/jbi.14617
[20]

Yoon S, Jung JM, Hwang J, Park Y, Lee WH. 2023. Ensemble evaluation of the spatial distribution of pine wilt disease mediated by insect vectors in South Korea. Forest Ecology and Management 529:120677

doi: 10.1016/j.foreco.2022.120677
[21]

Global Biodiversity Information Facility (GBIF). 2025. GBIF occurrence download. doi: 10.15468/dl.h8htnr (accessed 15 July 2025)
[22]

European and Mediterranean Plant Protection Organization (EPPO). 2025. EPPO global database. https://gd.eppo.int (accessed 16 July 2025)
[23]

Brown JL. 2014. SDMtoolbox: a python‐based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods in Ecology and Evolution 5:694−700

doi: 10.1111/2041-210x.12200
[24]

Fick SE, Hijmans RJ. 2017. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37:4302−4315

doi: 10.1002/joc.5086
[25]

Harris I, Osborn TJ, Jones P, Lister D. 2020. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data 7:109

doi: 10.1038/s41597-020-0453-3
[26]

Kass JM, Muscarella R, Galante PJ, Bohl CL, Pinilla-Buitrago GE, et al. 2021. ENMeval 2.0: redesigned for customizable and reproducible modeling of species’ niches and distributions. Methods in Ecology and Evolution 12:1602−1608

doi: 10.1111/2041-210x.13628
[27]

Muscarella R, Galante PJ, Soley-Guardia M, Boria RA, Kass JM, et al. 2014. ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution 5:1198−1205

doi: 10.1111/2041-210X.12261
[28]

Phillips SJ, Anderson RP, Schapire RE. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190:231−259

doi: 10.1016/j.ecolmodel.2005.03.026
[29]

Breiman L. 2001. Random forests. Machine Learning 45:5−32

doi: 10.1023/A:1010933404324
[30]

Song JW, Seo J, Lee WH. 2025. Spatial evaluation of Salurnis marginella occurrence according to climate change using multiple species distribution models. Agriculture 15:297

doi: 10.3390/agriculture15030297
[31]

Huang Y, Dong Y, Huang W, Guo J, Hao Z, et al. 2024. Predicting the global potential suitable distribution of fall armyworm and its host plants based on machine learning models. Remote Sensing 16:2060

doi: 10.3390/rs16122060
[32]

Chen T, Guestrin C. 2016. XGBoost: a scalable tree boosting system. arXiv 1603.02754v3

doi: 10.48550/arXiv.1603.02754
[33]

Byeon DH, Lee WH. 2025. Global multi-ensemble approach for assessing the invasion potential of tropical fire ant in South Korea. Global Ecology and Conservation 62:e03813

doi: 10.1016/j.gecco.2025.e03813
[34]

Kim TH, Byeon DH, Song JW, Lee WH. 2024. Ensemble spatial modeling, considering habitat and biological traits, for predicting the potential distribution of Corythucha ciliata. Entomological Research 54:e12756

doi: 10.1111/1748-5967.12756
[35]

Lobo JM, Jiménez-Valverde A, Real R. 2008. AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography 17:145−151

doi: 10.1111/j.1466-8238.2007.00358.x
[36]

Allouche O, Tsoar A, Kadmon R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43:1223−1232

doi: 10.1111/j.1365-2664.2006.01214.x
[37]

Grinsztajn L, Oyallon E, Varoquaux G. 2022. Why do tree-based models still outperform deep learning on tabular data? arXiv 2207.08815v1

doi: 10.48550/arXiv.2207.08815
[38]

García-Gómez RE, Aceves-Medina G, Villalobos H, Jiménez Rosenberg SPA, Durazo R. 2023. Predictive performance from abundance distribution models of Vinciguerria lucetia larvae in the southern portion of the California current system using XGBOOST. Deep Sea Research Part II: Topical Studies in Oceanography 212:105336

doi: 10.1016/j.dsr2.2023.105336
[39]

Booth TH, Nix HA, Busby JR, Hutchinson MF. 2014. BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Diversity and Distributions 20:1−9

doi: 10.1111/ddi.12144
[40]

Gao T, Brożek J, Dai W. 2022. Fine-structural morphology of the mouthparts of the polyphagous invasive planthopper, Ricania speculum (Walker) (Hemiptera: Fulgoromorpha: Ricaniidae). Insects 13:843

doi: 10.3390/insects13090843
[41]

Choi YS, Seo HY, Jo SH, Whang IS, Lee YS, et al. 2017. Host preference of Ricania spp. (Hemiptera: Ricaniidae) at different developmental stages. Korean Journal of Applied Entomology 56:319−329

doi: 10.5656/KSAE.2017.09.0.023
[42]

Lee HA, Lee YS. 2025. Seasonal occurrence and host plants of the potential pest, black planthopper, Ricania speculum (Hemiptera: Fulgoroidea: Ricaniidae). Korean Journal of Applied Entomology 64:127−130

doi: 10.5656/KSAE.2025.04.0.014
[43]

Grüter R, Trachsel T, Laube P, Jaisli I. 2022. Expected global suitability of coffee, cashew and avocado due to climate change. PLoS One 17:e0261976

doi: 10.1371/journal.pone.0261976
[44]

Baek S, Kim MJ, Lee JH. 2019. 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. Forests 10:490

doi: 10.3390/f10060490
[45]

Hastings A, Cuddington K, Davies KF, Dugaw CJ, Elmendorf S, et al. 2005. The spatial spread of invasions: new developments in theory and evidence. Ecology Letters 8:91−101

doi: 10.1111/j.1461-0248.2004.00687.x