Adhikari P, Lee Y H, Poudel A, Lee G, Hong S H, Park Y S. 2023. Predicting the impact of climate change on the habitat distribution of Parthenium hysterophorus around the world and in South Korea. Biology, 12, 84.

Allam Z, Bibri S E, Sharpe S A. 2022. The rising impacts of the COVID-19 pandemic and the Russia–Ukraine war: Energy transition, climate justice, global inequality, and supply chain disruption. Resources, 11, 99.

Andersen M C, Adams H, Hope B, Powell M. 2004. Risk assessment for invasive species. Risk Analysis, 24, 787–793.

Araújo M B, New M. 2007. Ensemble forecasting of species distributions. Trends in Ecology & Evolution, 22, 42–47.

Barbet-Massin M, Jiguet F, Albert C H, Thuiller W. 2012. Selecting pseudo-absences for species distribution models: How, where and how many? Methods in Ecology and Evolution, 3, 327–338.

Beaumont L J, Hughes L, Pitman A J. 2008. Why is the choice of future climate scenarios for species distribution modelling important? Ecology Letters, 11, 1135–1146.

Blok V, Pickup J, Davie K, Kettle H, Ewing D, Roberts A, Kuhfuss L, Kleczkowski A, McDougall B. 2020. The Future Threat of PCN in Scotland. Project Final Report. PHC2018/16. Scotland’s Centre of Expertise for Plant Health (PHC).

Breiner F T, Guisan A, Bergamini A, Nobis M P. 2015. Overcoming limitations of modelling rare species by using ensembles of small models. Methods in Ecology and Evolution, 6, 1210–1218.

Brownlee J. 2020. Why use ensemble learning? [2022-6-10]. https://machinelearningmastery.com/why-use-ensemble-learning

Buebos-Esteve D E, Mamasig G D N S, Ringor A M D, Layog H N B, Murillo L C S, Dagamac N H A. 2023. Modeling the potential distribution of two immortality flora in the Philippines: Applying MaxEnt and GARP algorithms under different climate change scenarios. Modeling Earth Systems and Environment, 9, 2857–2876.

Buisson L, Thuiller W, Casajus N, Lek S, Grenouillet G. 2010. Uncertainty in ensemble forecasting of species distribution. Global Change Biology, 16, 1145–1157.

Burnham K P, Anderson D R. 2002. Model selection and multimodel inference. In: A Practical Information-Theoretic Approach. Springer, New York.

CABI (Commonwealth Agricultural Bureaux International). 2021a. Globodera pallida (white potato cyst nematode). [2021-12-31]. https://www.cabi.org/isc/datasheet/27033

CABI (Commonwealth Agricultural Bureaux International). 2021b. Globodera rostochiensis (yellow potato cyst nematode). [2021-12-31]. https://www.cabi.org/isc/datasheet/27034

Chytrý M, Wild J, Pyšek P, Jarošík V, Dendoncker N, Reginster I, Pino J, Maskell L C, Vilà M, Pergl J, Kühn I, Spangenberg J H, Settele J. 2012. Projecting trends in plant invasions in Europe under different scenarios of future land-use change. Global Ecology and Biogeography, 21, 75–87.

Cook B I, Mankin J S, Marvel K, Williams A P, Smerdon J E, Anchukaitis K J. 2020. Twenty-first century drought projections in the CMIP6 forcing scenarios. Earth’s Future, 8, e2019EF001461.

Dong X, Yu Z, Cao W, Shi Y, Ma Q. 2020. A survey on ensemble learning. Frontiers of Computer Science, 14, 241–258.

Duan Y X. 2011. Plant Nematology. Sciencep, Beijing. pp. 43. (in Chinese)

Duyck P F, Dortel E, Tixier P, Vinatier F, Loubana P M, Chabrier C, Quénéhervé P. 2012. Niche partitioning based on soil type and climate at the landscape scale in a community of plant-feeding nematodes. Soil Biology and Biochemistry, 44, 49–55.

EcoCommons. 2022. Maxent. EcoCommons. [2022-11-30]. https://support.ecocommons.org.au/support/solutions/articles/6000254294-maxent

EPPO (European and Mediterranean Plant Protection Organization). 1998. Guidelines on pest risk analysis check-list of information required for pest risk analysis (PRA). In: EPPO Standards. EPPO, Paris.

EPPO (European and Mediterranean Plant Protection Organization). 2020a. Globodera pallida. [2020-6-23]. https://gd.eppo.int/taxon/HETDPA/categorization

EPPO (European and Mediterranean Plant Protection Organization). 2020b. Globodera rostochiensis. [2020-6-30]. https://gd.eppo.int/taxon/HETDRO/categorization

Eyring V, Bony S, Meehl G A, Senior C A, Stevens B, Stouffer R J, Taylor K E. 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9, 1937–1958.

Fan X, Duan Q, Shen C, Wu Y, Xing C. 2020. Global surface air temperatures in CMIP6: Historical performance and future changes. Environmental Research Letters, 15, 104056.

Finch D M, Butler J L, Runyon J B, Fettig C J, Kilkenny F F, Jose S, Frankel S J, Cushman S A, Cobb R C, Dukes J S. 2021. Effects of climate change on invasive species. In: Poland T M, Patel-Weynand T, Finch D M, Miniat C F, Hayes D C, Lopez V M eds., Invasive Species in Forests and Rangelands of the United States. Springer, Cham, Poland. p. 57.

Franklin J, Miller J A. 2010. Mapping Species Distributions. Cambridge University Press, Cambridge.

Gbif.org. 2021. Occurrence download. The global biodiversity information facility. [2021-10-20]. https://www.gbif.org/occurrence/download/0032093–210914110416597

Guisan A, Petitpierre B, Broennimann O, Daehler C, Kueffer C. 2014. Unifying niche shift studies: Insights from biological invasions. Trends in Ecology & Evolution, 29, 260–269.

Guisan A, Thuiller W, Zimmermann N E. 2017. Habitat Suitability and Distribution Models with Applications in R. Cambridge University Press, London.

Hamed M M, Nashwan M S, Shahid S. 2022. A novel selection method of CMIP6 GCMs for robust climate projection. International Journal of Climatology, 42, 4258–4272.

Hao T, Elith J, Guillera‐Arroita G, Lahoz‐Monfort J J. 2019. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Diversity and Distributions, 25, 839–852.

Hao T, Elith J, Lahoz‐Monfort J J, Guillera‐Arroita G. 2020. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography, 43, 549–558.

Harris R M B, Grose M R, Lee G, Bindoff N L, Porfirio L L, Fox-Hughes P. 2014. Climate projections for ecologists. Wiley Interdisciplinary Reviews: Climate Change, 5, 621–637.

He Y, Wang G, Ren Y, Zheng D, Gao S, McKirdy S J, Chu D. 2023. Integrated prediction of potato cyst nematodes’ (Globodera rostochiensis and Globodera pallida) potential distribution and risk regions: Using China as a case study. [2023-01-13]. https://dx.doi.org/10.21203/rs.3.rs-2466031/v1 (preprint)

He Y, Wang R, Zhao H, Ren Y, Agarwal M, Zheng D, Gao S, McKirdy S J, Chu D. 2022. Predicting potential global distribution and risk regions for potato cyst nematodes (Globodera rostochiensis and Globodera pallida). Scientific Reports, 12, 21843.

Hijmans R J. 2012. Cross-validation of species distribution models: Removing spatial sorting bias and calibration with a null model. Ecology, 93, 679–688.

Hodda M, Cook D C. 2009. Economic impact from unrestricted spread of potato cyst nematodes in Australia. Phytopathology, 99, 1387–1393.

Hutchinson G E. 1991. Population studies: Animal ecology and demography. Bulletin of Mathematical Biology, 53, 193–213.

IPPC (International Plant Protection Convention) Secretariat. 2019. Framework for pest risk analysis. Food and Agriculture Organization of the United Nations, Rome.

Jones L M, Koehler A K, Trnka M, Balek J, Challinor A J, Atkinson H J, Urwin P E. 2017. Climate change is predicted to alter the current pest status of Globodera pallida and G. rostochiensis in the United Kingdom. Global Change Biology, 23, 4497–4507.

Jordan M I, Mitchell T M. 2015. Machine learning: Trends, perspectives, and prospects. Science, 349, 255–260.

Kaczmarek A, MacKenzie K, Kettle H, Blok V C. 2014. Influence of soil temperature on Globodera rostochiensis and Globodera pallida. Phytopathologia Mediterranea, 53, 396–405.

Kaky E, Nolan V, Alatawi A, Gilbert F. 2020. A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants. Ecological Informatics, 60, 101150.

Kim E, Seo Y, Kim Y S, Park Y, Kim Y H. 2017. Effects of soil textures on infectivity of root-knot nematodes on carrot. Plant Pathology Journal, 33, 66–74.

Kindt R. 2018. Ensemble species distribution modelling with transformed suitability values. Environmental Modelling & Software, 100, 136–145.

Li X, Wang Y. 2013. Applying various algorithms for species distribution modelling. Integrative Zoology, 8, 124–135.

Liu C, White M, Newell G. 2013. Selecting thresholds for the prediction of species occurrence with presence-only data. Journal of Biogeography, 40, 778–789.

Merow C, Smith M J, Silander Jr J A. 2013. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography, 36, 1058–1069.

Miller J. 2010. Species distribution modeling. Geography Compass, 4, 490–509.

Mohammed K S, Usman M, Ahmad P, Bulgamaa U. 2022. Do all renewable energy stocks react to the war in Ukraine? Russo-Ukrainian conflict perspective. Environmental Science and Pollution Research, 30, 36782–36793.

Nakicenovic N, Alcamo J, Davis G, Vries B D, Fenhann J, Gaffin S, Gregory K, Griibler A, Jung T Y, Kram T, Rovere E L L, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Riahi K, Roehrl A, Rogner H H, et al. 2000. Emissions scenarios. In: Nakicenovic N, Swart R, eds., The Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, England. p. 59.

O’Neill B C, Tebaldi C, Van Vuuren D P, Eyring V, Friedlingstein P, Hurtt G, Knutti R, Kriegler E, Lamarque J F, Lowe J, Meehl G A, Moss R, Riahi K, Sanderson B M. 2016. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9, 3461–3482.

Polikar R. 2012. Ensemble learning. In: Zhang C, Ma Y, eds., Ensemble Machine Learning. Springer, Germany. pp. 1–34.

Pompeu J, de Oliveira Portella R. 2023. Genetic viability and habitat suitability of the Critically Endangered southern muriqui (Brachyteles arachnoides) in the Atlantic Forest’s fragmented landscapes under land use and climate change scenarios. Climate Change Ecology, 5, 100065.

Price J A, Coyne D, Blok V C, Jones J T. 2021. Potato cyst nematodes Globodera rostochiensis and G. pallida. Molecular Plant Pathology, 22, 495–507.

Rana S K, Rana H K, Luo D, Sun H. 2021. Estimating climate-induced ‘nowhere to go’ range shifts of the Himalayan Incarvillea Juss. using multi-model median ensemble species distribution models. Ecological Indicators, 121, 107127.

Riahi K, Van Vuuren D P, Kriegler E, Edmonds J, O’Neill B C, Fujimori S, Bauer N, Calvin K, Dellink R, Fricko O, Lutz W, Popp A, Cuaresma J C, Kc S, Leimbach M, Jiang L, Kram T, Rao S, Emmerling J, Ebi K, et al. 2017. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168.

Robinson T B, Martin N, Loureiro T G, Matikinca P, Robertson M P. 2020. Double trouble: the implications of climate change for biological invasions. NeoBiota, 62, 463–487.

Ruthes A C, Dahlin P. 2022. The impact of management strategies on the development and status of potato cyst nematode populations in Switzerland: An overview from 1958 to present. Plant Disease, 106, 1096–1104.

Shabani F, Kumar L, Ahmadi M. 2018. Assessing accuracy methods of species distribution models: AUC, specificity, sensitivity and the true skill statistic. Global Journal of Human Social Science, 18, 6–18.

Sillero N, Campos J C, Arenas-Castro S, Barbosa A M. 2023. A curated list of R packages for ecological niche modelling. Ecological Modelling, 476, 110242.

da Silva J C P, de Medeiros F H V, Campos V P. 2018. Building soil suppressiveness against plant-parasitic nematodes. Biocontrol Science and Technology, 28, 423–445.

Skelsey P, Kettle H, Mackenzie K, Blok V. 2018. Potential impacts of climate change on the threat of potato cyst nematode species in Great Britain. Plant Pathology, 67, 909–919.

Song Y H, Nashwan M S, Chung E-S, Shahid S. 2021. Advances in CMIP6 INM-CM5 over CMIP5 INM-CM4 for precipitation simulation in South Korea. Atmospheric Research, 247, 105261.

Srivastava V, Lafond V, Griess V C. 2019. Species distribution models (SDM): Applications, benefits and challenges in invasive species management. CAB Reviews, 14, 1–13.

Stanton J C, Pearson R G, Horning N, Ersts P, Reşit Akçakaya H. 2012. Combining static and dynamic variables in species distribution models under climate change. Methods in Ecology and Evolution, 3, 349–357.

Stewart S B, Fedrigo M, Kasel S, Roxburgh S H, Choden K, Tenzin K, Allen K, Nitschke C R. 2022. Predicting plant species distributions using climate‐based model ensembles with corresponding measures of congruence and uncertainty. Diversity and Distributions, 28, 1105–1122.

Sukhanova A A, Boyandin A N, Ertiletskaya N L. 2022. Potato cyst nematode management strategies: Current situation and promising approaches. ACS Agricultural Science & Technology, 2, 415–425.

Thuiller W, Georges D, Gueguen M, Engler R, Breiner F. 2021. Biomod2: Ensemble platform for species distribution modeling. CRAN. [2022-01-20]. https://CRAN.R-project.org/package=biomod2

Thuiller W, Lafourcade B, Engler R, Araújo M B. 2009. BIOMOD - a platform for ensemble forecasting of species distributions. Ecography, 32, 369–373.

Touzé‐Peiffer L, Barberousse A, Le Treut H. 2020. The coupled model intercomparison project: History, uses, and structural effects on climate research. Wiley Interdisciplinary Reviews: Climate Change, 11, e648.

Wallingford P D, Morelli T L, Allen J M, Beaury E M, Blumenthal D M, Bradley B A, Dukes J S, Early R, Fusco E J, Goldberg D E, Ibáñez I, Laginhas B B, Vilà M, Sorte C J B. 2020. Adjusting the lens of invasion biology to focus on the impacts of climate-driven range shifts. Nature Climate Change, 10, 398–405.

Witze A. 2022. Russia’s war in Ukraine forces Arctic climate projects to pivot. Nature, 607, 432.

Yang Y. 2017. Chapter 4: Ensemble learning. In: Yang Y, ed., Temporal Data Mining Via Unsupervised Ensemble Learning. Elsevier, the Netherlands. pp. 35–56.