Kim, I. R. et al. Genetic diversity and population structure of nutria (Myocastor coypus) in South Korea. Animals 9, 1164. https://doi.org/10.3390/ani9121164 (2019).
Google Scholar
GISD. Of the World’s Worst Invasive Alien Species. Global Invasive Species Database. http://www.iucngisd.org/gisd/100_worst.php. 100, (2021).
Hong, S., Do, Y., Kim, J. Y., Kim, D. & Joo, G. Distribution, spread and habitat preferences of nutria (Myocastor coypus) invading the lower Nakdong River, South Korea. Biol. Invas. 17, 1485–1496. https://doi.org/10.1007/s10530-014-0809-8 (2015).
Google Scholar
Ojeda, R., Bidau, C. & Emmons, L. Myocastor coypus (errata version published in 2017). The IUCN Red List Threat. Species (2016): e.T14085A121734257.
Tsiamis, K. et al. Baseline Distribution of Invasive Alien Species of Union Concern (Publications Office of the European Union, 2017).
Carter, J. & Leonard, B. P. A review of the literature on the worldwide distribution, spread of, and efforts to eradicate the coypu (Myocastor coypus). Wildl. Soc. Bull. 30, 162–175 (2002).
Kim, Y. C. et al. Distribution and management of nutria (Myocastor coypus) populations in South Korea. Sustainability 11, 4169. https://doi.org/10.3390/su11154169 (2019).
Google Scholar
Park, J. H. et al. The first case of Capillaria hepatica infection in a nutria (Myocastor coypus) in Korea. Korean J. Parasitol. 52, 527–529. https://doi.org/10.3347/kjp.2014.52.5.527 (2014).
Google Scholar
Fratini, F., Turchi, B. E., Ebani, V. V. & Bertelloni, F. The presence of Leptospira in coypus (Myocastor coypus) and rats (Rattus norvegicus) living in a protected wetland in Tuscany (Italy). Vet. Arh. 85, 407–414 (2015).
Lee, D. H., Kil, J. H. & Kim, D. E. The study on the distribution and inhabiting status of nutria (Myocastor coypus) in Korea. Korean J. Environ. Ecol. 27, 316–326 (2013).
Google Scholar
Guichón, M. L., Doncaster, C. P. & Cassini, M. H. Population structure of coypus (Myocastor coypus) in their region of origin and comparison with introduced populations. J. Zool. 261, 265–272. https://doi.org/10.1017/S0952836903004187 (2003).
Google Scholar
Bertolino, S., Perrone, A. & Gola, L. Effectiveness of coypu control in small Italian Wetland areas. Wildl. Soc. Bull. 33, 714–720. https://doi.org/10.2193/0091-7648(2005)33[714:EOCCIS]2.0.CO;2 (2005).
Google Scholar
Schertler, A. et al. The potential current distribution of the coypu (Myocastor coypus) in Europe and climate change induced shifts in the near future. NeoBiota 58, 129–160. https://doi.org/10.3897/neobiota.58.33118 (2020).
Google Scholar
Hilts, D. J., Belitz, M. W., Gehring, T. M., Pangle, K. L. & Uzarski, D. G. Climate change and nutria range expansion in the Eastern United States. J. Wild. Manaag. 83, 591–598. https://doi.org/10.1002/jwmg.21629’ (2019).
Google Scholar
Jarnevich, C. et al. Evaluating simplistic methods to understand current distributions and forecast distribution changes under climate change scenarios: An example with coypu (Myocastor coypus). NeoBiota 32, 107–125. https://doi.org/10.3897/neobiota.32.8884 (2017).
Google Scholar
Korean Metrological Administration, (2020). Korean Climate Change Assessment Report 2020.
Guillera-Arroita, G. et al. Is my species distribution model fit for purpose? Matching data and models to applications. Glob. Ecol. Biogeogr. 24, 276–292. https://doi.org/10.1111/geb.12268 (2015).
Google Scholar
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).
Google Scholar
Hong, S., Cowan, P., Do, Y. & Gim, J. S. Seasonal feeding habits of coypu (Myocastor coypus) in South Korea. Hystrix 27, 123–128 (2016).
Kim, H. S., Kong, J. Y., Kim, J. H., Yeon, S. C. & Hong, I. H. A Case of Fascioliasis in A Wild Nutria, Myocastor coypus Republic of Korea. Korean J. Parasitol. 56, 375–378. https://doi.org/10.3347/kjp.2018.56.4.375 (2018).
Google Scholar
Do, Y., Kim, J. Y., Im, R. Y. & Kim, S. B. Spatial distribution and social characteristics for wetlands in Gyeongsangnam-do Province. Korean J. Limnol. 45, 252–260 (2012).
IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (2013).
Sheffels, T. R. Status of Nutria (Myocastor coypus) Populations in the Pacific Northwest and Development of Associated Control and Management Strategies, with an Emphasis on Metropolitan Habitats, PhD Thesis (Portland State Univ., 2013).
Doncaster, C. P. & MlCOL, T. Annual cycle of a coypu (Myocastor coypus) population: Male and female strategies. J. Zool. 217, 227–240. https://doi.org/10.1111/j.1469-7998.1989.tb02484.x (1989).
Google Scholar
Reggiani, G., Boitani, L. & Stefano, R. Population dynamics and regulation in the coypu Myocastor coypus in Central Italy. Ecography 18, 138–146. https://doi.org/10.1111/j.1600-0587.1995.tb00334.x (1995).
Google Scholar
Cha, Y., Cho, K. H., Lee, H., Kang, T. & Kim, J. H. The relative importance of water temperature and residence time in predicting cyanobacteria abundance in regulated rivers. Water Res. 124, 11–19. https://doi.org/10.1016/j.watres.2017.07.040 (2017).
Google Scholar
Hellmann, J. J., Byers, J. E., Bierwagen, B. G. & Dukes, J. S. Five potential consequences of climate change for invasive species. Conserv. Biol. 22, 534–543. https://doi.org/10.1111/j.1523-1739.2008.00951.x (2008).
Google Scholar
Pereira, A. D. et al. Modeling the geographic distribution of Myocastor coypus (Mammalia, Rodentia) in Brazil: Establishing priority areas for monitoring and an alert about the risk of invasion. Stud. Neotrop. Fauna Environ. 55, 139–148. https://doi.org/10.1080/01650521.2019.1707419 (2020).
Google Scholar
Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful?. Glob. Ecol. Biogeogr. 12, 361–371. https://doi.org/10.1046/j.1466-822X.2003.00042.x (2003).
Google Scholar
Rogers, C. E. & McCarty, J. P. Climate change and ecosystems of the mid-atlantic region. Clim. Res. 14, 235–244. https://doi.org/10.3354/cr014235 (2000).
Google Scholar
Adhikari, P. et al. Potential impact of climate change on plant invasion in the Republic of Korea. J. Ecol. Environ. 43, 36. https://doi.org/10.1186/s41610-019-0134-3 (2019).
Google Scholar
Welsch, D. J., Smart, D. L., Boyer, J. N. & Minkin, P. Forested Wetlands: Functions, Benefits and the Use of Best Management Practices (US Dept of the Interior Fish and Wildlife Service, 2021).
Borgnia, M., Galante, M. L. & Cassini, M. H. Diet of the coypu (nutria, Myocastor coypus) in agro-systems of Argentinean pampas. J. Wildl. Manag. 64, 354–361. https://doi.org/10.2307/3803233 (2000).
Google Scholar
Colares, I. G., Oliveira, R. N. V., Liveira, R. M. & Colares, E. P. Feeding habits of coypu (Myocastor coypus Molina 1978) in the wetlands of the Southern region of Brazil. An. Acad. Bras. Cienc. 82, 671–678. https://doi.org/10.1590/s0001-37652010000300015 (2010).
Google Scholar
Corriale, M. J., Arias, S. M., Bó, R. F. & Porini, G. Habitat-use patterns of the coypu (Myocastor coypus) in an urban wetland of its original distribution. Acta Theriol. 51, 295–302. https://doi.org/10.1007/BF03192681 (2006).
Google Scholar
Linscombe, G., Kinler, N. & Wright, V. Nutria population density and vegetative changes in brackish marsh in coastal Louisiana. In Worldwide Furbearer Conference Proceedings (eds Chapman, J. A. & Pursley, D.) 129–141 (Worlwide Furbearer Conference Inc, 1981).
Aliev, F. Contribution to the study of nutria migrations (Myocastor coypus). Saugetierkd. Mitt. 16, 301–303 (1968).
Farashi, A. & Najafabadi, M. S. A model to predict dispersion of the alien nutria, Myocastor coypus Molina, 1782 (Rodentia) Northern Iran. Acta Zool. Bulg. 69, 65–70 (2017).
Vilà, M. et al. How well do we understand the impacts of alien species on ecosystem services? A pan-European, cross-taxa assessment. Front. Ecol. Environ. 8, 135–144. https://doi.org/10.1890/080083 (2010).
Google Scholar
Adhikari, P. et al. Seasonal and altitudinal variation in roe deer (Capreolus pygargus tianschanicus) diet on Jeju Island, South Korea. J. Asia Pac. Biodivers. 9, 422–428. https://doi.org/10.1016/j.japb.2016.09.001 (2016).
Google Scholar
Koo, K. A., Kong, W. S., Nibbelink, N. P., Hopkinson, C. S. & Lee, J. H. Potential effects of climate change on the distribution of cold-tolerant evergreen broadleaved woody plants in the Korean Peninsula. PLoS ONE 10, e0134043. https://doi.org/10.1371/journal.pone.0134043 (2015).
Google Scholar
National Institute of Biological Research. Korean Red List of Threatened Species 2nd edn. (Ministry of Environement of Korea, 2014).
Kil, J. et al. Monitoring of Invasive Alien Species Designated by the Wildlife Protection Act (VII) (Natl Inst. of Environmental Research, 2013).
Busby, J. R. In Bioclim, a Bioclimatic Analysis and Prediction System in Nature Conservation: Cost Effective Biological Surveys and Data Analysis (eds Margules, C. R. & Austin, M. P.) 64–68 (CSIRO, 1991).
Lee I. H., Park S. H., Kang, H. S. & Cho C. H. Regional climate projections using the HadGEM3-RA in Proceedings of the 3rd International Conference on Earth System Modelling; Hamburg, Germany. 17–21 September 2012. (2012).
Robert, J. H., Phillips, S., Leathwick, J. & Elith, J. Package ‘dismo’ version 1.3. , https://cran.rproject.org/web/packages/dismo.pdf (2021).
Jeon, J. Y., Adhikari, P. & Seo, C. Impact of climate change on potential dispersal of Paeonia obovata (Paeoniaceae), a critically endangered medicinal plant of South Korea. Ecol. Environ. Conserv. 26, S145–S155 (2020).
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x (2013).
Google Scholar
Shin, M. S., Seo, C., Lee, M. & Kim, J. Y. Prediction of potential species richness of plants adaptable to climate change in the Korean Peninsula. J. Environ. Impact Assess. 27, 562–581 (2018).
Adhikari, P. et al. Northward range expansion of southern butterflies according to climate change in South Korea. KSCCR 11, 643–656. https://doi.org/10.15531/KSCCR.2020.11.6.643 (2020).
Google Scholar
Song, C. et al. Estimation of future land cover considering shared socioeconomic pathways using scenario generators. KSCCR 9, 223–234. https://doi.org/10.15531/KSCCR.2018.9.3.223 (2018).
Google Scholar
Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315. https://doi.org/10.1002/joc.5086 (2017).
Google Scholar
Dukes, J. S. & Mooney, H. A. Does global change increase the success of biological invaders?. Trends Ecol. Evol. 14, 135–139. https://doi.org/10.1016/s0169-5347(98)01554-7 (1999).
Google Scholar
Thuiller, W., Georges, D., Gueguen, M., Engler, R. & Breiner, F. Package ‘biomod2’: Ensemble Platform for Species Distribution Modeling, version 3.5.1. https://cran.r-project.org/web/packages/biomod2/biomod2.pdf (2021).
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x (2006).
Google Scholar
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many?. Methods Ecol. Evol. 3, 327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x (2012).
Google Scholar
Brown, J. L. SDM toolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5, 694–700. https://doi.org/10.1111/2041-210X.12200 (2014).
Google Scholar
Veloz, S. D. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence–only niche models. J. Biogeogr. 36, 2290–2299. https://doi.org/10.1111/j.1365-2699.2009.02174.x (2009).
Google Scholar
Adhikari, P., Lee, Y. H., Park, Y.-S. & Hong, S. H. Assessment of the spatial invasion risk of intentionally introduced alien plant species (IIAPS) under environmental change in South Korea. Biology 10, 1169 (2021).
Google Scholar
Hong, S. H., Lee, Y. H., Lee, G., Lee, D. H. & Adhikari, P. Predicting impacts of climate change on northward range expansion of invasive weeds in South Korea. Plants 10, 1604. https://doi.org/10.3390/plants10081604 (2021).
Google Scholar
Pearsons, R. G. Species distribution modeling for conservation educators and practitioners. Lessons Conserv. 3, 54–58 (2010).
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x (2006).
Google Scholar
Thuiller, W., Lavorel, S. & Araújo, M. B. Niche properties and geographical extent as predictors of species sensitivity to climate change. Glob. Ecol. Biogeogr. 14, 347–357. https://doi.org/10.1111/j.1466-822X.2005.00162.x (2005).
Google Scholar
Lobo, J. M., Jiménez-Valverde, A. & Real, R. AUC: A misleading measure of the performance of predictive distribution models. Global. Ecol. Biogeography. 17, 145–151. https://doi.org/10.1111/j.1466-8238.2007.00358.x (2008).
Google Scholar
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).
Google Scholar
Baldwin, R. Use of maximum entropy modeling in wildlife research. Entropy 11, 854–866. https://doi.org/10.3390/e11040854 (2009).
Google Scholar
Adhikari, P. et al. Potential impact of climate change on the species richness of subalpine plant species in the mountain national parks of South Korea. J. Ecol. Environ. 42, 36. https://doi.org/10.1186/s41610-018-0095-y (2018).
Google Scholar
Hijmans, R. J. et al. Package ‘raster’ v 3.5: geographical data analysis and modeling. https://cran.r-project.org/web/packages/raster/raster.pdf, (2021).
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