Acknowledgement
본 연구는 국립산림과학원 난대·아열대산림연구소의 지원으로 수행되었습니다 (Project No. FE0200-2021-01-2022).
References
- AMNH 2022. American Musem of Natural History. https://biodiversityinformatics.amnh.org/open_source/maxent. Assessed 01 April 2022.
- Bae, E.H. and Jung, J.H. 2015. Prediction of shift in fish distributions in the Geum River Watershed under climate change. Ecology and Resilient Infrastructure 2: 198-205. (in Korean) https://doi.org/10.17820/eri.2015.2.3.198
- Cho, K.H. and Lee, S.H. 2015. Prediction of changes in the potential distribution of a waterfront alien plant, Paspalum distichum var. indutum, under climate change in the Korean Peninsula. Ecology and Resilient Infrastructure 2: 206-215. (in Korean) https://doi.org/10.17820/eri.2015.2.3.206
- Elith, J., Phillips, S.J., Hastie, T., Dudik, M., Chee, Y.E., and Yates, C.J. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17: 43-57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
- GBIF. 2022. Global Biodiversity Information Facility. http://data.gbif.org/species. Assessed 06 July 2022.
- Go, S.-J., Kang H., Chim S.-H., and Chang, J.I. 1997. Native habitat survey of wax myrtle in Cheju Province and its propagation by seed and cutting. Journal of Biological Production Facilities & Environmental Control 6: 225-234. (in Korean)
- Kim, C.H., Moon, M.O., An, J.G., Hwang, I.C., Lee, S.H., Choi, S.S., Lee, J.H., Beom, H.M., Kim, C.G., and Cha, J.Y. 2018. Floristic target species in Korea. National Institute of Ecology, Seocheon, South Korea. (in Korean)
- Kim, W.-M., Kim, C.Y., Cho, J.P., Hur, J.N., and Song, W.K. 2022. Prediction of Acer pictum subsp. mono distribution using bioclimatic predictor based on SSP scenario detailed data. Ecology and Resilient Infrastructure 9: 163-173. (in Korean) https://doi.org/10.17820/ERI.2022.9.3.163
- KMA. 2020. Korean climate change assessment report 2020. Korea Meteorological Administration, Seoul, South Korea. (in Korean)
- Lopez-Martinez, V., Sanchez-Martinez, G., Jimenez-Garcia, D., Perez-De la O, N.B., and Coleman, T.W. 2016. Environmental suitability for Agrilus auroguttatus (Coleoptera: Buprestidae) in Mexico using MaxEnt and database records of four Quercus (Fagaceae) species. Agricultural and Forest Entomology 18: 409-418. https://doi.org/10.1111/afe.12174
- NBC. 2022. CBD-CHM KOREA. National Biodiversity Center. https://www.kbr.go.kr. Assessed 06 July 2022.
- Negrini, M., Fidelis, E.G., Picanco, M.C., and Ramos, R.S. 2020. Mapping of the Steneotarsonemus spinki invasion risk in suitable areas for rice (Oryza sativa) cultivation using MaxEnt. Experimental and Applied Acarology 80: 445-461.
- NIFS. 2021. Analysis of potential distribution area of subtropical forest life resources in response to climate change. National Institute of Forest Science, Seoul, South Korea. (in Korean)
- Oh, Y.-J., Kim, M.-H., Choi, S.-K., Kim, M.-K., Eo, J., Yeob, S.-J., Bang, J. H., and Lee, Y. H. 2021. Prediction of the spatial distribution of suitable habitats for Geranium carolinianum under SSP scenarios. Ecology and Resilient Infrastructure, 8: 154-163. (in Korean)
- 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., and Sanderson, B. M. 2016. The Scenario model intercomparison project (ScenarioMIP) for CMIP6, Geoscientific Model Development, 9: 3461-3482. https://doi.org/10.5194/gmd-9-3461-2016
- Padalia, H., Srivastava, V., and Kushwaha, S.P.S. 2014. Modeling potential invasion range of alien invasive species, Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP. Ecological Informatics 22: 36-43. https://doi.org/10.1016/j.ecoinf.2014.04.002
- Palkar, R.S., Janarthanam, M.K., and Sellappan, K. 2020. Prediction of potential distribution and climatic factors influencing Garcinia indica in the Western Ghats of India using ecological niche modeling. National Academy Science Letters 7: 1-7.
- Phillips, S.J., Anderson, R.P., and Schapire, R.E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
- Phillips, S.J., Anderson, R.P., Dudik, M., Schapire, R.E., and Blair, M.E. 2017. Opening the black box: An open-source release of Maxent. Ecography 40: 887-893. https://doi.org/10.1111/ecog.03049
- QGIS Development Team. 2022. QGIS Geographic Information System. Open Source Geospatial Foundation. http://qgis.org. Assessed 06 April 2022.
- Santos, R.R., Hernandez, M.J.P., Baez, W.L., Ramos, J.H., Flores, H.J. M., Uicab, J.V.C., and Santos, M.D.R. 2018. The ecological niche as a tool for predicting potential areas of two pine species. Mexican Journal of Forest Sciences, 9: 47-68.
- Schmidt, H., Radinger, J., Teschlade, D., and Stoll, S. 2020. The role of spatial units in modelling freshwater fish distributions: Comparing a subcatchment and river network approach using MaxEnt. Ecological Modelling 418: 108937.
- Valavi, R., Shafizadeh-Moghadam, H., Matkan, A., Shakiba, A., Mirbagheri, B., and Kia, S.H. 2019. Modelling climate change effects on Zagros forests in Iran using individual and ensemble forecasting approaches. Theoretical and Applied Climatology 137: 1015-1025. https://doi.org/10.1007/s00704-018-2625-z
- Warren, D.L., Glor, R.E., and Turelli, M. 2010. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33: 607-611. https://doi.org/10.1111/j.1600-0587.2009.06142.x
- Worldclim. 2022. Global climate and weather data. http://www.worldclim.org. Assessed 06 July 2022.