Advanced SearchSearch Tips
Predicting change of suitable plantation of Schisandra chinensis with ensemble of climate change scenario
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Predicting change of suitable plantation of Schisandra chinensis with ensemble of climate change scenario
Lee, Sol Ae; Lee, Sang-Hyuk; Ji, Seung-Yong; Choi, Jaeyong;
  PDF(new window)
Predicting possible distributed area of Schisandra chinensis which has long term cultivation period among non-timber forest products is needed to be studied to deal with climate change. Hence, distribution of Schisandra chinensis in the 2050s and 2070s was predicted under two scenario, RCP 4.5 and RCP 8.5, with ensemble of 5 climate models used in IPCC AR5. According to estimation using RCP 4.5, distribution of Schisandra chinensis in 2050s appeared to decrease 43% of current area and appeared to decrease 57% in 2070s respectively. Moreover, According to estimation using RCP 8.5, distribution of Schisandra chinensis in 2050s appeared to decrease 55% of current area and appeared to decrease 85% in 2070s. As a final outcome, Schisandra chinensis was estimated to extinct in the future except Gangwon-do and Gyeongsangbuk-do when analyzing change between current distributed area and future distributed area. As a result, those areas were classified as vulnerable areas to climate change. Therefore, Gangwon-do and Gyeongsangbuk-do were thought to be ideal for growing Schisandra chinensis. The result from this study can be used to provide basic information for selecting proper area of Schisandra chinensis considering climate change effect.
CMIP5;RCP;SDM;Maxent;Non-timber forest Products;
 Cited by
Byun YH, Park JK, Jung HS. 2002. A comparison of ensemble method between models in prediction of season. J Meteorological Society. 12(1): 1-3. [Korean Literature]

Cantor SB, Sun CC, Tortolero-Luna G, Richard- Kortum R, Follen M. 1999. A comparison of C/B ratios from studies using receiver operating characteristic curve analysis. J Clinical Epidemiology. 52: 885-892. crossref(new window)

Dormann CF. 2007. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecology and Biogeography. 16(2): 129-138. crossref(new window)

Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton J McC, Peterson AT, Phillips SJ, Richardson KS, Scachetti-Pereira R, Schapire RE, Soberon J, Williams S, Wisz MS, Zimmermann NE. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography. 29: 129-151. crossref(new window)

Gibson L, Barrett B, Burbidge A. 2007. Dealing with uncertain absences in habitat modelling: a case study of a rare grounddwelling parrot. Diversity and Distributions. 13(6): 704-713. crossref(new window)

Hyun BK, Jung SJ, Sonn YK, Park CW, Zhang YS, Song KC, Kim LH, Choi EY, Hong SY, Kwon SI, Jang BC. 2010. J Soil Science and Fertilizer. 43(5): 696-704. [Korean Literature]

IPCC. 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Core Writing Team, R.K. Pachauri and L.A. Meyer. Geneva, Switzerland.

Kim HJ, Hong JK, Kim SC, Oh SH, Kim JH. 2011. Plant Phenology of Threatened species for Climate change in Sub-alpine zone of Korea - Especially on the Summit Area of Mt. Deogyusan -. J plant resources. 24(5): 549-556. [Korean Literature] crossref(new window)

Kim YD. 2010. New trend of research in reduction of uncertainty in effect assessment of climate change. J Water Resources Association. 43(9): 32-36. [Korean Literature]

Korea Meteorological Administration. 2012. Catchable Forecasting Technology. [Korean Literature]

Korean Forestry Promotion Institute. 2013a. Manual of Standard Income Process of Non-timber Forest Products. [Korean Literature]

Korean Forestry Promotion Institute. 2013b. Easy Omija. [Korean Literature]

Korean Forest Service. 2008. Specialized Items Technical Dissemination Book(6). Special Tree Cultivation. [Korean Literature]

Korean Forest service. 2012. Standard Forest Products Cultivation Manual for Good Agricultural Practices(GAP). [Korean Literature]

Lee SC, Choi SH, Lee WK, Byun JG. 2011. The Effect of Climate Data Applying Temperature Lapse Rate on Prediction of Potential Forest Distribution. J Society for GeoSpatial Information System. 19(2): 19-27. [Korean Literature]

Lee SH, Jung HC, Choi JY. 2012. Projecting Climate Change Impact on the Potential Distribution of Endemic Plants (Megaleranthis saniculifolia) in Korea. J Society of Environmental Restoration Technology. 15(3): 75-84. [Korean Literature] crossref(new window)

Lee SJ, Ahn YH. 2011. Influences of Global Warming and Succession Possibility through Vertical Distribution of Communities in Ecotone. Wolchulsan National Park. J environmental sciences. 20(12): 1561-1584. [Korean Literature]

Lim SJ, Lee KS, Jung HR, Kim YG, Song MS, Cho JY. 2010. Soil and Environmental Characteristics of Schizandra Chinensis Baillon Habitat Located in Jangsu-gun, Jeollabuk-do. J Soil Science and Fertilizer. 43(6): 771-775. [Korean Literature]

Manel S, Dias JM, Ormerod SJ. 1999. Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecological Modelling. 120: 337-347. crossref(new window)

Ministry of Agriculture. Food and Rural Affairs. 2013. Current State of Production of Special Crops. [Korean Literature]

Ministry of Environment. 2007. National Longterm Research Project of Ecology. [Korean Literature]

Relevant ministries. 2010. National Climate Change Adaptation Measures(2011-2015). [Korean Literature]

Seo CW, Park YR, Choi YS. 2008. Comparison of Species Distribution Models According to Location Data. J Society for GeoSpatial Information System. 16(4): 59-64. [Korean Literature]

Statistic Korea. National statistic portal (

Syfert MM, Smith MJ, Coomes DA. 2013. The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models. PLoS ONE. 8(2): e55158. crossref(new window)

Watling JI, Romaach SS, Bucklin DN, Speroterra C, Brandt LA, Pearlstine LG, Mazzotti FJ. 2012. Do bioclimate variables improve performance of climate envelope models?. Ecological Modelling. 246(10): 79-85. crossref(new window)

Yun JH, Kim JH, Oh KH, Lee BY. 2011. Distributional Change and Climate Condition of Warm-temperate Evergreen Broad-leaved Trees in Korea. J Environ and ecology. 25(1): 47-56. [Korean Literature]