DOI QR코드

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불확실성을 고려한 미래 잣나무의 서식 적지 분포 예측 - 종 분포 모형과 RCP시나리오를 중심으로 -

Estimating Korean Pine(Pinus koraiensis) Habitat Distribution Considering Climate Change Uncertainty - Using Species Distribution Models and RCP Scenarios -

  • Ahn, Yoonjung (Korea Environment Institute) ;
  • Lee, Dong-Kun (Department of Landscape Architecture and Rural System Engineering, Seoul National University) ;
  • Kim, Ho Gul (Graduate school, Seoul National University) ;
  • Park, Chan (Korea Research Institute for Human Settlements) ;
  • Kim, Jiyeon (Graduate school, Seoul National University) ;
  • Kim, Jae-uk (Korea Environment Institute)
  • 투고 : 2015.04.13
  • 심사 : 2015.06.19
  • 발행 : 2015.06.30

초록

Climate change will make significant impact on species distribution in forest. Pinus koraiensis which is commonly called as Korean Pine is normally distributed in frigid zones. Climate change which causes severe heat could affect distribution of Korean pine. Therefore, this study predicted the distribution of Korean Pine and the suitable habitat area with consideration on uncertainty by applying climate change scenarios on an ensemble model. First of all, a site index was considered when selecting present and absent points and a stratified method was used to select the points. Secondly, environmental and climate variables were chosen by literature review and then confirmed with experts. Those variables were used as input data of BIOMOD2. Thirdly, the present distribution model was made. The result was validated with ROC. Lastly, RCP scenarios were applied on the models to create the future distribution model. As a results, each individual model shows quite big differences in the results but generally most models and ensemble models estimated that the suitable habitat area would be decreased in midterm future(40s) as well as long term future(90s).

과제정보

연구 과제번호 : 불확도를 고려한 기후변화 영향 및 적응 경제성 평가 기술개발, 도시생태계 적응.관리기법 및 지원시스템 개발

연구 과제 주관 기관 : 환경부

참고문헌

  1. Arau'jo, M. B.․Robert, J. W.․Richard, J. L. and Markus, E. 2005a. Reducing uncertainty in projections of extinction risk from climate change. Global Ecol. Biogeogr. 14: pp. 529-538. https://doi.org/10.1111/j.1466-822X.2005.00182.x
  2. Choi, Jaeyong․Lee, Peter Sang-Hoon and Lee, Sanghyuk. 2015. Anticipation of the Future Suitable Cultivation Areas for Korean Pines in Korean Peninsula with Climate Change. ournal of the Korea Society of Environmental Restoration Technology, 18(1), pp. 103-113. https://doi.org/10.13087/kosert.2015.18.1.103
  3. Elith, J. and Graham, C. H. 2009. Do they? How do they? WHY do they differ? on finding reasons for differing performances of species distribution models. Ecography, 32(December 2008), pp. 66-77. https://doi.org/10.1111/j.1600-0587.2008.05505.x
  4. Elith, J.․Graham, C. H.․Anderson, R. P.․ Dudik, M.․Ferrier, S.․Guisan, A.․Hijmans, R. J.․Huettmann, F.․Leathwick, J. R.․ Lehmann, A.․Li, J.․Lohmann, L. G.․ Loiselle, B. A.․Manion, G.․Moritz, C.․ Nakamura, M.․Nakazawa, Y.․Overton, J. M.․Peterson, A. T.․Phillips, S. J.․ Richardson, K.․Scachetti-Pereira, R.․ Schapire, R. E.․Soberon, J.․Williams, S.․ Wisz, M. S. and Zimmermann, N. E. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29, pp. 129-151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
  5. Fortin, M. and Dale, M. R. T. 2014. Spatial Analysis a guide for ecologist, Cambridge University Press.
  6. Franklin, J. 2006. Mapping species distributions:spatial inference and prediction, Cambridge University Press.
  7. Green, R. H. 1979. Sampling design and statistical methods for environmental biologists, John Wiley and Sons.
  8. Hastie, T.․Tibshirani, R. and Andreas, B. 1995. Flexible discriminant and mixture models. Neural networks and statistics, pp. 1-23.
  9. IPCC, 2014. Working Group II Chapter 17. pp. 14-15.
  10. Jaeschke, A.․Torsten, B.․Bjorn, R. and Carl, B. 2013. Can they keep up with climate change? - Integrating specific dispersal abilities of protected Odonata in species distribution modelling. Insect Conservation and Diversity, 6(1), pp. 93-103. https://doi.org/10.1111/j.1752-4598.2012.00194.x
  11. Jose' Alexandre F. Diniz-Filho․Luis Mauricio Bini․Thiago Fernando Rangel․Rafael D. Loyola․Christian Hof, D. N. and M. B. A. 2009. Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography, 32(6), pp. 897-906. https://doi.org/10.1111/j.1600-0587.2009.06196.x
  12. Kim, JI Yeon․Seo, Chang Wan․Kwon, Hyuk Soo․Ryu, Ji Eun and Kim, Myung Jiin. 2012. A Study on the Species Distribution Modeling using National Ecosystem Survey Data. Korean Society of Environmental Impact Assessment 21(4), pp. 593-607.
  13. Kim, Yong-Kyung․Lee, Woo-Kyung․Park, Young-hwan․Oh, Suhyun and Heo, Jun-Hyeok. 2012. Changes in Potential Distribution of Pinus rigida Caused by Climate Changes in Korea. Journal of Korean Forest Society, 101(3). pp. 509-516.
  14. Kwon, Hyuk Soo․Seo, Chang Wan and Park, Chong Hwa. 2012. Development of Species Distribution Models and Evaluation of Species Richness in Jirisan region. Journal of the Korean Society for Geo-Spatial Information System 20(3), pp. 11-18.
  15. Lee, Sangtae․Bae, Sang-Won․Jang, Seok Chang․ Hwang, Jae Hong․Chung, Jungmo and Kim, Hyun-Seop. 2009. A Study on the Relationship Between Radial Growth and Climate Factors by Regions in Korean Pine (Pinus koraiensis). Journal of Korean Forest Society, 98(6), pp. 733-739.
  16. Lee, Yong Seok․Sung, Joo Han․Chun, Jung Hwa․Shin, Man Yong. 2012. Development of Site Index Equations and Assessment of Productive Areas Based on Environmental Foctors for Major Coniferous Tree Species. Journal of Korean Forest Society, 101(3), pp. 395-404.
  17. Miller, J. a. 2014. Virtual species distribution models: Using simulated data to evaluate aspects of model performance. Progress in Physical Geography, 38(1), pp. 117-128. https://doi.org/10.1177/0309133314521448
  18. Muraoka, H.․Saitoh, T. M. and Nagai, S. 2015. Long-term and interdisciplinary research on forest ecosystem functions: challenges at Takayama site since 1993 Takayama chronicle, pp. 197-200.
  19. Pearson, R. G.․Thuiller, W.․Araujo, M. B.․ Martinez-Meyer, E. B.․Lluls M.․Colin M.․Lera S.․Pedro D.․Terence P. and Lees, D. C. 2006. Model-based uncertainty in species range prediction. Journal of Biogeography, 33, pp. 1704-1711. https://doi.org/10.1111/j.1365-2699.2006.01460.x
  20. Phillips, S. J.․Dudik, M.․Elith, J.․Graham, C. H.․Lehmann, A.․Leathwick, J. F. and Simon. 2009. Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecological Applications, 19(1), pp. 181-197. https://doi.org/10.1890/07-2153.1
  21. Spittlehouse, D. L. and Stewart, R. B. 2003. Adaptation to climate change in forest management. J. Ecosyst. Mangment. 4, pp. 1-11.
  22. Thuiller, W. 2003. BIOMOD - optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biology, 9(10), pp. 1353-1362. https://doi.org/10.1046/j.1365-2486.2003.00666.x
  23. Thuiller, W.․Lafourcade, B.․Engler, R.․ Araujo, M. B. 2009. BIOMOD - a platform for ensemble forecasting of species distributions. Ecography, 32(3), pp. 369-373. https://doi.org/10.1111/j.1600-0587.2008.05742.x
  24. Thuiller, W.․Miguel B. Araujo․Richard G. Pearson․Robert J. W.․Lluis B. and Sandra L., 2004. Biodiversity conservation: uncertainty in predictions of extinction risk. Nature 427, pp. 145-148. https://doi.org/10.1038/nature02121