Korean Journal of Agricultural Science (농업과학연구)

Institute of Agricultural Science, CNU (충남대학교 농업과학연구소)
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Projection of climate change effects on the potential distribution of Abeliophyllum distichum in Korea

기후변화에 따른 우리나라 미선나무의 분포변화 예측

  • Received : 2011.04.09
  • Accepted : 2011.06.20
  • Published : 2011.06.30
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Abstract

Changes in biota, species distribution range shift and catastrophic climate influence due to recent global warming have been observed during the last century. Since global warming affects various sectors, such as agriculture and vegetation, it is important to predict more accurate impact of future climate change. The purpose of this study is to examine the observed distribution of Abeliophyllum distichum in the Korean peninsula. For this purpose, two period (present and future) climate data were used. Mean data between 1950 and 2000, were used as the present value and the year 2050 and 2080 data from A1B senario in IPCC SRES were used for the future value. Potential habitation is analyzed by MaxEnt(Maximum Entropy model), and Abeliophyllum distichum's coordinates data were used as a dependent variable and independent variables are composed of environmental data such as BioClim, altitude, aspect and slope. The result of six types GCM mean calculation, the potential habitability decreased by 40-60% of the average existing distribution. The methodogies and results of this research can be applicable to the climate changing adaptation stratiegies for the biodiversity conservation.

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References

  1. Ahn SR, Lee YJ, Park GA, Kim SJ. 2007. Analysis of Future Land Use and Climate Change Impact. Journal of Civil Engineering 28(2B): 315-224. [in Korean]
  2. Collins WD, Bitz CM, Blackmon ML, Bonan GB. 2006. The Community Climate System Model Version 3 (CCSM3). Journal of Climate 19(11): 2122-2143. https://doi.org/10.1175/JCLI3761.1
  3. Elith J, Ferrier S, Huettmann F, Leathwick J. 2006. The evaluation strip : A new and robust method for plotting predicted responses from species distribution models. Ecol. Model. 186(3): 280-289.
  4. Gordon HB, Rotstayn LD, McGregor JL, Dix MR, Kowalczyk EA, O'Farrell SP, waterman LJ, Hirst AC, Wilson SG, Collier MA, Watterson IG, Elliott TI. 2002. The CSIRO MK3 Climate System Model. [Electronic publication]. Aspendale: CSIRO Atmospheric Research. (CSIRO Atmospheric Research technical paper, no. 60). 130 pp.
  5. Han WJ, Ahn SE. 2007. Climate Change Impact Assessment and Development of Adaptation Strategies in Korea III. KEI (Korea Enviornment Institute) RE-01. pp. 1-2. [in Korean]
  6. IPCC. 2007. Climate Change: Physical Science Basis. Paris, France. pp. 2-21.
  7. Johns TC, Durman CF, Banks HT, Roberts MJ, McLaren AJ. 2006. The new Hadley centre climate model HadGEM1: Evaluation of coupled simulations. Journal of climate 19(7): 1327-1353. https://doi.org/10.1175/JCLI3712.1
  8. Jungclaus JH, Botzet M, Haak H, Keenlyside N, Luo JJ, Latif M, Marotzke J, Mikolajewics U, Roeckner E. 2005. Ocean circulation and tropical variability in the AOGCM ECHAM5/MPI-OM. Journal of Climate 19(16): 3592-3972.
  9. Jung HC, Jeon SW, Lee DK. 2002. Impact assessment on the forest systems of the Korean peninsula due to the global warming. J. Korean Env. Res. & Reveg. Tech 5(5): 1-10.
  10. Kim MY. 2004. Korean Endemic Plants. Solgwahak Publishing, Korea. [in Korean]
  11. Laster RB. 2008. Plan B 3.0. pp. 103-140.
  12. Marti O, Braconnot P, Bellier J, Benshila R, Bony, S. 2005. The new IPSL climate system model: IPSL-CM4. Institute Pierre Simon Laplace, technical report.
  13. Phillips SJ, Dudik M, Schapire RE. 2004. A maximum entropy approach to species distribution modeling. In Proceedings of the Twenty-First International Conference on Machine Learning. pp. 655-662.
  14. Phillips S, Anderson R, Schapire R. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
  15. Phillps SJ, Dudik M. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31(2): 161-175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
  16. Scinocca JF, McFarlane NA, Lazare M, Li J, Plummer D. 2008. The CCCma third generation AGCM and its extension into the middle atmosphere. Atmospheric Chemistry and Physics 8: 7055-7074. https://doi.org/10.5194/acp-8-7055-2008
  17. Song WK. 2011. Habitat network modeling of Leopard cat (Prionailurus bengalensis) based on the spatial graph theory. Ph.D. dissertation, Seoul National Univ., Seoul, Korea. [in Korean]