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Habitat Prediction and Impact Assessment of Eurya japonica Thunb. under Climate Change in Korea

기후변화에 따른 한반도 사스레피나무의 생육지 예측과 영향 평가

  • Yun, Jong-Hak (Div. of Ecological Assessment, National Institute of Ecology) ;
  • Park, Jeong Soo (Div. of Ecological Assessment, National Institute of Ecology) ;
  • Choi, Jong-Yun (Div. of Ecological Assessment, National Institute of Ecology) ;
  • Nakao, Katsuhiro (Dep. of Plant Ecology, Forestry and Forest Products Research Institute)
  • Received : 2017.05.31
  • Accepted : 2017.09.08
  • Published : 2017.10.31

Abstract

The research was carried out in order to find climate factors which determine the distribution of Eurya japonica, and the potential habitats (PHs) under the current climate and climate change scenario by using species distribution models (SDMs). Four climate factors; the warmth index (WI), the minimum temperature of the coldest month (TMC), summer precipitation (PRS), and winter precipitaion (PRW) : were used as independent variables for the model. Seventeen general circulation models under RCP (Representative concentration pathway) 8.5 scenarios were used as future climate scenarios for the 2050s (2040~2069) and 2080s (2070~2099). Highly accurate SDMs were obtained for E. japonica. The model of distribution for E. japonica constructed by SDMs showed that minimum temperature of the coldest month (TMC) is a major climate factor in determining the distribution of E. japonica. The area above the $-5.7^{\circ}C$ of TMC revealed high occurrence probability of the E. japonica. Future PHs for E. japonica were projected to increase respectively by 2.5 times, 3.4 times of current PHs under 2050s and 2080s. It is expected that the potential of E. japonica habitats is expanded gradually. E. japonica is applicable as indicator species for monitoring in the Korean Peninsula. E. japonica is necessary to be monitored of potential habitats.

본 연구는 사스레피나무의 분포를 규정하는 기후요인과 종분포 모델을 이용하여 현재기후와 미래기후에서의 잠재생육지를 분석하기 위해 수행되었다. 4개 기후요인(온량지수, 최한월최저기온, 하계강수량, 동계강수량)은 모델에서 독립변수로 사용하였다. 17개 전지구 기후모델(GCMs; General Circulation Models)에 의한 RCP(대표농도경로) 8.5 시나리오를 2050년(2040~2069)과 2080년(2070~2099)의 미래기후로 사용하였다. 사스레피나무(Eurya japonica)에 대한 종분포 모델은 높은 분포예측 모델로 구축되었다. 사스레피나무의 분포모델에서 최한월최저기온이 사스레피나무 분포를 규정하는 주요 기후요인으로 분석되었다. 최한월최저기온 $-5.7^{\circ}C$이상 지역은 사스레피나무의 높은 출현확률을 나타내었다. 사스레피나무의 잠재 생육지는 2050년과 2080년에서 현재기후에서 보다 각각 2.5배, 3.4배 증가되었으며, 기후변화에 의해 점점 확대될 것으로 판단되었다. 사스레피나무는 한반도에서 기후변화 지표종으로 가능하며, 잠재 생육지를 모니터링 할 필요가 있다.

Keywords

References

  1. Armonies W, Reise K. 2003. Empty habitat in coastal sediments for populations of macrozoobenthos. Helgoland Mar Research. 56(4): 279-287. https://doi.org/10.1007/s10152-002-0129-8
  2. Clark LA, Pregibon D. 1992. Tree-based models, In: J. M. Chambers and T. J. Hastie, eds., Statistical Models in S, California, Wadsworth & Brooks/Cole Advanced Books & Software. Pacific Grove: p. 377-419.
  3. Berry PM, Dawson TP, Harrison PA, Pearson R, Butt N. 2003. The sensitivity and vulnerability of terrestrial habitats and species in Britain and Ireland to climate change. Journal of Nature Conservation. 11(1): 15-23. https://doi.org/10.1078/1617-1381-00030
  4. Breiman L, Friedman JH, Olshen RA, Stone CJ. 1984. Classification and regression trees. Chapman & Hall/CRC, Boca Raton, FL, US, pp 358.
  5. Hanley J, McNeil B. 1982. The meaning and use of the area under areceiver operating characteristic (ROC) curve. Radiology. 143(1): 29-36. https://doi.org/10.1148/radiology.143.1.7063747
  6. Huntley B, Berry PM, Cramer W, McDonald AP. 1995. Modelling present and potential future ranges of some European higher plants using climate response surfaces. Journal of Biogeography. 22(6): 967-1001. https://doi.org/10.2307/2845830
  7. Horikawa M, Tsuyama I, Matsui T, Kominami Y, Tanaka N. 2009. Assessing the potential impacts of climate change on the alpine habitat suitability of Japanese stone pine (Pinus pumila). Landscape Ecology. 24(1): 115-128. https://doi.org/10.1007/s10980-008-9289-5
  8. IPCC 2014. Climate Change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University.
  9. Iverson LR, Prasad AM. 1998. Predicting abundance of 80 tree species following climate change in the eastern United States. Ecological Monographs. 68(4): 465-485. https://doi.org/10.1890/0012-9615(1998)068[0465:PAOTSF]2.0.CO;2
  10. Kira T. 1977. A Climatological interpretation of Japanese vegetation zones. In Miyawaki, A. and Tuxen, R. (eds.) Vegetation science and environmental protection. Maruzen, Tokyo: p. 21-30.
  11. Kumar P. 2012. Assessment of impact of climate change on Rondodendrons in Sikkim Himalayas using Maxent moddelling: limitations and changes. Biodiversity and Conservation. 21(5): 1251-1266. https://doi.org/10.1007/s10531-012-0279-1
  12. Matsui T, Yagihashi T, Nakaya T, Tanaka N, Taoda H. 2004a. Climatic controls on distribution of Fagus crenata forests in Japan. Journal of Vegetation Science. 15(1): 57-66. https://doi.org/10.1111/j.1654-1103.2004.tb02237.x
  13. Matsui T, Yagihashi T, Nakaya T, Taoda H, Yoshinaga S, Daimaru H, Tanaka N. 2004b. Probability distributions, vulnerability and sensitivity in Fagus crenata forests following predicted climate changes in Japan. Journal of Vegetation Science. 15(5): 605-614. https://doi.org/10.1111/j.1654-1103.2004.tb02302.x
  14. Metz CE. 1978. Basic principles of ROC Analysis, Seminars in Nuclear Medicine. 8(4): 283-298.
  15. Nakao K, Matsui T, Tanaka N. Hukusima T. 2009. Climatic controls of the distribution and abundance of two evergreen Quercus species in Japan. Japanese Journal of Forest Environment. 51(1): 27-37. [Japanese Literature]
  16. Nakao K, Matsui T, Horikawa M, Tsuyama I, Tanaka N. 2011. Assessing the impact of land use and climate change on the evergreen broad-leaved species of Quercus acuta in Japan. Plant Ecology. 212(2): 229-243. https://doi.org/10.1007/s11258-010-9817-7
  17. Nakao K, Higa M, Tsuyama I, Lin CT, Sun ST, Lin JR, Chiou CR, Chen TY, Matsui T. Tanaka N. 2014. Changes in the potential habitats of 10 dominant evergreen broadleaved tree species in the Taiwan-Japan archipelago. Plant Ecology. 215(6): 639-650. https://doi.org/10.1007/s11258-014-0329-8
  18. Normand S, Svenning JC, Skov F. 2007. National and European perspectives on climate change sensitivity of the habitats directive characteristic plant species. Journal for Nature Conservation. 15(1): 41-53. https://doi.org/10.1016/j.jnc.2006.09.001
  19. Ohsawa M. 1990. An interpretation in latitudinal patterns of limits in south and east Asian mountains. Journal of Ecology. 78(2): 326-339. https://doi.org/10.2307/2261115
  20. Ohsawa M. 1991. Structural comparison of tropical mountain rain-forest along latitudinal and altitudinal gradients in south and east-Asia. Vegetatio. 97: 1-10.
  21. Ohsawa M. 1993. Latitudinal pattern of mountain vegetation zonation in southern and eastern Asia. Journal of Vegetation Science. 4(1): 13-18. https://doi.org/10.2307/3235728
  22. Parmesan C, Yohe G. 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature. 421: 37-42. https://doi.org/10.1038/nature01286
  23. R Development Core Team. 2011. R: A language and environment for statistical computing, R. Foundation for Statistical Computing. Vienna, Austria, ISBN 3-900051-07-0, URL://www.R-project.org.
  24. Swets KA. 1988. Measuring the accuracy of diagnostic systems. Science. 240(4857): 1285-1293. https://doi.org/10.1126/science.3287615
  25. Tanaka N. 2007. PRDB (Phytosociological Releve Data Base), Environment change impact team. Forestry and Forest Products Research Institute.
  26. Tanaka N, Nakazono E, Tsuyama I, Matsui T. 2009. Assessing impact of climate warming on potential habitats of ten conifer species in Japan. Global Environmental Research. 14(2): 153-164.
  27. Thuiller W. 2003. BIOMOD-optimizing predictions of species distributions and projecting potential shifts under global change. Global Change Biology. 9(10): 1353-1362. https://doi.org/10.1046/j.1365-2486.2003.00666.x
  28. Thuiller W. Lavorel S, Araujo MB, Sykes MT, Prentice IC. 2005. Climate Change threats to plant diversity in Europe. Proceeding of the National Academy of Sciences of the United States of America. 102(23): 8245-8250. https://doi.org/10.1073/pnas.0409902102
  29. Tsuyama I, Matsui T, Ogawa M, Kominami Y, Tanaka N. 2008. Habitat prediction and impact assessment of climate change on Sasa kurilensis in eastern Honshu, Japan. Theory and Applications of GIS. 16(1): 11-25. [Japanese Literature]
  30. Tsuyama I, Nakao K, Matsui T, Higa M, Horikawa M, Kominami Y, Tanaka N. 2011. Climatic controls of a keystone understory species, Sasamorpha borealis, and an impact assessment of climate change in Japan. Annals of Forest Science. 68(4): 689-699. https://doi.org/10.1007/s13595-011-0086-y
  31. Uyeki H. 1941. On the northern limit of evergreen broad-leaved tree in Korea Acta. Phytotax. Geobot. 10(2): 89-93. [Japanese Literature]
  32. Yun JH, Kim JH, Oh KH, Lee BY. 2011a. Distributional Change and Climate Condition of Warm-temperate Evergreen Broad-leaved Trees in Korea. Korean Journal of Environment and Ecology. 25(1): 47-56. [Korean Literature]
  33. Yun JH, Nakao K, Park CH, Lee BY. 2011b. Potential Habitats and Change Prediction of Machilus thunbergii Siebold & Zucc in Korea by Climate Change. Korean Journal of Environment and Ecology. 25(6): 903-910. [Korean Literature]
  34. Yun JH, Nakao K, Kim JH, Kim SY, Park CH, Lee BY. 2014a. Habitat prediction and impact assessment of Neolitsea sericea (Blume) Koidz. under Climate Change in Assessment. 23(2): 101-111. [Korean Literature]
  35. Yun JH, Nakao K, Tsuyama I, Higa M, Matsui T, Park CH, Lee BY, Tanaka N. 2014b. Does future climate change facilitate expansion of evergreen broad-leaved tree species in the human-disturbed landscape of the Korean Peninsula? Implication for monitoring design of the impact assessment. Japanese Forest Society. 19(1): 174-183.
  36. Zweig MH, Campbell G. 1993. Receiver-operating characteristic (ROC) Plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry. 39(4): 561-577.