생태와환경 (Korean Journal of Ecology and Environment)

  • 제48권4호
  • /
  • Pages.229-237
  • /
  • 2015
  • /
  • 2288-1115(pISSN)
  • /
  • 2288-1123(eISSN)
한국수생태학회 (The Korean Society of Limnology)
권호 표지
DOI QR코드

DOI QR Code

기후변화에 따른 소나무림 분포변화 예측모델

Prediction Model of Pine Forests' Distribution Change according to Climate Change

  • 김태근 (국립공원관리공단 보전정책부) ;
  • 조영호 (경북대학교 계통진화유전체학연구소) ;
  • 오장근 (국립공원관리공단 보전정책부)
  • 투고 : 2015.09.22
  • 심사 : 2015.11.13
  • 발행 : 2015.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구는 보다 정확한 소나무림의 분포현황을 이용하여 효과적으로 소나무림을 보전관리하기 위한 기초자료를 제공하는 데 목적이 있다. 따라서 본 논문은 기후변화에 의한 남한지역에 서식하고 있는 소나무림의 지리적 분포 변화를 예측하고 연령대별 소나무림의 공간적 분포 특성을 평가하고자 한다. 이를 위해서 종 분포 변화를 예측하는데 유용한 MaxEnt 모델을 현재와 미래 시기의 기후변화 시나리오 자료에 적용하여 소나무림의 잠재적인 분포 변화를 예측하고, 연령대별 분포면적과 변화에 미치는 생물 기후 변수의 영향을 분석하였다. 소나무림의 잠재적 분포지역은 남한지역에서 10~30년생의 소나무림이 상대적으로 많이 감소하는 것으로 나타났으며, 연령대별 소나무림에게 기후적으로 적합한 지역의 면적이 클수록 감소 지역은 커지고, 새롭게 확장되는 지역은 작아지는 경향으로 나타났다. 이는 서식 적합한 지역의 대부분이 중복되는 지역으로서 유사한 기후환경에서 소나무림의 연령대별 상호작용이 상호 촉진 관계에서 경쟁적인 관계로 변하는 데 기인할 것으로 추측된다. 기온변화보다 강수량이 소나무림의 분포에 더 큰 영향을 주고, 소나무의 지리적인 분포 변화는 평균적인 기후 특성보다 건조한 시기의 강수량 및 최고 한기의 기온과 같이 기후의 극한성에 영향을 더 받는 것으로 나타났다. 특히 최고 건기의 강수량에 의한 소나무림의 분포 변화에 대한 영향은 연령대와 상관없이 나타났다. 이러한 결과는 향후 기온 상승에 따른 수분결핍이 증가하여 결국 생장과 생리반응에 영향을 주는 가뭄과 연관된 기후환경이 조성되어 소나무림의 감소를 초래할 것으로 예상된다. 본 연구에서 유도된 결과는 기존에 구축된 다양한 생물 자원 정보를 활용하여 기후변화에 따른 지리적인 변화를 예측하는 데 유용한 방법으로 적용될 수 있고, 자연생태계 분야에서 산림식생보전과 관련된 기후변화 적응정책 수립에 기초자료로 활용될 것으로 기대된다.

This study aims to offer basic data to effectively preserve and manage pine forests using more precise pine forests' distribution status. In this regard, this study predicts the geographical distribution change of pine forests growing in South Korea, due to climate change, and evaluates the spatial distribution characteristics of pine forests by age. To this end, this study predicts the potential distribution change of pine forests by applying the MaxEnt model useful for species distribution change to the present and future climate change scenarios, and analyzes the effects of bioclimatic variables on the distribution area and change by age. Concerning the potential distribution regions of pine forests, the pine forests, aged 10 to 30 years in South Korea, relatively decreased more. As the area of the region suitable for pine forest by age was bigger, the decreased regions tend to become bigger, and the expanded regions tend to become smaller. Such phenomena is conjectured to be derived from changing of the interaction of pine forests by age from mutual promotional relations to competitive relations in the similar climate environment, while the regions suitable for pine forests' growth are mostly overlap regions. This study has found that precipitation affects more on the distribution of pine forests, compared to temperature change, and that pine trees' geographical distribution change is more affected by climate's extremities including precipitation of driest season and temperature of the coldest season than average climate characteristics. Especially, the effects of precipitation during the driest season on the distribution change of pine forests are irrelevant of pine forest's age class. Such results are expected to result in a reduction of the pine forest as the regions with the increase of moisture deficiency, where climate environment influencing growth and physiological responses related with drought is shaped, gradually increase according to future temperature rise. The findings in this study can be applied as a useful method for the prediction of geographical change according to climate change by using various biological resources information already accumulated. In addition, those findings are expected to be utilized as basic data for the establishment of climate change adaptation policies related to forest vegetation preservation in the natural ecosystem field.

키워드

참고문헌

  1. Bae, B.H. and H.J. Lee. 1999. Phytosociological Studies for Vegetation Conservation of Pine Forest. The Korean Journal of Ecology 22(1): 21-29.
  2. Boldwin, R.A. 2009. Use of maximum entropy modeling in wildlife research. Entropy 11: 854-866. https://doi.org/10.3390/e11040854
  3. Brooker, R.W. 2006. Plant-plant interactions and environmental change. New Phytologist 171: 271-284. https://doi.org/10.1111/j.1469-8137.2006.01752.x
  4. Chardon, N.I., W.K. Cornwell, L.E. Flint, A.L. Flint and D.D. Ackerly. 2014. Topographic, latitudinal and climatic distribution of Pinus coulteri: geographic range limits are not at the edge of the climate envelope. Ecography 37: 1-12. https://doi.org/10.1111/j.1600-0587.2013.00236.x
  5. Chun, J.H. and C.B. Lee. 2013. Assessing the Effects of Climate Change on the Geographic Distribution of Pinus densiflora in Korea using Ecological Niche Model. Korean Journal of Agricultural and Forest Meteorology 15(4): 219-233. https://doi.org/10.5532/KJAFM.2013.15.4.219
  6. Elith, J., C.H. Graham, R.P. Anderson, M. Dudik, S. Ferrier, A. Guisan, R.J. Hijmans, F. Huettmann, J.R. Leathwick, A. Lehmann, J. Li, L.G. Lohmann, B.A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J.M. Overton, A.T. Peterson, S.J. Phillips, K. Richardson, R. Scachetti-Pereira, R.E. Schapire, J. Soberon, S. Williams, M.S. Wisz and N.E. Zimmermann. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129-151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
  7. ESRI. 2011. ArcGIS Desktop: Release 10. Environmental Systems Research Institute (ESRI), Redlands, CA.
  8. Fielding, A.H. and J.F. Bell. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24: 38-49. https://doi.org/10.1017/S0376892997000088
  9. Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. https://doi.org/10.1002/joc.1276
  10. IPCC (Intergovernmental Panel on Climate Change). 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, P.M. Midgley, eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  11. IPCC. 2014: Climate Change 2014: Synthesis Report. Contribution of Working Group I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Core Writign Team, R.K. Rachauri and L.A. Meyer, eds.). IPCC, Geneva, Switzerland. 151p.
  12. Kim, G.N., S.H. Han and G.S. Park. 2014. Differences on Growth, Photosynthesis and Pigment Contents of Openpollinated Pinus densiflora Families Under Elevated Temperature and Drought. Korean Journal of Agricultural and Forest Meteorology 16(4): 2288-1859. https://doi.org/10.5532/KJAFM.2014.16.4.285
  13. Korea Forest Service. 2014. Statistical annual report of Forestry sector. 496p.
  14. Kwon, J.O. 2003. A study on the application of the ecological evaluation for the nature-friendly residential site development planning. PhD Dissertation Seoul University, Seoul, Korea (Korean with English Abstract).
  15. Lee, C.S., J.H. Kim, H. Yi and Y.H. You. 2004. Seedling establishment and regeneration of Korean red pine (Pinus densiflora S. et Z.) forests in Korea in relation to soil moisture. Forest Ecology and Management 199: 423-432. https://doi.org/10.1016/j.foreco.2004.05.053
  16. Lee, K.J., J.W. Choi, W.K. Choi and B.H. Han. 2006. Ecological Characteristics and Change for Fifteen Years (1989-2004) of Plant Community Structure of the pinus densiflora S. et Z. Forest in Hongrudong Valley, Gayasan National Park. Korean Journal of Environment and Ecology 20(2): 188-199.
  17. Lee, S.D., K.J. Lee and J.W. Choi. 2009. Management Plan to Consider Ecological Characteristic of Pinus densiflora Community in Seoul. Korean Journal of Environment and Ecology 23(3): 258-271.
  18. Lee, S.M., Y.J. Chung, Y.S. Moon, S.G. Lee, D.W. Lee, H.Y. Choo and C.K. Lee. 2003. Insecticidal Activity and Fumigatin Conditions of Several Insecticides against Japanese Pine Sawyer (Monochamus alternatus) Larvae. Journal of Korean Forest Society 92(3): 191-198.
  19. Mark, C. Urban. 2015. Accelerating extinction risk from climate change. Science 348: 571-573. https://doi.org/10.1126/science.aaa4984
  20. Phillips, S.J., R.P. Anderson and R.E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
  21. R Core Team. 2015. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
  22. Seo, D.J., C.Y. Oh, K.S. Woo and J.C. Lee. 2013. A Study on Ecological Niche of Pinus densiflora Forests according to the Environmental Factors. Korean Journal of Agricultural and Forest Meteorology 15(3): 153-160. https://doi.org/10.5532/KJAFM.2013.15.3.153
  23. Shin, H.J., G.A. Park, M.J. Park and S.J. Kim. 2012. Projection of Forest Vegetation Change by Applying Future Climate Change Scenario MIROC3.2 A1B. Journal of the Korean Association of Geographic Information Studies 15(1): 64-75. https://doi.org/10.11108/kagis.2012.15.1.064
  24. Swets. 1988. Measuring the accuracy of diagnositic systems. Science 240: 1285-1293. https://doi.org/10.1126/science.3287615
  25. Williams, J.N., C.W. Seo, J. Thorne, J.K. Nelson, S. Erwin, J.M. O'Brien and M.W. Schwartz. 2009. Using species distribution models to predict new occurrences for rare plants. Diversity and Distributions 15(4): 565-576. https://doi.org/10.1111/j.1472-4642.2009.00567.x
  26. WorldClim. 2005. WorldClim - Global Climate Data, http://www.worldclim.org, accessed on: 20th August 2009.