Ecology and Resilient Infrastructure

응용생태공학회 (Korean Society of Ecology and Infrastructure Engineering)
권호 표지
DOI QR코드

DOI QR Code

한국에서 동아시아 난대 목본식물의 잠재분포 가능성 평가

Assessment of Potential Distribution Possibility of the Warm-Temperate Woody Plants of East Asia in Korea

  • 이철호 (인하대학교 바이오시스템융합학과 ) ;
  • 김휘래 ((주)수성엔지니어링 ) ;
  • 조강현 (인하대학교 생명과학과 ) ;
  • 최병기 (국립산림과학원 난대.아열대산림연구소) ;
  • 이보라 (국립산림과학원 난대.아열대산림연구소)
  • Cheolho, Lee (Department Biological Sciences and Bioengineering, Inha University) ;
  • Hwirae, Kim (Soosung Engineering Co.) ;
  • Kang-Hyun, Cho (Department of Biological Sciences, Inha University) ;
  • Byeongki, Choi (Warm-Temperate and Subtropical Forest Research Center, National Institute of Forest Science) ;
  • Bora, Lee (Warm-Temperate and Subtropical Forest Research Center, National Institute of Forest Science)
  • 투고 : 2022.12.20
  • 심사 : 2022.12.21
  • 발행 : 2022.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

기후변화에 따라서 식생과 식물종의 분포 변화를 예측하는 것이 생태계 관리에서 중요하다. 본 연구에서는 동아시아의 난대 목본식물종의 한반도 분포 가능성을 체계적으로 평가할 수 있는 방안을 개발하고자 하였다. 먼저 중국과 일본에서는 분포하지만 한국에는 분포하지 않은 난대 목본식물종의 목록을 수집하고 그들의 전지구적 분포와 생물기후 자료를 수집하였다. 또한 한국의 난대식생대를 한랭지수를 이용하여 구분하고 이 지역의 기후 정보를 수집하였다. 기후 변수들 사이의 상관분석으로 다중공선성을 배제하고 분포에 영향을 미치는 기후변수로서 최한사분기 평균기온, 평균온도일교차 및 연강수량이 선택되었다. 동아시아 난대 목본식물종의 분포지와 한국 난대식생대의 3가지 기후 변수 사이의 유사도를 산출하기 위하여 다변량 환경 유사도 표면 (MESS) 분석을 실시하였다. 최종적으로 단계적 변수선택 회귀로 MESS 유사도 지수에 영향을 미치는 주요 기후변수로서 최한사분기 평균기온과 연강수량을 선별하였다. 선택된 2 변수로 구성된 다변량 일차회귀에서 최한사분기 평균기온이 전체 변이의 88%를 차지하였다. 총 319 동아시아 난대 목본식물종에 대하여 MESS 유사도 지수를 산출하는 구축된 다변량 회귀식을 적용하여 이들이 한국에 잠재분포 할 가능성을 평가할 수 있었다.

The prediction of changes regarding the distribution of vegetation and plant species according to climate changes is important for ecosystem management. In this study, we attempted to develop an assessment method to evaluate the possibility of the potential distribution of warm-temperate woody plant species of East Asia in Korea. To begin with, a list of warm-temperate woody plants distributed in China and Japan, but not in Korea, was prepared, and a database consisting their global distribution and bioclimatic variables was constructed. In addition, the warm-temperate vegetation zone in Korea was delineated using the coldness index and relevant bioclimatic data were collected. After the exclusion of multicollinearity among bioclimatic variables using correlation analysis, mean temperature of the coldest quarter, mean temperature diurnal range, and annual precipitation were selected as the major variables that influence the distribution of warm-temperate plants. A multivariate environment similarity surfaces (MESS) analysis was conducted to calculate the similarity scores between the distribution of these three bioclimatic variables in the global distribution sites of the East Asian warm-temperate woody plants and the Korean warm-temperate vegetation zone. Finally, using stepwise variable-selection regression, the mean temperature of the coldest quarter and annual precipitation were selected as the main bioclimatic variables that affect the MESS similarity index. The mean temperature of the coldest quarter accounted for 88% of the total variance. For a total of 319 East Asian warm-temperate woody plant species, the possibility of their potential distribution in Korea was evaluated by applying the constructed multivariate regression model that calculates the MESS similarity index.

키워드

과제정보

본 연구는 국립산림과학원 난대·아열대연구소 '도서 해안지역 탄소흡수원 확충을 위한 맹그로브 적응성 검증 및 조성기반 구축 연구' (과제번호 FE0100-2022-04-2022)에 의해 수행되었습니다.

참고문헌

  1. Aho, K. 2022. asbio: A collection of statistical tools for biologists, R package version 1.8-3. https://CRAN.R-project.org package=asbio. Accessed 4 December
  2. Barbosa, A.M., Real, R., Munoz, A.R., and Brown, J.A. 2013. New measures for assessing model equilibrium and prediction mismatch in species distribution models. Diversity and Distributions 19: 1333-1338. https://doi.org/10.1111/ddi.12100
  3. Cho, K.H. and Lee, S.H. 2015. Prediction of changes in the potential distribution of a waterfront alien plant, Paspalum distichum var. indutum, under climate change in the Korean Peninsula. Ecology and Resilient Infrastructure 2: 206-215. https://doi.org/10.17820/eri.2015.2.3.206
  4. Elith, J. and Leathwick, J.R. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution and Systematics 40: 677-697. https://doi.org/10.1146/annurev.ecolsys.110308.120159
  5. Elith, J., Kearney, M. and Phillips, S. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution 1: 330-342. https://doi.org/10.1111/j.2041-210X.2010.00036.x
  6. GBIF. 2022. Free and open access to biodiversity data. Global Biodiversity Information Facility Secretariat. https://www.gbif.org. Accessed 24 April 2021.
  7. IPCC. 2021. Climate change 2021: The physical science basis. Intergovernmental Panel on Climate Change, Geneva, Switzerland.
  8. Kira, T. 1945. A new classification of climate in eastern Asia as the basis for agricultural geography. Horticultural Institute, Kyoto University, Kyoto, Japan. (In Japanese)
  9. Kira, T. 1991. Forest ecosystems of east and southeast Asia in a global perspective. Ecological Research 6(2): 185-200. https://doi.org/10.1007/BF02347161
  10. KMA. 2021. Korea's 109 climate change analysis report. Korea Meteorological Administration, Seoul, South Korea. (in Korean)
  11. Mesgaran, M.B., Cousens, R.D. and Webber, B.L. 2014. Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. Diversity and Distributions 20: 1147-1159. https://doi.org/10.1111/ddi.12209
  12. Negrini, M., Fidelis, E.G., Picanco, M.C. and Ramos, R.S. 2020. Mapping of the Steneotarsonemus spinki invasion risk in suitable areas for rice (Oryza sativa) cultivation using MaxEnt. Experimental and Applied Acarology 80: 445-461. https://doi.org/10.1007/s10493-020-00474-6
  13. NIFS. 2012. Impact assessment and monitoring for terrestrial ecosystem to climate change on Juju Island. National Institute of Forest Science, Seoul, South Korea. (In Korean)
  14. NIFS. 2021. Analysis of potential distribution area of subtropical forest life resources in response to climate change. National Institute of Forest Science, Seoul, South Korea. (in Korean)
  15. NIFS. 2022. Analysis and prediction of potential growth sites of Mangroves in Korea. National Institute of Forest Science, Seoul, South Korea. (in Korean)
  16. Oksanen, J., Simpson, G., Blanchet, F., Kindt, R., Legendre, P., Minchin, P., ... and Weedon, J. 2022. Vegan: Community ecology package, R package, version 2.6-2. https://CRAN.R-project.org/package=vegan.
  17. Owens, H.L., Campbell, L.P., Dornak, L.L., Saupe, E.E., Barve, N., Soberon, J., Ingenloff, K., Lira-Noriega, A., Hensz, C.M., Myers, C.E., and Peterson, A.T. 2013. Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecological Modelling 263: 10-18. https://doi.org/10.1016/j.ecolmodel.2013.04.011
  18. Padalia, H., Srivastava, V., and Kushwaha, S.P.S. 2014. Modeling potential invasion range of alien invasive species, Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP. Ecological Informatics 22: 36-43. https://doi.org/10.1016/j.ecoinf.2014.04.002
  19. QGIS.org. 2022. QGIS Geographic Information System. QGIS Association. http://www.qgis.org. Accessed 20 June 2022.
  20. R Core Team. 2022. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  21. Santini, L., Benitez-Lopez, A., Maiorano, L., Cengic, M. and Huijbregts, M.A. 2021. Assessing the reliability of species distribution projections in climate change research. Diversity and Distributions 27: 1035-1050. https://doi.org/10.1111/ddi.13252
  22. Wang, H., Harrison, S.P., Prentice, I.C., Yang, Y., Bai, F., Togashi, H.F., Wang, M., Zhou, S., and Ni, J. 2018. The China plant trait database: Toward a comprehensive regional compilation of functional traits for land plants. Ecology 99: 500.
  23. WFO. 2022. An online flora of all known plants. World Flora Online. http://www.worldfloraonline.org. Accessed 4 April 2022.
  24. WorldClim. 2022. Global climate data. WorldClim. https://www.worldclim.org. Accessed 1 November 2022.
  25. WWF. 2015. Impact of climate change on species. World Wide Fund for Nature, Geneva, Switzerland.
  26. Yim, Y.J. and Kira, T. 1975. Distribution of forest vegetation and climate in the Korean Peninsula. I. Distribution of some indices of thermal climate. Japanese Journal of Ecology 25(2): 77-88.