한국환경생태학회지 (Korean Journal of Environment and Ecology)

  • 제34권6호
  • /
  • Pages.589-600
  • /
  • 2020
  • /
  • 1229-3857(pISSN)
  • /
  • 2288-131X(eISSN)
한국환경생태학회 (Korean Society of Environment and Ecology)
권호 표지
DOI QR코드

DOI QR Code

산림생태계 보호구역에서 외래식물 분포 예측을 위한 최적 모형의 선발

Selection of Optimal Models for Predicting the Distribution of Invasive Alien Plants Species (IAPS) in Forest Genetic Resource Reserves

  • 임치홍 (국립생태원 생태자연도연구팀) ;
  • 정성희 (국립수목원 광릉숲보전센터) ;
  • 정수영 (국립수목원 산림생물다양성연구과) ;
  • 김남신 (국립생태원 생태자연도연구팀) ;
  • 조용찬 (국립수목원 광릉숲보전센터)
  • Lim, Chi-hong (Research Team on Ecological and Natural Map, National Institute of Ecology) ;
  • Jung, Song-hie (Gwangneung Forest Conservation Center, Korea National Arboretum) ;
  • Jung, Su-young (Forest Biodiversity Research Division, Korea National Arboretum) ;
  • Kim, Nam-shin (Research Team on Ecological and Natural Map, National Institute of Ecology) ;
  • Cho, Yong-chan (Gwangneung Forest Conservation Center, Korea National Arboretum)
  • 투고 : 2020.10.21
  • 심사 : 2020.12.07
  • 발행 : 2020.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

효과적인 보호구역의 보전 관리를 위해서는 외래종의 정착 모니터링 및 확산 위험에 대한 저감 노력이 수반되어야 한다. 본 연구는 울진에 위치한 산림유전자원보호구역(2,274ha)에서 조사된 외래식물 분포 정보를 대상으로 활용도가 높은 세가지 종분포모형(Bioclim, GLM, MaxEnt)을 활용하여 외래식물의 잠재출현지역을 모의하였고, 모의 결과를 비교하여 지역적 지리 및 생태 관리 특성이 반영된 현실성 및 적합성 높은 종분포모형을 선발하였다. 분석에서 예측된 외래식물의 출현지역은 실제 분포와 같이 도로 같은 선형 경관 요소를 따라 분포하는 경향이었으며, 일부 벌채지가 포함되었다. 본 연구에서 적용한 각 모형의 예측력과 정확도를 통계적으로 비교한 결과, GLM과 MaxEnt 모형은 대체로 높은 예측력과 정확도를 보였지만, Bioclim 모형은 낮았다. Bioclim은 가장 넓은 면적을 출현예상지역으로 계산하였고, GLM, 그리고 MaxEnt 순으로 면적이 작았다. 모의 결과의 현상학적 검토에서는 GLM과 Bioclim 모형은 표본 수에 따라 예측력이 크게 영향을 받는 것으로 나타났고, 표본 수와 관계없이 가장 일관성 높은 모형은 MaxEnt로 평가되었다. 종합적으로, 본 연구에 사용된 모형 중 외래식물 분포 예측을 위한 최적 모형은 MaxEnt 모형인 것으로 판단되었다. 본 연구에서 제시한 정밀 생물종 분포 자료 기반의 모델 선발 접근 방식은 산림생태계 보호구역의 보전 관리 및 지역 특성이 반영된 현실적이고 정교한 모델 발굴 연구에 도움이 될 것이다.

Effective conservation and management of protected areas require monitoring the settlement of invasive alien species and reducing their dispersion capacity. We simulated the potential distribution of invasive alien plant species (IAPS) using three representative species distribution models (Bioclim, GLM, and MaxEnt) based on the IAPS distribution in the forest genetic resource reserve (2,274ha) in Uljin-gun, Korea. We then selected the realistic and suitable species distribution model that reflects the local region and ecological management characteristics based on the simulation results. The simulation predicted the tendency of the IAPS distributed along the linear landscape elements, such as roads, and including some forest harvested area. The statistical comparison of the prediction and accuracy of each model tested in this study showed that the GLM and MaxEnt models generally had high performance and accuracy compared to the Bioclim model. The Bioclim model calculated the largest potential distribution area, followed by GLM and MaxEnt in that order. The Phenomenological review of the simulation results showed that the sample size more significantly affected the GLM and Bioclim models, while the MaxEnt model was the most consistent regardless of the sample size. The optimal model overall for predicting the distribution of IAPS among the three models was the MaxEnt model. The model selection approach based on detailed flora distribution data presented in this study is expected to be useful for efficiently managing the conservation areas and identifying the realistic and precise species distribution model reflecting local characteristics.

키워드

참고문헌

  1. Akaike, H.(1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19: 716-723. https://doi.org/10.1109/TAC.1974.1100705
  2. Bae, K.H., H.J. Cho and S.C. Hong(2003) Vegetation composition and structure at the forest genetic resources reserve, Sokwang-ri, Uljin-gun in Korea. Journal of Korean Forest Society 92: 536-544.
  3. Barnosky, A.D., E.A. Hadly, P. Gonzalez, J. Head, P.D. Polly, A.M. Lawing, J.T. Eronen, D.D. Ackerly, K. Alex and E. Biber(2017) Merging paleobiology with conservation biology to guide the future of terrestrial ecosystems. Science 355(6325). https://doi.org/10.1126/science.aah4787
  4. Benedetti, Y. and F. Morelli(2017) Spatial mismatch analysis among hotspots of alien plant species, road and railway networks in Germany and Austria. PLoS One 12: e0183691. https://doi.org/10.1371/journal.pone.0183691
  5. Benjamin, P. and R. Hiebert(2004) Assessing the invasive plant issue. Park Science 22: 27-31.
  6. Choe, H., J.H. Thorne and C. Seo(2016) Mapping national plant biodiversity patterns in South Korea with the MARS species distribution model. PloS One 11: e0149511. https://doi.org/10.1371/journal.pone.0149511
  7. Crall, A.W., C.S. Jarnevich, B. Panke, N. Young, M. Renz and J. Morisette(2013) Using habitat suitability models to target invasive plant species surveys. Ecological Applications 23: 60-72. https://doi.org/10.1890/12-0465.1
  8. Dar, P., Z. Reshi and M. Shah(2015) Roads act as corridors for the spread of alien plant species in the mountainous regions: A case study of Kashmir valley, India. Tropical Ecology 56: 183-190.
  9. Dix, M.E., M. Buford, J. Slavicek, A.M. Solomon and S.G. Conard(2010) Invasive species and disturbances: Current and future roles of Forest Service research and development. A Dynamic Invasive Species Research Vision: Opportunities and Priorities 2009(29): 91-130.
  10. Elith, J., M. Kearney and S. Phillips(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
  11. Elith, J., S.J. Phillips, T. Hastie, M. Dudik, Y.E. Chee and C.J. Yates(2011) A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17: 43-57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
  12. Guisan, A. and W. Thuiller(2005) Predicting species distribution: Offering more than simple habitat models. Ecology Letters 8: 993-1009. https://doi.org/10.1111/j.1461-0248.2005.00792.x
  13. Hijmans, R. and C. Graham(2006) The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biology 12: 2272-2281. https://doi.org/10.1111/j.1365-2486.2006.01256.x
  14. Hijmans, R. and J. Elith(2013) Species distribution modeling with R Introduction. Encyclopedia of Biodiversity 71.
  15. Holcombe, T.R., T.J. Stohlgren and C.S. Jarnevich(2010) From points to forecasts: predicting invasive species habitat suitability in the near term. Diversity 2: 738-767. https://doi.org/10.3390/d2050738
  16. Joly, M., P. Bertrand, R.Y. Gbangou, M.C. White, J. Dube and C. Lavoie(2011) Paving the way for invasive species: Road type and the spread of common ragweed (Ambrosia artemisiifolia). Environmental Management 48: 514-522. https://doi.org/10.1007/s00267-011-9711-7
  17. Jones, C.C., S.A. Acker and C.B. Halpern(2010) Combining local- and large-scale models to predict the distributions of invasive plant species. Ecological Applications 20: 311-326. https://doi.org/10.1890/08-2261.1
  18. Jung, S.H., C.H. Lim, A.R. Kim, D.M. Woo, H.J. Kwon, Y.C. Cho and C.S. Lee(2017) Edge effects confirmed at the clear-cut area of Korean red pine forest in Uljin, eastern Korea. Journal of Ecology and Environment 41: 1-12. https://doi.org/10.1186/s41610-016-0020-1
  19. Kim, J.S., Y.C. Cho and K.H. Bae(2017) Recovery process of forest edge formed by clear-cutting harvest in Korean red pine (Pinus densiflora) forest in Gangwondo, South Korea. Journal of Korean Forest Society 106: 1-9. https://doi.org/10.14578/JKFS.2017.106.1.1
  20. Korea National Arboretum(2019) Checklist of vascular plants in Korea. www.nature.go.kr/kpni/index.do (in Korean)
  21. Latombe, G., D.M. Richardson, P. Pysek, T. Kucera and C. Hui(2018) Drivers of species turnover vary with species commonness for native and alien plants with different residence times. Ecology 99(12): 2763-2775. https://doi.org/10.1002/ecy.2528
  22. Levine, J.M., M. Vila, C.M.D. Antonio, J.S. Dukes, K. Grigulis and S. Lavorel(2003) Mechanisms underlying the impacts of exotic plant invasions. Proceedings of the Royal Society of London. Series B: Biological Sciences 270: 775-781. https://doi.org/10.1098/rspb.2003.2327
  23. Liu, C., M. White and G. Newell(2013) Selecting thresholds for the prediction of species occurrence with presence-only data. Journal of Biogeography 40: 778-789. https://doi.org/10.1111/jbi.12058
  24. Mack, R.N., D. Simberloff, W. Mark Lonsdale, H. Evans, M. Clout and F.A. Bazzaz(2000) Biotic invasions: Causes, epidemiology, global consequences, and control. Ecological Applications 10: 689-710. https://doi.org/10.1890/1051-0761(2000)010[0689:BICEGC]2.0.CO;2
  25. Meunier, G. and C. Lavoie(2012) Roads as Corridors for Invasive Plant Species: New Evidence from Smooth Bedstraw (Galium mollugo). Invasive Plant Science and Management 5: 92-100. https://doi.org/10.1614/IPSM-D-11-00049.1
  26. Mortensen, D.A., E.S.J. Rauschert, A.N. Nord and B.P. Jones(2009) Forest Roads Facilitate the Spread of Invasive Plants. Invasive Plant Science and Management 2: 191-199. https://doi.org/10.1614/IPSM-08-125.1
  27. Park, J.S., J.H. Yun, J.Y. Choi, J.C. Kim, J. Lee and H.R. Song(2019) Multivariate Associations between Environmental Variables and the Invasion of Alien Plants in Floodplain Waterfront Parklands along the Nakdong River. Journal of Plant Biology 62: 400-409. https://doi.org/10.1007/s12374-019-0222-z
  28. 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
  29. Rew, L.J., B.D. Maxwell, F.L. Dougher and R. Aspinall(2006) Searching for a needle in a haystack: Evaluating survey methods for non-indigenous plant species. Biological Invasions 8: 523-539. https://doi.org/10.1007/s10530-005-6420-2
  30. Richardson, D.M., P.M. Holmes, K.J. Esler, S.M. Galatowitsch, J.C. Stromberg, S.P. Kirkman, P. Pysek and R.J. Hobbs(2007) Riparian vegetation: Degradation, alien plant invasions, and restoration prospects. Diversity and Distributions 13: 126-139. https://doi.org/10.1111/j.1366-9516.2006.00314.x
  31. RStudio Team(2020) RStudio: Integrated Development for R. RStudio, PBC, Boston, MA.
  32. Saumel, I. and I. Kowarik(2010) Urban rivers as dispersal corridors for primarily wind-dispersed invasive tree species. Landscape and Urban Planning 94: 244-249. https://doi.org/10.1016/j.landurbplan.2009.10.009
  33. Wisz, M.S., R. Hijmans, J. Li, A.T. Peterson, C. Graham, A. Guisan and N.P.S.D.W. Group(2008) Effects of sample size on the performance of species distribution models. Diversity and distributions 14: 763-773. https://doi.org/10.1111/j.1472-4642.2008.00482.x
  34. Wolkowycki, D. and P. Banaszuk(2016) Railway routes as corridors for invasive plant species. The case of NE Poland.