DOI QR코드

DOI QR Code

Prediction of Potential Habitat of Japanese evergreen oak (Quercus acuta Thunb.) Considering Dispersal Ability Under Climate Change

분산 능력을 고려한 기후변화에 따른 붉가시나무의 잠재서식지 분포변화 예측연구

  • Received : 2018.02.28
  • Accepted : 2018.05.19
  • Published : 2018.06.30

Abstract

This study was designed to predict potential habitat of Japanese evergreen oak (Quercus acuta Thunb.) in Korean Peninsula considering its dispersal ability under climate change. We used a species distribution model (SDM) based on the current species distribution and climatic variables. To reduce the uncertainty of the SDM, we applied nine single-model algorithms and the pre-evaluation weighted ensemble method. Two representative concentration pathways (RCP 4.5 and 8.5) were used to simulate the distribution of Japanese evergreen oak in 2050 and 2070. The final future potential habitat was determined by considering whether it will be dispersed from the current habitat. The dispersal ability was determined using the Migclim by applying three coefficient values (${\theta}=-0.005$, ${\theta}=-0.001$ and ${\theta}=-0.0005$) to the dispersal-limited function and unlimited case. All the projections revealed potential habitat of Japanese evergreen oak will be increased in Korean Peninsula except the RCP 4.5 in 2050. However, the future potential habitat of Japanese evergreen oak was found to be limited considering the dispersal ability of this species. Therefore, estimation of dispersal ability is required to understand the effect of climate change and habitat distribution of the species.

본 연구는 붉가시나무(Quercus acuta Thunb.)를 대상으로 기후변화의 영향을 평가함에 있어 분산능력을 고려해보고자 하였다. 기후변화에 따른 붉가시나무의 잠재서식지 변화를 예측하기 위하여 종의 분포자료와 기후자료를 활용하여 종분포모형을 개발하였다. 종분포모형은 9개 알고리즘을 True Skill Statistic 평가 값 가중치로 합산하는 앙상블모형을 적용하여 불확실성을 줄이고자 하였다. 미래의 시간적 범위는 2050년과 2070년을 대상으로 하였으며, 기후변화 시나리오는 RCP4.5와 RCP8.5를 선정 하였다. 최종적인 미래 잠재서식지는 현재 적합서식지에서 분산능력에 따라 분산가능한지의 여부를 고려하여 결정하였다. 분산능력은 제한이 없는 경우(Unlimited)와 거리에 따른 분산 확률 함수에 3가지 계수값 (${\theta}=-0.005$, ${\theta}=-0.001$, ${\theta}=-0.0005$)을 적용하여 R 패키지인 Migclim을 사용하여 구현하였다. 2050년 RCP4.5 시나리오에서 계수값이 ${\theta}=-0.005$일 때 붉가시나무의 잠재서식지가 감소하였다. 그 이외의 경우에는 분산능력이 낮은 경우에도 한반도 내의 잠재서식지가 늘어났다. 하지만 분산능력을 고려하였을 경우 붉가시나무의 미래 잠재서식지 확장에는 한계가 분명하게 나타났다. 따라서 기후변화에 따른 미래 잠재서식지 예측에 있어서 분산능력을 고려하는 것이 중요하다고 판단된다.

Keywords

References

  1. Ahn Y, Lee DK, Kim HG, Park C, Kim J, Kim J. 2015. Estimating Korean Pine(Pinus koraiensis) Habitat Distribution Considering Climate Change Uncertainty -Using Species Distribution Models and RCP Scenarios-. J. Korean Env. Res. Tech. 18(3): 51-64. [Korea Literature]
  2. Ahn JB, Choi YW, Jo S, Hong JY. 2014. Projection of 21st Century Climate over Korean Peninsula: Temperature and Precipitation Simulated by WRFV3. 4 Based on RCP4. 5 and 8.5 Scenarios. J. Korean Meteor. Soc. Atmos. 24(4): 541-554. [Korea Literature]
  3. Allouche O, Tsoar A, Kadmon R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of applied ecology 43(6): 1223-1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x
  4. Araújo MB & Guisan A. 2006. Five (or so) challenges for species distribution modelling. Journal of biogeography. 33(10): 1677-1688. https://doi.org/10.1111/j.1365-2699.2006.01584.x
  5. Araujo MB, Whittaker RJ, Ladle RJ, Erhard M. 2005. Reducing uncertainty in projections of extinction risk from climate change. Global ecology and Biogeography. 14(6): 529-538. https://doi.org/10.1111/j.1466-822X.2005.00182.x
  6. Araujo MB, Thuiller W, Pearson RG. 2006. Climate warming and the decline of amphibians and reptiles in Europe. Journal of Biogeography. 33: 1712-1728 https://doi.org/10.1111/j.1365-2699.2006.01482.x
  7. Araujo MB, Pearson RG, Thuiller W, Erhard M. 2005. Validation of species-climate impact models under climate change. Global Change Biol. 11:1504-1513. https://doi.org/10.1111/j.1365-2486.2005.01000.x
  8. Barry S & Elith J. 2006. Error and uncertainty in habitat models. Journal of Applied Ecology. 43(3): 413-423. https://doi.org/10.1111/j.1365-2664.2006.01136.x
  9. Choi TB. 2001. Genetic Structure and Diversity of Three Oak Species (Quercus, subgen. Cyclobalanopsis) in Korea and Conservation Strategy for Q. acuta Thunb. ex Murray. Ph. D. Dissertation, Seoul National University, Seoul, Korea 23pp. 139pp. [Korea Literature]
  10. Choi TY and Park CH. 2004. Korean Groal Potential Habitat Suitability Model at Soraksan National Park Using Fuzzy Set and Multi-Criteria Evaluation. Journal of the Korean Institute of Landscape Architecture. 32(4): 28-38. [Korea Literature]
  11. Crossman ND, Bryan BA, Summers DM. 2012. Identifying priority areas for reducing species vulnerability to climate change. Diversity and Distributions. 18(1): 60-72. https://doi.org/10.1111/j.1472-4642.2011.00851.x
  12. Engler R, Randin CF, Thuiller W, Dullinger S, Zimmermann NE, Araujo MB, Guisan A. 2011. 21st century climate change threatens mountain flora unequally across Europe. Global Change Biology. 17(7): 2330-2341. https://doi.org/10.1111/j.1365-2486.2010.02393.x
  13. Engler R, Hordijk W, Guisan A. 2012. The MIGCLIM R package - seamless integration of dispersal constraints into projections of species distribution models. Ecography. 35(10): 872-878. https://doi.org/10.1111/j.1600-0587.2012.07608.x
  14. Franklin J. 2010. Mapping species distributions: spatial inference and prediction. Cambridge University Press.
  15. Grinnell J. 1904. The origin and distribution of the chest-nut-backed chickadee. The Auk. 21(3): 364-382. https://doi.org/10.2307/4070199
  16. Heikkinen RK, Luoto M, Araujo MB, Virkkala R, Thuiller W, Sykes MT. 2006. Methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography. 30: 751-777. https://doi.org/10.1177/0309133306071957
  17. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 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
  18. IPCC. 2001. Climate Change 2001: Impacts, Adaptation, and Vulnerability: Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, UK.
  19. IPCC. 2007. Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 987 pp.
  20. IPCC. 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. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.
  21. Jaeschke A, Bittner T, Reineking B, Beierkuhnlein C. 2013. Can they keep up with climate change?-Integrating specific dispersal abilities of protected Odonata in species distribution modelling. Insect Conservation and Diversity. 6(1): 93-103. https://doi.org/10.1111/j.1752-4598.2012.00194.x
  22. Kang JH, Suh MS, Kwak CH, Lee YS. 2009. Classification of land cover over the Korean Peninsula using MODIS data. Atmosphere. 19(2): 169-182. [Korea Literature]
  23. Kim WJ, Park CH, Kim WM. 1998. Development of Habitat Suitability Analysis Models for Wild Boar (Sus Scrofa): A Case Study of Mt. Sulak and Mt. Jumbong. Korea Spatial Information Society. 6(3): 247-256. [Korea Literature]
  24. Kim YK, Lee WK, Kim YH, Oh SH, Heo JH. 2012. Changes in Potential Distribution of Pinus rigida Caused by Climate Changes in Korea. Journal of Korean Forestry Society. 101(3): 509-516. [Korea Literature]
  25. Koo KA, Park SU, Kong WS, Hong S, Jang I, Seo C. 2017. Potential climate change effects on tree distributions in the Korean Peninsula: Understanding model & climate uncertainties. Ecological Modelling 353: 17-27. https://doi.org/10.1016/j.ecolmodel.2016.10.007
  26. Koo KA, Kong WS, Nibbelink NP, Hopkinson CS, Lee JH. 2015. Potential effects of climate change on the distribution of cold-tolerant evergreen broadleaved woody plants in the korean peninsula. PloS one. 10(8): e0134043. https://doi.org/10.1371/journal.pone.0134043
  27. Korea National Arboretum. 2004. Distribution maps of vascular plants of Korean peninsula I. South coast province. Korea National Arboretum, Pocheon. [Korea Literature]
  28. Korea National Arboretum. 2005. Distribution maps of vascular plants of Korean peninsula II. South province (Jeollado & Jirisan). Korea National Arboretum, Pocheon. [Korea Literature]
  29. Korea National Arboretum. 2006. Distribution maps of vascular plants of Korean peninsula III. Central & South province (Chungcheong-do). Korea National Arboretum, Pocheon. [Korea Literature]
  30. Korea National Arboretum. 2007. Distribution maps of vascular plants of Korean peninsula IV. Central & south province (Gyeongsangbuk-do). Korea National Arboretum, Pocheon. [Korea Literature]
  31. Korea National Arboretum. 2008. Distribution maps of vascular plants of Korean peninsula V. Central province (Geonggi-do). Korea National Arboretum, Pocheon. [Korea Literature]
  32. Korea National Arboretum. 2009. Distribution maps of vascular plants of Korean peninsula VI. Central province (Gangwon-do). Korea National Arboretum, Pocheon. [Korea Literature]
  33. Korea National Arboretum. 2010a. Distribution maps of vascular plants of Korean peninsula VII. South province (Gyeongsangnam-do) and Ulleung-do province. Korea National Arboretum, Pocheon. [Korea Literature]
  34. Korea National Arboretum. 2010b. Distribution maps of vascular plants of Korean peninsula VIII. Jeju-do province. Korea National Arboretum, Pocheon. [Korea Literature]
  35. Korea National Arboretum. 2011. Distribution maps of vascular plants of Korean peninsula IX. West & South coast province. Korea National Arboretum, Pocheon. [Korea Literature]
  36. Kwon HS. 2011. Integrated Evaluation Model of Biodiversity for Conservation Planning: Focused on Mt. Jiri, Mt. Deokyu and Mt. Gaya regions. Ph.D dissertation, Seoul National University, Seoul, Korea. 18p [Korea Literature]
  37. Kwon HS. 2014. Applying Ensemble Model for Identifying Uncertainty in the Species Distribution Models. Journal of the Korean Society for Geospatial Information System 22(4): 47-52. [Korea Literature]
  38. Kwon HS, Seo CW, Park CH. 2012. Development of Species Distribution Models and Evaluation of Species Richness in Jirisan region. The Korean Society for GeoSpatial Information System. 20(3): 11-18. [Korea Literature]
  39. Kwon HS. 2014. Applying Ensemble Model for Identifying uncertainty in the Species Distribution Models. Journal of the Korean Society for Geospatial Information System. 22(4): 47-52. [Korea Literature]
  40. Lee SG, Jung SG, Park KH, Kim KT, Lee WS. 2010. A Prediction Model and Mapping for Forest-Dwelling Birds Habitat Using GIS. The Korean Association of Geographic information Studies. 13(1): 62-73. [Korea Literature]
  41. Lee JH, Choi BH. 2010. Distribution and northernmost limit on the Korean Peninsula of three evergreen trees. Korean Journal of Plant Taxonomy 40(4): 267-273. [Korea Literature] https://doi.org/10.11110/kjpt.2010.40.4.267
  42. Lee WC. 1996. Standard Illustrations of Korean Plants. Academy Publishing Co., Seoul. 624pp. [Korea Literature]
  43. Lee WC, Yim YJ. 2002. Plant Geography. Kangwon National University Press, Chuncheon. 412pp. [Korea Literature]
  44. Liu C, Berry P, Dawson T, Pearson R. 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28: 385-393. https://doi.org/10.1111/j.0906-7590.2005.03957.x
  45. Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W. 2009. Evaluation of consensus methods in predictive species distribution modelling. Divers Distrib. 15(1): 59-69. https://doi.org/10.1111/j.1472-4642.2008.00491.x
  46. Midgley GF, Hughes GO, Thuiller W, Rebelo AG. 2006. Migration rate limitations on climate change-induced range shifts in Cape Proteaceae. Diversity and Distributions. 12: 555-562 https://doi.org/10.1111/j.1366-9516.2006.00273.x
  47. Miller JR, Turner MG, Smithwick EAH, Dent CL, Stanley EH. 2004. Spatial Extrapolation: The Science of Predicting Ecological Patterns and Processes. BioScience. 54(4): 310-320. https://doi.org/10.1641/0006-3568(2004)054[0310:SETSOP]2.0.CO;2
  48. 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
  49. National Geographic Information Institute. 2016. The national atlas of Korea 2st Edition. Physical Geography, Suwon. 107pp. [Korea Literature]
  50. National Institute for Environmental Research. 2013. The second and third national ecosystem survey: 1997-2012. National Institute of Environmental Research, Incheon, Korea. [Korea Literature]
  51. Ohashi H, Ojashi K, Takahashi H. 2006. Identity of Quercus acuta Thunb. (Fagaceae) recorded from Taiwan and China. J. Jpn. Bot. 81: 173-187 (in Japanese).
  52. Park SU, Koo KA, Kong W-S. 2016a. Potential Impact of Climate Change on Distribution of Warm Temperate Evergreen Broad-leaved Trees in the Korean Peninsula. Journal of the korean Geographical Society 51(2): 1-17. [Korea Literature]
  53. Park SU, Koo KA, Seo C, Kong W-S. 2016b. Potential Impact of Climate Change on Distribution of Hedera rhombea in the Korean Peninsula. Journal of Climate Change Research 7(3): 325-334 [Korea Literature] https://doi.org/10.15531/ksccr.2016.7.3.325
  54. Park SU, Koo KA, Seo C, Hong S. 2017. Climate-related range shifts of Ardisia japonica in the Korean Peninsula: a role of dispersal capacity. Journal of Ecology and Environment, 41:38 [Korea Literature] https://doi.org/10.1186/s41610-017-0055-y
  55. Pearson RG. 2007. Species' distribution modeling for conservation educators and practitioners. Synthesis. American Museum of Natural History, 50.
  56. Peters DPC, Herrick JE, Urban DL, Gardner RH, Breshears DD. 2004. Strategies for ecological extrapolation. Oikos. 106(3): 627-636. https://doi.org/10.1111/j.0030-1299.2004.12869.x
  57. Pompe S, Hanspach J, Badeck F, Klotz S, Thuiller W, Kuhn I. 2008. Climate and land use change impacts on plant distributions in Germany. Biology Letters. 4(5): 564-567. https://doi.org/10.1098/rsbl.2008.0231
  58. Portnoy S, Willson MF. 1993. Seed dispersal curves: behavior of the tail of the distribution. Evolutionary Ecology 7(1): 25-44. https://doi.org/10.1007/BF01237733
  59. Schimper AFW. 1903. Plant-Geography upon a physiological basis. Trasl. WR fisher. Oxford, Clarendon Press.
  60. Seo CW, Choi TY, Choi YS, Kim DY. 2008. A Study on Wildlife Habitat Suitability Modeling for Goral (Nemorhaedus caudatus raddeanus) in Seoraksan National Park, The Korea Society For Environmental Restoration And Revegetation Technology. 11(3): 28-38. [Korea Literature]
  61. Shin MS, Jang RI, Seo CW, Lee MW. 2015. A Comparative Study on Species Richness and Land Suitability Assessment ; Focused on city in Boryeong. J. Environ. Impact Assess. 24(1): 35-50. [Korea Literature] https://doi.org/10.14249/eia.2015.24.1.35
  62. Song W, Kim E. 2012. A Comparison of Machine Learning Species Distribution Methods for Habitat Analysis of the Korea Water Deer (Hydropotes inermis argyropus). Korean Journal of Remote Sensing. 28(1): 171-180. [Korea Literature] https://doi.org/10.7780/kjrs.2012.28.1.171
  63. Thorn JS, Nijman V, Smith D, Nekaris KAI. 2009. Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates:Nycticebus). Diversity and Distributions. 15(2): 289-298. https://doi.org/10.1111/j.1472-4642.2008.00535.x
  64. Thuiller W, Albert C, Araujo MB, Berry PM, Cabeza M, Guisan A, Hickler T, Midgley GF, Paterson J, Schurr FM, Sykes MT, Zimmermann NE, Predicting global change impacts on plant species' distributions: future challenges. Perspectives in plant ecology. evolution and systematics. 9(3-4): 137-152.
  65. Thuiller W, Lafourcade B, Engler R, Arajo MB. 2009. Biomod-a platform for ensemble forecasting of species distributions. Ecography 32(3): 369-373. https://doi.org/10.1111/j.1600-0587.2008.05742.x
  66. Thuiller W. 2003. BIOMOD-optimizing predictions of species distributions and projecting potential future shifts under global change. Global change biology. 9(10): 1353-1362. https://doi.org/10.1046/j.1365-2486.2003.00666.x
  67. Vellend M, Myers JA, Gardescu S, Marks PL. 2003. Dispersal of Trillium seeds by deer: implications for long-distance migration of forest herbs. Ecology. 84(4): 1067-1072. https://doi.org/10.1890/0012-9658(2003)084[1067:DOTSBD]2.0.CO;2
  68. Vittoz P. & Engler R. 2007. Seed dispersal distances: a typology based on dispersal modes and plant traits. Botanica Helvetica. 117(2): 109-124. https://doi.org/10.1007/s00035-007-0797-8
  69. Wenger SJ, Som NA, Dauwalter DC, Isaak DJ, Neville HM, Luce CH, Rieman BE. 2013. Probabilistic accounting of uncertainty in forecasts of species distributions under climate change. Global Change Biology. 19(11): 3343-3354. https://doi.org/10.1111/gcb.12294
  70. Yates CJ, McNeill A, Elith J, Midgley GF. 2010. Assessing the impacts of climate change and land transformation on Banksia in the South West Australian Floristic Region. Diversity and Distributions. 16(1): 187-201. https://doi.org/10.1111/j.1472-4642.2009.00623.x
  71. Yun JH, Katsuhiro N, Park CH, Lee BY, Oh KH. 2011. Change prediction for potential habitats of warm-temperate evergreen broadleaved trees in Korea by climate change. Korean Journal of Environment and Ecology. 25(4): 590-600. [Korea Literature]