Prediction of Acer pictum subsp. mono Distribution using Bioclimatic Predictor Based on SSP Scenario Detailed Data

Whee-Moon Kim; Chaeyoung Kim; Jaepil Cho; Jina Hur; Wonkyong Song

doi:10.17820/eri.2022.9.3.163

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2022 Vol.9, Issue 3 Preview Page Next Page

Original Article

Prediction of Acer pictum subsp. mono Distribution using Bioclimatic Predictor Based on SSP Scenario Detailed Data SSP 시나리오 상세화 자료 기반 생태기후지수를 활용한 고로쇠나무 분포 예측

30 September 2022. pp. 163-173

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Abstract

Climate change is a key factor that greatly influences changes in the biological seasons and geographical distribution of species. In the ecological field, the BioClimatic predictor (BioClim), which is most related to the physiological characteristics of organisms, is used for vulnerability assessment. However, BioClim values are not provided other than the future period climate average values for each GCM for the Shared Socio-economic Pathways (SSPs) scenario. In this study, BioClim data suitable for domestic conditions was produced using 1 km resolution SSPs scenario detailed data produced by Rural Development Administration, and based on the data, a species distribution model was applied to mainly grow in southern, Gyeongsangbuk-do, Gangwon-do and humid regions. Appropriate habitat distributions were predicted every 30 years for the base years (1981 - 2010) and future years (2011 - 2100) of the Acer pictum subsp. mono. Acer pictum subsp. mono appearance data were collected from a total of 819 points through the national natural environment survey data. In order to improve the performance of the MaxEnt model, the parameters of the model (LQH-1.5) were optimized, and 7 detailed biolicm indices and 5 topographical indices were applied to the MaxEnt model. Drainage, Annual Precipitation (Bio12), and Slope significantly contributed to the distribution of Acer pictum subsp. mono in Korea. As a result of reflecting the growth characteristics that favor moist and fertile soil, the influence of climatic factors was not significant. Accordingly, in the base year, the suitable habitat for a high level of Acer pictum subsp. mono is 3.41% of the area of Korea, and in the near future (2011 - 2040) and far future (2071 - 2100), SSP1-2.6 accounts for 0.01% and 0.02%, gradually decreasing. However, in SSP5-8.5, it was 0.01% and 0.72%, respectively, showing a tendency to decrease in the near future compared to the base year, but to gradually increase toward the far future. This study confirms the future distribution of vegetation that is more easily adapted to climate change, and has significance as a basic study that can be used for future forest restoration of climate change-adapted species.

Keywords

AR6

BioClim

Climate change

CMIP6

MaxEnt

SSPs

기후변화는 종의 생물계절 및 지리적 분포 변화에 많은 영향을 미치는 핵심 요인으로 생태 분야에서는 취약성 평가를 위해 생물의 생리적 특성과 가장 관련이 높은 생태기후지수 (BioClimatic predictor, 이하 BioClim)를 사용하고 있다. 그러나, Shared Socio-economic Pathways (SSPs) 시나리오에 대한 GCM별 미래 기간 기후평균값 이외에 BioClim 값들은 제공되지 않고 있다. 본 연구는 농촌진흥청에서 생산한 1 km 해상도의 SSPs 시나리오 상세화 자료를 이용하여 국내 여건에 적합한 BioClim 자료를 생산하고, 해당 자료를 기반으로 종 분포모형을 적용하여 주로 남부 및 경상북도, 강원도 및 습한 지역에서 생육 환경이 적합한 고로쇠나무의 기준년대 (1981 - 2010년) 및 미래년도 (2011 - 2100년)에 대해 30년 단위로 적합 서식지 분포를 예측했다. 전국자연환경조사자료를 통해 총 819개 지점에서 고로쇠나무 출현 자료를 수집했다. MaxEnt 모형의 성능을 높이기 위해 모형의 매개 변수 (LQH-1.5)를 최적화하고 상세화된 Biolicm 7개 지수와 지형지수 5개를 MaxEnt 모델에 적용했다. 국내 고로쇠나무 분포는 배수, 연 강수량 (Bio12), 경사가 크게 기여하는 것으로 나타났다. 적습하고 비옥한 토양을 선호하는 생육 특성이 반영된 결과로 기후 요인의 영향은 크지 않았다. 이에 따라 기준년도에 고로쇠나무의 높은 수준 적합 서식지는 우리나라 면적의 3.41%, 근미래 (2011 - 2040년) 및 먼미래 (2071 - 2100년)에서 SSP1-2.6은 0.01%, 0.02%를 차지하여 점차 감소하였으나, SSP5-8.5에서는 각각 0.01%, 0.72%로 오히려 기준년도 대비 근미래에는 감소되다가 먼미래로 갈수록 점차 증가하는 경향을 보였다. 본 연구는 기후변화에 보다 적응이 수월한 식생의 미래 분포 양상을 확인한 연구로 기후변화 적응 종이 미래 산림 복원 등에 활용 가능한 기초 연구로 의의가 있다.

키워드

AR6

BioClim

CMIP6

기후변화

MaxEnt

SSPs

References

Ab Lah, N. Z., Yusop, Z., Hashim, M., Mohd Salim, J., and Numata, S. 2021. Predicting the Habitat Suitability of Melaleuca cajuputi Based on the MaxEnt Species Distribution Model. Forests 12(11): 1449. 10.3390/f12111449

Allen, J.L. and Lendemer, J.C. 2016. Climate change impacts on endemic, high-elevation lichens in a biodiversity hotspot. Biodiversity and Conservation 25(3): 555-568. 10.1007/s10531-016-1071-4

Arguez, A. and Vose, R.S. 2011. The definition of the standard WMO climate normal: The key to deriving alternative climate normals. Bulletin of the American Meteorological Society 92(6): 699-704. 10.1175/2010BAMS2955.1

Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J., ... and Zickfeld, K. 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group14 I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Technical Summary.

Bailing, M. and Jian, S. 2018. Predicting the distribution of Stipa purpurea across the Tibetan Plateau via the MaxEnt model. BMC Ecology 18(1): 1-12. 10.1186/s12898-018-0165-029466976PMC5822641

Barbet-Massin, M., Jiguet, F., Albert, C.H., and Thuiller, W. 2012. Selecting pseudo‐absences for species distribution models: how, where and how many?. Methods in ecology and evolution 3(2): 327-338. 10.1111/j.2041-210X.2011.00172.x

Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.A., Meurdesoif, Y., Cadule, P., Devilliers, M., Dupont, E., and Lurton, T. 2019. IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1532 .

Byun, Y.H., Lim, Y.J., Shim, S., Sung, H.M., Sun, M., Kim, J., ... and Moon, H. 2019. NIMS-KMA KACE1. 0-G model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation.

Dix, M., Bi, D., Dobrohotoff, P., Fiedler, R., Harman, I., Law, R., Mackallah, C., Marsland, S., O'Farrell, S., Rashid, H., Srbinovsky, J., Sullivan, A., Trenham, C., Vohralik, P., Watterson, I., Williams, G., Woodhouse, M., Bodman, R., Dias, F. B., … and Yang, R. 2019. CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation. 10.22033/ESGF/CMIP6.2285.

EC-Earth Consortium (EC-Earth). 2019. EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251 .

Elith, J., Graham, H.C., Anderson, P.R., Dudík, M., Ferrier, S., Guisan, A., ... and Zimmermann, E.N. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29(2): 129-151. 10.1111/j.2006.0906-7590.04596.x

Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y. E., and Yates, C.J. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and distributions 17(1): 43-57. 10.1111/j.1472-4642.2010.00725.x

Eum, H.I., Kim, J.P., and Cho, J. 2018. High-resolution climate data from an improved GIS-based regression technique for South Korea. KSCE Journal of Civil Engineering 22(12): 5215-5228. (in Korean) 10.1007/s12205-017-1441-9

Fitzpatrick, M.C., Gove, A.D., Sanders, N.J., and Dunn, R.R. 2008. Climate change, plant migration, and range collapse in a global biodiversity hotspot: the Banksia (Proteaceae) of Western Australia. Global Change Biology 14(6): 1337-1352. 10.1111/j.1365-2486.2008.01559.x

Good, P., Sellar, A., Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Kuhlbrodt, T., and Walton, J. 2019. MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation. 10.22033/ESGF/CMIP6.1567.

Guisan, A. and Thuiller, W. 2005. Predicting species distribution: offering more than simple habitat models. Ecology letters 8(9): 993-1009. 10.1111/j.1461-0248.2005.00792.x34517687

Guisan, A., Lehmann, A., Ferrier, S., Austin, M., Overton, J.M.C., Aspinall, R., and Hastie, T. 2006. Making better biogeographical predictions of species' distributions. Journal of Applied Ecology 43(3): 386-392. 10.1111/j.1365-2664.2006.01164.x

IPCC, A. 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 1535.

John, J.G., Blanton, C., McHugh, C., Radhakrishnan, A., Rand, K., Vahlenkamp, H., ... and Zeng, Y. 2018. Noaa-gfdl gfdl-esm4 model output prepared for cmip6 scenariomip. Earth System Grid Federation.

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., ... and Kessler, M. 2017. Climatologies at high resolution for the earth's land surface areas. Scientific data 4(1): 1-20. 10.1038/sdata.2017.12228872642PMC5584396

Kim, G.T., Kim, H.J., and Lee, J.H. 2014. Studies on the Community Structure, Samara and Leaf Shape of Three Natural Acer pictum subsp. mono Forest. Korean Journal of Environment and Ecology 28(1): 55-61. (in Korean) 10.13047/KJEE.2014.28.1.55

Kim, J., Jung, H., Jeon, S.W., and Lee, D.K. 2015. Predicting the potential distribution of Korean pine (Pinus koraiensis) using an ensemble of climate scenarios. Journal of the Korean Society of Environmental Restoration Technology 18(2): 79-88. (in Korean) 10.13087/kosert.2015.18.2.79

Koo, K.A., Kim, J., Kong, W.S., Jung, H., and Kim, G. 2016. Projecting the potential distribution of Abies koreana in Korea under the climate change based on RCP scenarios. Journal of the Korean Society of Environmental Restoration Technology 19(6): 19-30. (in Korean) 10.13087/kosert.2016.19.6.19

Kramer-Schadt, S., Niedballa, J., Pilgrim, J.D., Schröder, B., Lindenborn, J., Reinfelder, V., ... and Wilting, A. 2013. The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and distributions 19(11): 1366-1379. 10.1111/ddi.12096

Kriticos, D.J., Webber, B.L., Leriche, A., Ota, N., Macadam, I., Bathols, J., and Scott, J.K. 2012. CliMond: global high‐resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods in Ecology and Evolution 3(1): 53-64. 10.1111/j.2041-210X.2011.00134.x

Lee, B., Chung, J., and Kwon, D.S. 2006. Analysis of site suitability of forest stands for extracting sap of Acer pictum var. mono using GIS and fuzzy sets. Journal of Korean Society of Forest Science 95(1): 38-44. (in Korean)

Lee, C.B. 1990. Dendrology. Hangmoon Pub. Co., Seoul, Korea, 253.

Lenoir, J., Gégout, J. C., Marquet, P.A., de Ruffray, P., and Brisse, H. 2008. A significant upward shift in plant species optimum elevation during the 20th century. Science 320(5884): 1768-1771. 10.1126/science.115683118583610

Li, Y., Li, M., Li, C., and Liu, Z. 2020. Optimized maxent model predictions of climate change impacts on the suitable distribution of cunninghamia lanceolata in China. Forests 11(3): 302. 10.3390/f11030302

O'Donnell, M.S., and Ignizio, D.A. 2012. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geological Survey Data Series 691(10): 4-9. 10.3133/ds691

Oh, Y.J., Kim, M.H., Choi, S.K., Kim, M.K., Eo, J., Yeob, S.J., ... and Lee, Y.H. 2021. Prediction of the spatial distribution of suitable habitats for Geranium carolinianum under SSP scenarios. Ecology and Resilient Infrastructure 8(3): 154-163. (in Korean)

Parmesan, C. and Yohe, G. 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421(6918): 37-42. 10.1038/nature0128612511946

Phillips, S.J., Anderson, R.P., and Schapire, R.E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190(3-4): 231-259. 10.1016/j.ecolmodel.2005.03.026

Pounds, J.A., Bustamante, M.R., Coloma, L.A., Consuegra, J.A., Fogden, M.P., Foster, P.N., ... and Young, B.E. 2006. Widespread amphibian extinctions from epidemic disease driven by global warming. Nature 439(7073): 161-167. 10.1038/nature0424616407945

Robertson, M.P., Villet, M.H., and Palmer, A.R. 2004. A fuzzy classification technique for predicting species' distributions: applications using invasive alien plants and indigenous insects. Diversity and Distributions 10(5-6): 461-474. 10.1111/j.1366-9516.2004.00108.x

Seferian, R. 2019. CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation. 10.22033/ESGF/CMIP6,1395.

Seland, Ø., Bentsen, M., Oliviè, D.J.L., Toniazzo, T., Gjermundsen, A., Graff, L.S., ... and Schulz, M. 2019. NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604 .

Shiogama, H., Abe, M., and Tatebe, H. 2019. MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation.

Song, J.H. and Hur, S.D. 2011. Analysis of leaf morphological variation of 11 natural populations of Acer pictum subsp. mono (Maxim.) Ohashi. Korean Journal of Plant Resources 24(5): 540-548. (in Korean) 10.7732/kjpr.2011.24.5.540

Stocker, T. (Ed.). 2014. Climate change 2013: the physical science basis: Working Group I contribution to the Fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge university press.

Stockwell, D. 1999. The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science 13(2): 143-158. 10.1080/136588199241391

Swart, N.C., Cole, J.N., Kharin, V.V., Lazare, M., Scinocca, J.F., Gillett, N.P., ... and Sigmond, M. 2019. CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation.

Swets, J.A. 1988. Measuring the accuracy of diagnostic systems. Science, 240(4857): 1285-1293. 10.1126/science.32876153287615

Tachiiri, K., Abe, M., Hajima, T., Arakawa, O., Suzuki, T., Komuro, Y., ... and Kawamiya, M. 2019. MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation. Retrieved from http://cera-www.dkrz.de/WDCC/meta/CMIP6/CMIP6.ScenarioMIP.MIROC.MIROC-ES2L .

Thomas, C.D., Cameron, A., Green, R.E., Bakkenes, M., Beaumont, L.J., Collingham, Y.C., ... and Williams, S. E. 2004. Extinction risk from climate change. Nature 427(6970): 145-148. 10.1038/nature0212114712274

Tsoar, A., Allouche, O., Steinitz, O., Rotem, D., and Kadmon, R. 2007. A comparative evaluation of presence- only methods for modelling species distribution. Diversity and distributions 13(4): 397-405. 10.1111/j.1472-4642.2007.00346.x

Um, T.W. and Kim, G.T. 2006. Distribution and growth characteristics of Acer pictum var. mono in relation to topography and soil in Mt. Joongwang, Gangwon Province. Korean Journal of Environment and Ecology 20(2): 200-207. (in Korean)

Voldoire, A. 2019. CNRM-CERFACS CNRM-CM6-1 model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation.

Volodin, E., Mortikov, E., Gritsun, A., Lykossov, V., Galin, V., Diansky, N., Gusev, A., Kostrykin, S., Iakovlev, N., Shestakova, A., and Emelina, S. 2019a. INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321 .

Volodin, E., Mortikov, E., Gritsun, A., Lykossov, V., Galin, V., Diansky, N., Gusev, A., Kostrykin, S., Iakovlev, N., Shestakova, A., and Emelina, S. 2019b. INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation. 10.22033/ESGF/CMIP6.12322.

Waltari, E., Schroeder, R., McDonald, K., Anderson, R. P., and Carnaval, A. 2014. Bioclimatic variables derived from remote sensing: Assessment and application for species distribution modelling. Methods in Ecology and Evolution 5(10): 1033-1042. 10.1111/2041-210X.12264

Warren, D.L. and Seifert, S.N. 2011. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological applications 21(2): 335-342. 10.1890/10-1171.121563566

Wieners, K.H., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., ... and Roeckner, E. 2019. MPI-M MPIESM1. 2-LR model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation: Greenbelt, MD, USA.

Xu, T. and Hutchinson, M. 2011. ANUCLIM version 6.1 user guide. The Australian National University, Fenner School of Environment and Society, Canberra, 90.

Yackulic, C.B., Chandler, R., Zipkin, E.F., Royle, J.A., Nichols, J.D., Campbell Grant, E.H., and Veran, S. 2013. Presence‐only modelling using MAXENT: when can we trust the inferences?. Methods in Ecology and Evolution 4(3): 236-243. 10.1111/2041-210x.12004

Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., ... and Adachi, Y. 2019. MRI MRI- ESM2.0 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation 10.

Ziehn, T., Chamberlain, M., Lenton, A., Law, R., Bodman, R., Dix, M., ... and Druken, K. 2019. CSIRO ACCESS- ESM1. 5 model output prepared for CMIP6 CMIP. Earth System Grid Federation: Greenbelt, MD, USA.

Information

Publisher :Korean Society of Ecology and Infrastructure Engineering
Publisher(Ko) :응용생태공학회
Journal Title :Ecology and Resilient Infrastructure
Journal Title(Ko) :응용생태공학회 논문집
Volume : 9
No :3
Pages :163-173
Received Date : 2022-09-01
Revised Date : 2022-09-15
Accepted Date : 2022-09-18
DOI :https://doi.org/10.17820/eri.2022.9.3.163

Ecology and Resilient Infrastructure ISSN:2288-8527(Online) 응용생태공학회 논문집

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