Research Paper
A statistical method to generate high-resolution climate datasets for modeling plant distribution range and range shifts under climate change in mountainous areas
Chi-Cheng Liao, Huan-Yu Lin, Su-Wei Fan
Published on: 02 January 2023
Page: 8 - 22
DOI: 10.6165/tai.2023.68.8
Abstract
This study aims to develop a statistical method to generate high-resolution historical and future climate datasets for modeling plant distributions in mountainous area. Two climate datasets that were from Taiwan Climate Change Projection Information and Adaptation Knowledge Platform (TCCIP) and meteorological stations were used to construct two historical climate datasets with 50 × 50 m2 spatial resolution, respectively. The two historical climate datasets presented similar temperature pattern but distinct precipitation patterns in northern Taiwan (NTWN). Random Forests (RF) had predicted similar distribution range of natural grassland along mountain ridge when RF were applied by the two climate datasets, whereas RF had predicted restricted distribution range when it was applied by true absence data. The two historical climate datasets were added to the relative changes of climate variables representing four future climate scenarios. RF method based on the future climate datasets predicted habitat loss of natural grassland at the mid and end of this century, regardless of climate datasets and four warming scenarios. Due to the altitudinal limits of NTWN, there is almost no chance for natural grassland to track their climatic requirements toward higher elevations under climate change. High-resolution historical and future climate datasets generated by the statistical method were useful for species distribution model to project species potential distribution range in mountainous area and were available to examine species range shifts under climate change. Model performances based on the high-resolution climate dataset may have better expressed the climatic requirements and exact climatic niches of species in mountainous areas.
Keyword: Climate change, high-resolution climate dataset, Random Forests, species distribution model, Taiwan
Literature Cited
Allen, C. D., Macalady, A. K., Chenchouni, H., Bachelet, D., McDowell, N., Vennetier, M., Kitzberger, T., Rigling, A., Breshears, D.D., Hogg, E.H., Gonzalez, P., Fensham, R., Zhang, Z., Castro, J., Demidova, N., Lim, J.-H., Allard, G., Running, S.W., Semerci, A., Cobb, N. 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259(4): 660?684.
DOI: 10.1016/j.foreco.2009.09.001View Article
Google Scholar
Ashcroft, M. B., Gollan, J. R. 2012. Fine?resolution (25 m) topoclimatic grids of near?surface (5 cm) extreme temperatures and humidities across various habitats in a large (200× 300 km) and diverse region. Int. J. Climatol. 32(14): 2134?2148.
DOI: 10.1002/joc.2428View Article
Google Scholar
Ashcroft, M. B., Gollan, J. R., Warton, D. I., Ramp, D. 2012. A novel approach to quantify and locate potential microrefugia using topoclimate, climate stability, and isolation from the matrix. Glob. Change Biol. 18(6): 1866?1879.
DOI: 10.1111/j.1365-2486.2012.02661.xView Article
Google Scholar
Boulesteix, A. L., Janitza, S., Kruppa, J., K?nig, I. R. 2012. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 2(6): 493?507.
DOI: 10.1002/widm.1072View Article
Google Scholar
Breiman, L. 2001. Random forests. Mach. Learn. 45(1): 5?32.
DOI: 10.1023/A:1010933404324View Article
Chambers, J., Hastie, T. 1992. Linear models. Chapter 4 of statistical models in S. Wadsworth & Brooks/Cole.
Chen, W. K., Tsai, C. Y. 1983. The climate of Yangmingshan National Park. Yangmingshan National Park, Construction and Planning Agency Ministry of the Interior, Executive Yuan, Taipei, Taiwan.
Chiou, C.R., Song, G.Z.M., Chien, J.H., Hsieh, C.F., Wang, J.-C., Chen, M.Y., Liu, H.Y., Yeh, C.L., Hsia, Y.J., Chen, T.Y. 2010. Altitudinal distribution patterns of plant species in Taiwan are mainly determined by the northeast monsoon rather than the heat retention mechanism of Massenerhebung. Bot. Stud. 51(1): 89?97.
Dingman, J. R., Sweet, L. C., McCullough, I., Davis, F. W., Flint, A., Franklin, J., Flint, L. E. 2013. Cross-scale modeling of surface temperature and tree seedling establishment in mountain landscapes. Ecol. Process. 2(1): 1?15.
DOI: 10.1186/2192-1709-2-30View Article
Google Scholar
Dobrowski, S.Z. 2011. A climatic basis for microrefugia: the influence of terrain on climate. Glob. Change Biol. 17(2): 1022?1035.
DOI: 10.1111/j.1365-2486.2010.02263.xView Article
Google Scholar
Elith, J., Leathwick, J.R. 2009. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40(1): 677?697.
DOI: 10.1146/annurev.ecolsys.110308.120159View Article
Google Scholar
Evans, J.S., Cushman, S.A. 2009. Gradient modeling of conifer species using random forests. Landsc. Ecol. 24(5): 673?683.
DOI: 10.1007/s10980-009-9341-0View Article
Google Scholar
Fatemi, S. S., Rahimi, M., Tarkesh, M., Ravanbakhsh, H. 2018. Predicting the impacts of climate change on the distribution of Juniperus excelsa M. Bieb. in the central and eastern Alborz Mountains, Iran. iForest 11(5): 643?650.
DOI: 10.3832/ifor2559-011View Article
Google Scholar
Fick, S. E., Hijmans, R. J. 2017. WorldClim 2: new 1?km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12): 4302?4315.
DOI: 10.1002/joc.5086View Article
Google Scholar
Fois, M., Fenu, G., Lombrana, A. C., Cogoni, D., Bacchetta, G. 2015. A practical method to speed up the discovery of unknown populations using Species Distribution Models. J. Nat. Conserv. 24: 42?48.
DOI: 10.1016/j.jnc.2015.02.001View Article
Google Scholar
Fridley, J. D. 2009. Downscaling climate over complex terrain: high finescale (< 1000 m) spatial variation of near-ground temperatures in a montane forested landscape (Great Smoky Mountains). J. Appl. Meteorol. Climatol. 48(5): 1033?1049.
DOI: 10.1175/2008JAMC2084.1View Article
Google Scholar
Godsoe, W., Murray, R., Plank, M. J. 2015. Information on biotic interactions improves transferability of distribution models. Am. Nat. 185(2): 281?290.
DOI: 10.1086/679440View Article
Google Scholar
Greiser, C., Meineri, E., Luoto, M., Ehrl?n, J., Hylander, K. 2018. Monthly microclimate models in a managed boreal forest landscape. Agric. For. Meteorol. 250: 147?158.
DOI: 10.1016/j.agrformet.2017.12.252View Article
Google Scholar
Guisan, A., Zimmermann, N.E., Elith, J., Graham, C.H., Phillips, S., Peterson, A.T. 2007. What matters for predicting the occurrences of trees: techniques, data, or species characteristics? Ecol. Monogr. 77(4): 615?630.
DOI: 10.1890/06-1060.1View Article
Google Scholar
Hamann, A., Wang, T. 2006. Potential effects of climate change on ecosystem and tree species distribution in British Columbia. Ecology 87(11): 2773?2786.
DOI: 10.1890/0012-9658(2006)87[2773:PEOCCO]2.0.CO;2View Article
Google Scholar
Hao, T., Elith, J., Guillera?Arroita, G., Lahoz?Monfort, J. J. J. D., Distributions. 2019. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. 25(5): 839?852.
DOI: 10.1111/ddi.12892View Article
Google Scholar
Heikkinen, R. K., Marmion, M., Luoto, M. 2012. Does the interpolation accuracy of species distribution models come at the expense of transferability? Ecography 35(3): 276?288.
DOI: 10.1111/j.1600-0587.2011.06999.xView Article
Google Scholar
Hsieh, C. F., Chao, W. C., Liao, C. C., Yang, K. C., Hsieh, T. H. 1997. Floristic composition of the evergreen broad-leaved forests of Taiwan. Nat. Hist. Res. 4, 1?16.
Hu, X.-G., Wang, T., Liu, S.-S., Jiao, S.-Q., Jia, K.-H., Zhou, S.-S., Jin, Y., Li, Y., El-Kassaby, Y.A., Mao, J.-F. 2017. Predicting future seed sourcing of Platycladus orientalis (L.) for future climates using climate niche models. Forests 8(12): 471.
IPCC. 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.
DOI: 10.3390/f8120471View Article
Google Scholar
Iturbide, M., Bedia, J., Guti?rrez, J. M. 2018. Background sampling and transferability of species distribution model ensembles under climate change. Glob. Planet. Change 166: 19?29.
DOI: 10.1016/j.gloplacha.2018.03.008View Article
Google Scholar
Karger, D. N., Conrad, O., B?hner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N.E., Linder, H.P., Kessler, M. 2017. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4(1): 170122.
DOI: 10.1038/sdata.2017.122View Article
Google Scholar
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M., Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., Polvani, L., Vertenstein, M. 2015. The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96(8): 1333?1349.
DOI: 10.1175/BAMS-D-13-00255.1View Article
Google Scholar
Keppel, G., Van Niel, K. P., Wardell?Johnson, G. W., Yates, C. J., Byrne, M., Mucina, L., Schut, A.G.T., Hopper, S.D., Franklin, S.E. 2012. Refugia: identifying and understanding safe havens for biodiversity under climate change. Glob. Ecol. Biogeogr. 21(4): 393?404.
DOI: 10.1111/j.1466-8238.2011.00686.xView Article
Google Scholar
Khoshgoftaar, T. M., Golawala, M., Van Hulse, J. 2007. An empirical study of learning from imbalanced data using random forest. 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007): 310–317.
DOI: 10.1109/ICTAI.2007.46View Article
Google Scholar
Korner, C. 1998. A re-assessment of high elevation treeline positions and their explanation. Oecologia 115(4): 445–459.
DOI: 10.1007/s004420050540View Article
Google Scholar
Korner, C., Paulsen, J. 2004. A world-wide study of high altitude treeline temperatures. J. Biogeogr. 31(5): 713?732.
DOI: 10.1111/j.1365-2699.2003.01043.xView Article
Google Scholar
Lannuzel, G., Balmot, J., Dubos, N., Thibault, M., Fogliani, B. 2021. High-resolution topographic variables accurately predict the distribution of rare plant species for conservation area selection in a narrow-endemism hotspot in New Caledonia. Biodivers. Conserv. 30(4): 963?990.
DOI: 10.1007/s10531-021-02126-6View Article
Google Scholar
Lenoir, J., Hattab, T., Pierre, G. 2017. Climatic microrefugia under anthropogenic climate change: implications for species redistribution. Ecography 40(2): 253?266.
DOI: 10.1111/ecog.02788View Article
Google Scholar
Li, C. F., Chytr?, M., Zelen?, D., Chen, M. Y., Chen, T. Y., Chiou, C. R., Hsia, Y.-J., Liu, H.-Y., Yang, S.-Z., Yeh, C.-L., Wang, J.-C., Yu, C.-F., Lai, Y.-J., Chao, W.-C., Hsieh, C.-F., Bruelheide, H. 2013. Classification of Taiwan forest vegetation. Appl. Veg. Sci. 16(4): 698?719.
DOI: 10.1111/avsc.12025View Article
Google Scholar
Liao, C. C., Chang, C. R., Hsu, M. T., Poo, W. K. 2014. Experimental evaluation of the sustainability of dwarf bamboo (Pseudosasa usawai) sprout-harvesting practices in Yangminshan National Park, Taiwan. Environ. Manage. 54(2): 320?330.
DOI: 10.1007/s00267-014-0296-9View Article
Google Scholar
Liao, C. C., Chen, Y. H. 2021. Improving performance of species distribution model in mountainous areas with complex topography. Ecol. Res. 36(4): 648?662.
DOI: 10.1111/1440-1703.12227View Article
Google Scholar
Liao, C.C., Chen, Y.H. 2022. The effects of true and pseudo-absence data on the performance of species distribution models at landscape scale. Taiwania 67(1): 9?20.
DOI: 10.6165/tai.2022.67.9View Article
Google Scholar
Liao, C.C., Kuo, S.C., Chang, C.R. 2012. Forest distribution on small isolated hills and implications on woody plant distribution under threats of global warming. Taiwania 57(3): 242?250.
DOI: 10.6165/tai.2012.57(3).242View Article
Google Scholar
Liaw, A., Wiener, M. 2002. Classification and regression by random. Forest. R news 2(3): 18?22.
Lin, H. Y., Hu, J. M., Chen, T. Y., Hsieh, C. F., Wang, G., Wang, T. 2018. A dynamic downscaling approach to generate scale-free regional climate data in Taiwan. Taiwania 63(3): 251?266.
DOI: 10.6165/tai.2018.63.251View Article
Google Scholar
Lin, H. Y., Li, C. F., Chen, T. Y., Hsieh, C. F., Wang, G., Wang, T., Hu, J. M. 2020. Climate?based approach for modeling the distribution of montane forest vegetation in Taiwan. Appl. Veg. Sci. 23(2): 239?253.
DOI: 10.1111/avsc.12485View Article
Google Scholar
Lin, L.-Y., Lin, C.-T., Chen, Y.-M., Cheng, C.-T., Li, H.-C., Chen, W.-B. 2022. The Taiwan Climate Change Projection Information and Adaptation Knowledge Platform: A decade of climate research. Water 14(3): 358.
DOI: 10.3390/w14030358View Article
Google Scholar
Liu, B., Liang, E., Zhu, L. 2011. Microclimatic conditions for Juniperus saltuaria treeline in the Sygera Mountain, Southeastern Tibetan Plateau. Mt. Res. Dev. 31(1): 45?53.
DOI: 10.1659/MRD-JOURNAL-D-10-00096.1View Article
Google Scholar
Lobo, J. M., Jim?nez?Valverde, A., Real, R. 2008. AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17(2): 145?151.
DOI: 10.1111/j.1466-8238.2007.00358.xView Article
Google Scholar
Maria, B., Udo, S. 2017. Why input matters: Selection of climate data sets for modelling the potential distribution of a treeline species in the Himalayan region. Ecol. Modell. 359, 92?102.
DOI: 10.1016/j.ecolmodel.2017.05.021View Article
Google Scholar
Meineri, E., Hylander, K. 2017. Fine?grain, large?domain climate models based on climate station and comprehensive topographic information improve microrefugia detection. Ecography 40(8): 1003?1013.
DOI: 10.1111/ecog.02494View Article
Google Scholar
Mi, C., Huettmann, F., Guo, Y., Han, X., Wen, L. 2017. Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence. PeerJ, 5, e2849.
DOI: 10.7717/peerj.2849View Article
Google Scholar
Miles, L., Grainger, A., Phillips, O. 2004. The impact of global climate change on tropical forest biodiversity in Amazonia. Glob. Ecol. Biogeogr. 13(6): 553?565.
DOI: 10.1111/j.1466-822X.2004.00105.xView Article
Google Scholar
Mohapatra, J., Singh, C.P., Hamid, M., Verma, A., Semwal, S.C., Gajmer, B., Khuroo, A.A., Kumar, A., Nautiyal, M.C., Sharma, N., Pandya, H.A. 2019. Modelling Betula utilis distribution in response to climate-warming scenarios in Hindu-Kush Himalaya using random forest. Biodivers. Conserv. 28(8-9): 2295?2317.
DOI: 10.1007/s10531-019-01731-wView Article
Google Scholar
Orsenigo, S., Montagnani, C., Fenu, G., Gargano, D., Peruzzi, L., Abeli, T., Alessandrini, A., Bacchetta, G., Bartolucci, F., Bovio, M., Brullo, C., Brullo, S., Carta, A., Castello, M., Cogoni, D., Conti, F., Domina, G., Foggi, B., Gennai, M., Gigante, D., Iberite, M., Lasen, C., Magrini, S., Perrino, E.V., Prosser, F., Santangelo, A., Selvaggi, A., Stinca, A., Vagge, I., Villani, M., Wagensommer, R.P., Wilhalm, T., Tartaglini, N., Dupr?, E., Blasi, C., Rossi, G. 2018. Red Listing plants under full national responsibility: extinction risk and threats in the vascular flora endemic to Italy. Biol. Conserv. 224: 213?222.
DOI: 10.1016/j.biocon.2018.05.030View Article
Google Scholar
Pearse, I.S., Hipp, A.L. 2012. Global patterns of leaf defenses in oak species. Evolution 66(7): 2272?2286.
DOI: 10.1111/j.1558-5646.2012.01591.xView Article
Google Scholar
Qian, H. 2017. Climatic correlates of phylogenetic relatedness of woody angiosperms in forest communities along a tropical elevational gradient in South America. J. Plant Ecol. 11(3): 394?400.
DOI: 10.1093/jpe/rtx006View Article
Google Scholar
Qiao, H., Feng, X., Escobar, L. E., Peterson, A. T., Sober?n, J., Zhu, G., Pape?, M. 2019. An evaluation of transferability of ecological niche models. Ecography 42(3): 521?534.
DOI: 10.1111/ecog.03986View Article
Google Scholar
Schorr, G., Holstein, N., Pearman, P., Guisan, A., Kadereit, J. 2012. Integrating species distribution models (SDMs) and phylogeography for two species of Alpine Primula. Ecol. Evol. 2(6): 1260?1277.
DOI: 10.1002/ece3.100View Article
Google Scholar
Smith, W. K., Germino, M. J., Johnson, D. M., Reinhardt, K. 2009. The altitude of alpine treeline: A Bellwether of climate change effects. Bot. Rev. 75(2): 163?190.
DOI: 10.1007/s12229-009-9030-3View Article
Google Scholar
Su, H. J. 1984. Studies on the climate and vegetation types of the natural forests in Taiwan (II) Altitudinal vegetation zones in relation to temperature gradient. Quarterly Journal of Chinese Forestry 17: 57?73.
Thomas, C. D., Cameron, A., Green, R. E., Bakkenes, M., Beaumont, L. J., Collingham, Y. C., Erasmus, B.F.N., de Siqueira, M.F., Grainger, A., Hannah, L., Hughes, L., Huntley, B., van Jaarsveld, A.S., Midgley, G.F., Miles, L., Ortega-Huerta, M.A., Peterson, A. T., Phillips, O.L., Williams, S. E. 2004. Extinction risk from climate change. Nature 427(6970): 145?148.
DOI: 10.1038/nature02121View Article
Google Scholar
Thuiller, W., Georges, D., Engler, R., Breiner, F., Georges, M. D., Thuiller, C. W. 2016. Package ‘biomod2’. Species distribution modeling within an ensemble forecasting framework.
Vanneste, T., Michelsen, O., Graae, B. J., Kyrkjeeide, M. O., Holien, H., Hassel, K., Lindmo, S., Kap?s, R.E., De Frenne, P. 2017. Impact of climate change on alpine vegetation of mountain summits in Norway. Ecol. Res. 32(4): 579?593.
DOI: 10.1007/s11284-017-1472-1View Article
Google Scholar
Vanwalleghem, T., Meentemeyer, R. 2009. Predicting forest microclimate in heterogeneous landscapes. Ecosystems 12(7): 1158?1172.
DOI: 10.1007/s10021-009-9281-1View Article
Google Scholar
Walther, G.-R. 2010. Community and ecosystem responses to recent climate change. Philos. Trans. R. Soc. Lond. B Biol Sci. 365(1549):2019?2024.
DOI: 10.1098/rstb.2010.0021View Article
Google Scholar
Wang, T., Hamann, A., Spittlehouse, D., Carroll, C. 2016. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS One 11(6): e0156720.
DOI: 10.1371/journal.pone.0156720View Article
Google Scholar
Weng, S., Yang, C. 2012. The construction of monthly rainfall and temperature datasets with 1km gridded resolution over Taiwan area (1960?2009) and its application to climate projection in the near future (2015?2039). Atmos. Sci. 40(4): 349?369.
Williams, J. N., Seo, C., Thorne, J., Nelson, J. K., Erwin, S., O’Brien, J. M., Schwartz, M. W. 2009. Using species distribution models to predict new occurrences for rare plants. Divers. Distrib. 15(4): 565?576.
DOI: 10.1111/j.1472-4642.2009.00567.xView Article
Google Scholar
Xu, Y., Huang, Y., Zhao, H., Yang, M., Zhuang, Y., Ye, X. 2021. Modelling the effects of climate change on the distribution of endangered Cypripedium japonicum in China. Forests 12(4): 429.
DOI: 10.3390/f12040429View Article
Google Scholar
Zhao, X., Meng, H., Wang, W., Yan, B. 2016. Prediction of the distribution of alpine tree species under climate change scenarios: Larix chinensis in Taibai Mountain (China). Pol. J. Ecol. 64(2): 200?212.
DOI: 10.3161/15052249PJE2016.64.2.005View Article
Google Scholar
Zhu, Y., Wei, W., Li, H., Wang, B., Yang, X., Liu, Y. 2018. Modelling the potential distribution and shifts of three varieties of Stipa tianschanica in the eastern Eurasian Steppe under multiple climate change scenarios. Glob. Ecol. Conserv. 16: e00501.
DOI: 10.1016/j.gecco.2018.e00501View Article
Google Scholar