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Research Article | Open Access

Modelling analysis embodies drastic transition among global potential natural vegetations in face of changing climate

Zhengchao Rena,b, Lei Liua,c( ), Fang Yind, Xiaoni Liue
School of Earth Science and Resources, Chang'an University, Xi'an, 710054, China
Research Center of Ecological Construction and Environmental Conservation in Gansu Province, Gansu Agricultural University, Lanzhou, 730070, China
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China
School of Land Engineering, Chang'an University, Xi'an, 710054, China
College of Pratacultural Science, Gansu Agricultural University, Lanzhou, 730070, China
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Abstract

Potential natural vegetation (PNV) is a valuable reference for ecosystem renovation and has garnered increasing attention worldwide. However, there is limited knowledge on the spatio-temporal distributions, transitional processes, and underlying mechanisms of global natural vegetation, particularly in the case of ongoing climate warming. In this study, we visualize the spatio-temporal pattern and inter-transition procedure of global PNV, analyse the shifting distances and directions of global PNV under the influence of climatic disturbance, and explore the mechanisms of global PNV in response to temperature and precipitation fluctuations. To achieve this, we utilize meteorological data, mainly temperature and precipitation, from six phases: the Last Inter-Glacial (LIG), the Last Glacial Maximum (LGM), the Mid Holocene (MH), the Present Day (PD), 2030 (2021–2040) and 2090 (2081–2100), and employ a widely-accepted comprehensive and sequential classification system (CSCS) for global PNV classification. We find that the spatial patterns of five PNV groups (forest, shrubland, savanna, grassland and tundra) generally align with their respective ecotopes, although their distributions have shifted due to fluctuating temperature and precipitation. Notably, we observe an unexpected transition between tundra and savanna despite their geographical distance. The shifts in distance and direction of five PNV groups are mainly driven by temperature and precipitation, although there is heterogeneity among these shifts for each group. Indeed, the heterogeneity observed among different global PNV groups suggests that they may possess varying capacities to adjust to and withstand the impacts of changing climate. The spatio-temporal distributions, mutual transitions and shift tendencies of global PNV and its underlying mechanism in face of changing climate, as revealed in this study, can significantly contribute to the development of strategies for mitigating warming and promoting re-vegetation in degraded regions worldwide.

Keywords

Global warmingCSCSPotential natural vegetationVegetation classificationPredicted model

References

 

Aguirre-Gutiérrez, J., Malhi, Y., Lewis, S.L., Fauset, S., Adu-Bredu, S., Affum-Baffoe, K., Baker, T.R., Gvozdevaite, A., Hubau, W., Moore, S., Peprah, T., Ziemińska, K., Phillips, O.L., Oliveras, I., 2020. Long-term droughts may drive drier tropical forests towards increased functional, taxonomic and phylogenetic homogeneity. Nat. Commun. 11, 3346. https://doi.org/10.1038/s41467-020-16973-4.
Crossref Google Scholar
 

Bakkenes, M., Alkemade, J., Ihle, F., Leemans, R., Latour, J., 2002. Assessing effects of forecasted climate change on the diversity and distribution of European higher plants for 2050. Glob. Chang. Biol. 8, 390–407. https://doi.org/10.1046/j.1354-1013.2001.00467.x.
Crossref Google Scholar
 

Bonan, G.B., Pollard, D., Thompson, S.L., 1992. Effects of boreal forest vegetation on global climate. Nature 359, 716–718. https://doi.org/10.1038/359716a0.
Crossref Google Scholar
 

Bourdouxhe, A., Wibail, L., Claessens, H., Dufrêne, M., 2023. Modeling potential natural vegetation: a new light on an old concept to guide nature conservation in fragmented and degraded landscapes. Ecol. Model. 481, 110382. https://doi.org/10.1016/j.ecolmodel.2023.110382.
Crossref Google Scholar
 

Chen, C., Park, T., Wang, X., Piao, S., Xu, B., Chaturvedi, R.K., Fuchs, R., Brovkin, V., Ciais, P., Fensholt, R., Tømmervik, H., Bala, G., Zhu, Z., Nemani, R.R., Myneni, R.B., 2019. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129. https://doi.org/10.1038/s41893-019-0220-7.
Crossref Google Scholar
 

Ding, L., Kapp, P., Cai, F., Garzione, C.N., Xiong, Z., Wang, H., Wang, C., 2022. Timing and mechanisms of Tibetan Plateau uplift. Nat. Rev. Earth Environ. 3, 652–667. https://doi.org/10.1038/s43017-022-00318-4.
Crossref Google Scholar
 

Du, H., Zhao, J., Shi, Y., 2021. Spatio-temporal distribution of sensitive regions of potential vegetation in China based on the Comprehensive Sequential Classification System (CSCS) and a climate-change model. Rangel. J. 43, 353–361. https://doi.org/10.1071/RJ20096.
Crossref Google Scholar
 

Duveiller, G., Filipponi, F., Ceglar, A., Bojanowski, J., Alkama, R., Cescatti, A., 2021. Revealing the widespread potential of forests to increase low level cloud cover. Nat. Commun. 12, 4337. https://doi.org/10.1038/s41467-021-24551-5.
Crossref Google Scholar
 
Esri Inc., 2020. ArcMap (version 10.8). https://www.esri.com/en-us/arcgis/products/arcgis-pro. (Accessed 18 October 2023).
 

Feng, Q., Liang, T., Huang, X., Lin, H., Xie, H., Ren, J., 2013. Characteristics of global potential natural vegetation distribution from 1911 to 2000 based on comprehensive sequential classification system approach. Grassl. Sci. 59, 87–99. https://doi.org/10.1111/grs.12016.
Crossref Google Scholar
 

Gonzalez, P., Neilson, R.P., Lenihan, J.M., Drapek, R.J., 2010. Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change. Glob. Ecol. Biogeogr. 19, 755–768. https://doi.org/10.1111/j.1466-8238.2010.00558.x.
Crossref Google Scholar
 

Hart, J.F., 1954. Central tendency in areal distributions. Econ. Geogr. 30, 48–59. https://doi.org/10.2307/142234.
Crossref Google Scholar
 

Hickler, T., Vohland, K., Feehan, J., Miller, P.A., Smith, B., Costa, L., Giesecke, T., Fronzek, S., Carter, T.R., Cramer, W., Kühn, I., Sykes, M.T., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model. Glob. Ecol. Biogeogr. 21, 50–63. https://doi.org/10.1111/j.1466-8238.2010.00613.x.
Crossref Google Scholar
 

Hinze, J., Albrecht, A., Michiels, H.G., 2023. Climate-adapted potential vegetation-a European multiclass model estimating the future potential of natural vegetation. Forests 14, 239. https://doi.org/10.3390/f14020239.
Crossref Google Scholar
 

Holdridge, L.R., 1947. Determination of world plant formations from simple climatic data. Science 105, 367–368. https://doi.org/10.1126/science.105.2727.3.
Crossref Google Scholar
 
IPCC, 2021. Climate Change 2021: the Physical Science Basis, 1st edn. Cambridge University Press, London.
 

Liang, T., Feng, Q., Cao, J., Xie, H., Lin, H., Zhao, J., Ren, J., 2012a. Changes in global potential vegetation distributions from 1911 to 2000 as simulated by the comprehensive sequential classification system approach. Chin. Sci. Bull. 57, 1298–1310. https://doi.org/10.1007/s11434-011-4870-8.
Crossref Google Scholar
 

Liang, T., Feng, Q., Yu, H., Huang, X., Lin, H., An, S., Ren, J., 2012b. Dynamics of natural vegetation on the Tibetan Plateau from past to future using a comprehensive and sequential classification system and remote sensing data. Grassl. Sci. 58, 208–220. https://doi.org/10.1111/grs.12000.
Crossref Google Scholar
 

Liu, H., Yin, Y., 2013. Response of forest distribution to past climate change: an insight into future predictions. Chin. Sci. Bull. 58, 4426–4436. https://doi.org/10.1007/s11434-013-6032-7.
Crossref Google Scholar
 

Liu, X., Li, Q., Wang, H., Ren, Z., He, G., Zhang, D., Han, T., Sun, B., Pan, D., Ji, T., 2021. Response of potential grassland vegetation to historical and future climate change in Inner Mongolia. Rangel. J. 43, 329–338. https://doi.org/10.1071/RJ20108.
Crossref Google Scholar
 

Miao, Y., Fang, X., Sun, J., Xiao, W., Yang, Y., Wang, X., Fransworth, A., Huang, K., Ren, Y., Wu, F., Qiao, Q., Zhang, W., Meng, Q., Yan, X., Zheng, Z., Song, C., Utescher, T., 2022. A new biologic paleoaltimetry indicating Late Miocene rapid uplift of northern Tibet Plateau. Science 378, 1074–1079. https://doi.org/10.1126/science.abo2475.
Crossref Google Scholar
 

Popkin, G., 2019. How much can forests fight climate change? Nature 565, 280–282. https://doi.org/10.1038/d41586-019-00122-z.
Crossref Google Scholar
 
R Core Team, 2023. R (version 4.3.1). R Core Team. https://cloud.r-project.org. (Accessed 18 October 2023).
 

Ramankutty, N., Foley, J.A., 1999. Estimating historical changes in global land cover: croplands from 1700 to 1992. Glob. Biogeochem. Cycles 13, 997–1027. https://doi.org/10.1029/1999GB900046.
Crossref Google Scholar
 

Ren, J., Hu, Z., Zhao, J., Zhang, D., Hou, F., Lin, H., Mu, X., 2008. A grassland classification system and its application in China. Rangel. J. 30, 199–209. https://doi.org/10.1071/RJ08002.
Crossref Google Scholar
 

Ren, Z., Zhu, H., Shi, H., Liu, X., 2021. Shift of potential natural vegetation against global climate change under historical, current and future scenarios. Rangel. J. 43, 309–319. https://doi.org/10.1071/RJ20092.
Crossref Google Scholar
 

Schickhoff, U., Bobrowski, M., Boehner, J., Buerzle, B., Chaudhary, R.P., Gerlitz, L., Heyken, H., Lange, J., Mueller, M., Scholten, T., Schwab, N., Wedegaertner, R., 2015. Do Himalayan treelines respond to recent climate change? An evaluation of sensitivity indicators. Earth Syst. Dyn. 6, 245–265. https://doi.org/10.5194/esd-6-245-2015.
Crossref Google Scholar
 

Smith, B., Prentice, I.C., Sykes, M.T., 2001. Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob. Ecol. Biogeogr. 10, 621–637. https://doi.org/10.1046/j.1466-822X.2001.t01-1-00256.x.
Crossref Google Scholar
 

Song, H., An, Z., Ye, Q., Stüeken, E.E., Li, J., Hu, J., Algeo, T.J., Tian, L., Chu, D., Song, H., Xiao, S., Tong, J., 2023. Mid-latitudinal habitable environment for marine eukaryotes during the waning stage of the Marinoan snowball glaciation. Nat. Commun. 14, 1564. https://doi.org/10.1038/s41467-023-37172-x.
Crossref Google Scholar
 

Stott, L.D., Kennett, J.P., 1991. Abrupt deep-sea warming, palaeoceanographic changes and benthic extinctions at the end of the Palaeocene. Nature 353, 225–229. https://doi.org/10.1038/353225a0.
Crossref Google Scholar
 

Sun, A., Feng, Z., Ma, Y., 2010. Vegetation and environmental changes in western Chinese Loess Plateau since 13.0 ka BP. J. Geogr. Sci. 20, 177–192. https://doi.org/10.1007/s11442-010-0177-y.
Crossref Google Scholar
 

Sykes, M.T., Prentice, I.C., Laarif, F., 1999. Quantifying the impact of global climate change on potential natural vegetation. Clim. Change 41, 37–52. https://doi.org/10.1023/A:1005435831549.
Crossref Google Scholar
 

Tüxen, R., 1956. Die heutige potenticlle natürliche Vegetation als Gegenstand der Vegetationskartierung. Angew Pflanzensoziol. 13, 5–42.
Google Scholar
 

Wang, H., Ni, J., Prentice, I.C., 2011. Sensitivity of potential natural vegetation in China to projected changes in temperature, precipitation and atmospheric CO2. Reg. Environ. Change 11, 715–727. https://doi.org/10.1007/s10113-011-0204-2.
Crossref Google Scholar
 

Xu, T., Hutchinson, M.F., 2013. New developments and applications in the ANUCLIM spatial climatic and bioclimatic modelling package. Environ. Model. Software 40, 267–279. https://doi.org/10.1016/j.envsoft.2012.10.003.
Crossref Google Scholar
 

Yuan, Q., Zhao, D., Wu, S., Dai, E., 2011. Validation of the Integrated Biosphere Simulator in simulating the potential natural vegetation map of China. Ecol. Res. 26, 917–929. https://doi.org/10.1007/s11284-011-0845-0.
Crossref Google Scholar
 

Yue, T., Fan, Z., Chen, C., Sun, X., Li, B., 2011. Surface modelling of global terrestrial ecosystems under three climate change scenarios. Ecol. Model. 222, 2342–2361. https://doi.org/10.1016/j.ecolmodel.2010.11.026.
Crossref Google Scholar
 

Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H., Shindo, E., Mizuta, R., Obata, A., Adachi, Y., Ishii, M., 2019. The meteorological research institute earth system model version 2.0, MRI-ESM2.0: description and basic evaluation of the physical component. J. Meteorol. Soc. Japan 97, 931–965. https://doi.org/10.2151/jmsj.2019-051.
Crossref Google Scholar
 

Zhang, X., Wang, W., Fang, X., Ye, Y., Li, B., 2011. Natural vegetation pattern over Northeast China in late 17th century. Sci. Geol. Sin. 31, 184–189. http://geoscien.neigae.ac.cn/EN/10.13249/j.cnki.sgs.2011.02.184.
Crossref Google Scholar
 

Zhao, M., Neilson, R.P., Yan, X., Dong, W., 2002. Modelling the vegetation of China under changing climate. Acta Geograph. Sin. 57, 28–38. http://www.geog.com.cn/EN/10.11821/xb200201004.
Crossref Google Scholar
Forest Ecosystems
Volume 11 Issue 2,
April 2024
Article number: 100180
DOI: 10.1016/j.fecs.2024.100180
Cite this article:
Ren Z, Liu L, Yin F, et al. Modelling analysis embodies drastic transition among global potential natural vegetations in face of changing climate. Forest Ecosystems, 2024, 11(2): 100180. https://doi.org/10.1016/j.fecs.2024.100180

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Received: 28 October 2023
Revised: 02 February 2024
Accepted: 26 February 2024
Published: 08 March 2024
© 2024 The Authors.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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