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Evolution of Potential Spatial Distribution Patterns of Carex Tussock Wetlands Under Climate Change Scenarios, Northeast China

Qing QI Mingye ZHANG Shouzheng TONG Yan LIU Dongjie ZHANG Guanglei ZHU Xianguo LYU

QI Qing, ZHANG Mingye, TONG Shouzheng, LIU Yan, ZHANG Dongjie, ZHU Guanglei, LYU Xianguo, 2022. Evolution of Potential Spatial Distribution Patterns of Carex Tussock Wetlands Under Climate Change Scenarios, Northeast China. Chinese Geographical Science, 32(1): 142−154 doi:  10.1007/s11769-022-1260-x
Citation: QI Qing, ZHANG Mingye, TONG Shouzheng, LIU Yan, ZHANG Dongjie, ZHU Guanglei, LYU Xianguo, 2022. Evolution of Potential Spatial Distribution Patterns of Carex Tussock Wetlands Under Climate Change Scenarios, Northeast China. Chinese Geographical Science, 32(1): 142−154 doi:  10.1007/s11769-022-1260-x

doi: 10.1007/s11769-022-1260-x

Evolution of Potential Spatial Distribution Patterns of Carex Tussock Wetlands Under Climate Change Scenarios, Northeast China

Funds: Under the auspices of the National Natural Science Foundation of China (No. 41871101), the Science and Technology Development Project of Jilin Province (No. 20190201115JC), the ‘Strategic Priority Research Program’ of the Chinese Academy of Sciences (No. XDA23060402).
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  • Figure  1.  Location of study area and Carex tussock wetland distribution

    Figure  2.  The receiver operator characteristics (ROC) curve in the Maxent model predictions for Carex tussock wetland in Northeast China. AUC is the area under the curve

    Figure  3.  Potential spatial distribution (a) and areas (b) of Carex tussock wetland in Northeast China

    Figure  4.  Jackknife result of Carex tussock wetland distribution prediction in Northeast China. The Jackknife result represents the contribution of each environmental variable on the habitat spatial distribution. ‘BD’ is ‘bulk density’; ‘Bio1’ is ‘annual mean temperature’; ‘Bio11’ is ‘mean temperature of coldest quarter’; ‘Bio14’ is ‘precipitation of driest month’; ‘Bio15’ is ‘precipitation seasonality (coefficient of variation)’; ‘Bio18’ is ‘precipitation of warmest quarter’; ‘Bio3’ is ‘isothermality’; ‘Bio4’ is ‘temperature seasonality’; ‘CEC’ is ‘cation exchange capacity’; ‘Por’ represents ‘soil porosity’; ‘TN’ refers to ‘soil total nitrogen’; ‘TP’ refers to ‘soil total phosphorus’.

    Figure  5.  Response curves of main environment factors for occurrence probability of Carex tussock wetland in Northeast China. Bio3 is isothermality, Bio15 is precipitation seasonality (coefficient of variation).

    Figure  6.  Potential suitable habitats distribution of Carex tussock under future climate scenarios in Northeast China, RCP 2.6, 4.5 and 8.5 refer to radiative forcing reaches 2.6 W/m2, 4.5 W/m2 and 8.5 W/m2 by 2100, respectively

    Figure  7.  Percentage change of potential suitable habitats of Carex tussock under future climate scenarios in Northeast China, RCP 2.6, 4.5 and 8.5 refer to radiative forcing reaches 2.6 W/m2, 4.5 W/m2 and 8.5 W/m2 by 2100, respectively

    Figure  8.  Habitats stability of Carex tussock under future climate scenarios in Northeast China, RCP 2.6, 4.5 and 8.5 refer to radiative forcing reaches 2.6 W/m2, 4.5 W/m2 and 8.5 W/m2 by 2100, respectively

    Table  1.   Bioclimatic variables and its connotation included in Maxent model

    Bioclimatic variablesConnotation
    Bio1 Annual mean temperature
    Bio2 Mean diurnal range (Mean of monthly (max temp-min temp))
    Bio3 Isothermality (Bio2/Bio7) ×100
    Bio4 Temperature seasonality (standard deviation × 100)
    Bio5 Max temperature of warmest month
    Bio6 Min temperature of coldest month
    Bio7 Temperature annual range (Bio5‒Bio6)
    Bio8 Mean temperature of wettest quarter
    Bio9 Mean temperature of driest quarter
    Bio10 Mean temperature of warmest quarter
    Bio11 Mean temperature of coldest quarter
    Bio12 Annual precipitation
    Bio13 Precipitation of wettest month
    Bio14 Precipitation of driest month
    Bio15 Precipitation seasonality (Coefficient of variation)
    Bio16 Precipitation of wettest quarter
    Bio17 Precipitation of driest quarter
    Bio18 Precipitation of warmest quarter
    Bio19 Precipitation of coldest quarter
    下载: 导出CSV

    Table  2.   The shifts of potential suitable habitats of Carex tussock under future climate scenarios

    Climate scenariosYearArea /104 km2Proportion of area / %
    StableLostExpandedStableLostExpanded
    RCP 2.62050s36.2413.274.2529.210.73.4
    2070s32.4517.064.3626.113.73.5
    RCP4.52050s41.757.768.9733.66.27.2
    2070s29.9819.531.1024.115.70.9
    RCP 8.52050s40.778.747.7032.87.06.2
    2070s41.238.2911.8433.26.79.5
    Notes: RCP 2.6, 4.5 and 8.5 refer to radiative forcing reaches 2.6 W/m2, 4.5 W/m2 and 8.5 W/m2 by 2100, respectively
    下载: 导出CSV
  • [1] Bai Guangrun, Wang Shengzhong, Leng Xuetian et al. , 1999. Bio-environmental mechanism of herbaceous peat forming. Acta Geographica Sinica. 54(3): 247−254 (in Chinese).
    [2] Bastiaansen R, Doelman A, Eppinga M B et al., 2020. The effect of climate change on the resilience of ecosystems with adaptive spatial pattern formation. Ecology Letters, 23(3): 414–429. doi:  10.1111/ele.13449
    [3] Beckage B, Osborne B, Gavin D G et al., 2008. A rapid upward shift of a forest ecotone during 40 years of warming in the Green Mountains of Vermont of the United States of America. Proceedings of the National Academy of Sciences, 105(11): 4197–4202. doi:  10.1073/pnas.0708921105
    [4] Bellard C, Bertelsmeier C, Leadley P et al., 2012. Impacts of climate change on the future of biodiversity. Ecology letters, 15(4): 365–377. doi:  10.1111/j.1461-0248.2011.01736.x
    [5] Bertrand R, Lenoir J, Piedallu C et al., 2011. Changes in plant community composition lag behind climate warming in lowland forests. Nature, 479(7374): 517–520. doi:  10.1038/nature10548
    [6] Bonin C L, Zedler J B, 2008. Southern California salt marsh dominance relates to plant traits and plasticity. Estuaries and Coasts, 31(4): 682–693. doi:  10.1007/s12237-008-9057-4
    [7] Cao B, Bai C K, Xue Y et al., 2020. Wetlands rise and fall: six endangered wetland species showed different patterns of habitat shift under future climate change. Science of The Total Environment, 731: 138518. doi:  10.1016/j.scitotenv.2020.138518
    [8] Crain C M, Bertness M D, 2005. Community impacts of a tussock sedge: is ecosystem engineering important in benign habitats? Ecology, 86(10): 2695–2704. doi:  10.1890/04-1517
    [9] Du H B, Liu J, Li M H et al., 2018. Warming-induced upward migration of the alpine treeline in the Changbai Mountains, northeast China. Global Change Biology, 24(3): 1256–1266. doi:  10.1111/gcb.13963
    [10] Fu J, Liu J, Wang X W et al., 2020. Ecological risk assessment of wetland vegetation under projected climate scenarios in the Sanjiang Plain, China. Journal of Environmental Management, 273: 111108. doi:  10.1016/j.jenvman.2020.111108
    [11] Geng X J, Fu Y H, Hao F H et al., 2020. Climate warming increases spring phenological differences among temperate trees. Global Change Biology, 26(10): 5979–5987. doi:  10.1111/gcb.15301
    [12] Gosejohan M C, Weisberg P J, Merriam K E, 2017. Hydrologic influences on plant community structure in vernal pools of Northeastern California. Wetlands, 37(2): 257–268. doi:  10.1007/s13157-016-0863-3
    [13] Han Yuanyuan, Wang Ming, Wang Shengzhong et al., 2018. Characteristics of Soil Enzyme activity of peat bog in Jinchuan, Changbai Mountain. Wetland Science, 16(5): 671–678. (in Chinese). doi:  10.13248/j.cnki.wetlandsci.2018.05.014
    [14] He Wei, Bu Rencang, Xiong Zaiping et al., 2013. Characteristics of temperature and precipitation in Northeastern China from 1961 to 2005. Acta Eclogical Sinica, 33(2): 519–531. (in Chinese). doi:  10.5846/stxb201111241799
    [15] Hunter E A, Raney P A, Gibbs J P et al., 2012. Improving wetland mitigation site identification through community distribution modeling and a patch-based ranking scheme. Wetlands, 32(5): 841–850. doi:  10.1007/s13157-012-0315-7
    [16] Hu W J, Wang Y, Dong P et al., 2020. Predicting potential mangrove distributions at the global northern distribution margin using an ecological niche model: determining conservation and reforestation involvement. Forest Ecology and Management, 478: 118517. doi:  10.1016/j.foreco.2020.118517
    [17] Jin Yinghua, Zhang Yingjie, Xu Jiawei et al., 2018. Comparative assessment of tundra vegetation changes between north and southwest slopes of Changbai Mountains, China, in response to global warming. Chinese Geographical Science, 28(4): 665–679. doi:  10.1007/s11769-018-0978-y
    [18] Johnston C A, Zedler J B, 2012. Identifying preferential associates to initiate restoration plantings. Restoration Ecology, 20(6): 764–772. doi:  10.1111/j.1526-100x.2011.00837.x
    [19] Koncki N G, Aronson M F J, 2015. Invasion risk in a warmer world: modeling range expansion and habitat preferences of three nonnative aquatic invasive plants. Invasive Plant Science and Management, 8(4): 436–449. doi:  10.1614/ipsm-d-15-00020.1
    [20] Lawrence B A, Zedler J B, 2013. Carbon storage by Carex stricta tussocks: a restorable ecosystem service? Wetlands, 33(3): 483–493. doi:  10.1007/s13157-013-0405-1
    [21] Lawrence B A, Zedler J B, 2011. Formation of tussocks by sedges: effects of hydroperiod and nutrients. Ecological Applications, 21(5): 1745–1759. doi:  10.1890/10-1759.1
    [22] Li Xiaojiang, Zhao Chao, Zhou Xinying, 2019a. Vegetation pattern of Northeast China during the special periods since the Last Glacial Maximum. Science China Earth Sciences, 62: 1224–1240. doi:  10.1007/s11430-018-9347-3
    [23] Li Yan, Cao Wei, He Xingyuan et al., 2019b. Prediction of suitable habitat for lycophytes and ferns in Northeast China: a case study on Athyrium brevifrons. Chinese Geographical Science, 29(6): 1011–1023. doi:  10.1007/s11769-019-1085-4
    [24] Li Yingnian, Zhao Xinquan, Zhao Liang et al., 2003. Analysis of vegetation succession and climate change in Haibei alpine marsh in the Qilian Mountains. Journal of Glaciology and Geocryology, 25(3): 243–349. (in Chinese)
    [25] Liu G D, Sun J F, Tian K et al., 2017. Long-term responses of leaf litter decomposition to temperature, litter quality and litter mixing in plateau wetlands. Freshwater Biology, 62(1): 178–190. doi:  10.1111/fwb.12860
    [26] Liu Chao, Huo Hongliang, Tian Luming et al., 2018. Potential geographical distribution of Pyrus calleryana under different climate change scenarios based on the MaxEnt model. Chinese Journal of Applied Ecology, 29(11): 3696–3704. (in Chinese)
    [27] Liu Shuangshuang, Wang Ming, Dong Yanmin et al., 2018. The influence of hummock microtopography on plant litter decomposition in Carex peat mire. Wetland Science, 37(1): 95–102. (in Chinese)
    [28] Liu Yan. 2020. Mechanism and pattern analysis of wetland hydrological connectivity—taking Momoge National Natural as an example. Changchun: Jilin University. (in Chinese)
    [29] Lou Y J, Gao C Y, Pan Y W et al., 2018. Niche modelling of marsh plants based on occurrence and abundance data. Science of the Total Environment, 616: 198–207. doi:  10.1016/j.scitotenv.2017.10.300
    [30] Mao D H, Luo L, Wang Z M et al., 2018. Conversions between natural wetlands and farmland in China: a multiscale geospatial analysis. Science of the Total Environment, 634: 550–560. doi:  10.1016/j.scitotenv.2018.04.009
    [31] Mao Dehua. 2014. Quantitative assessment in the impacts of human activities on net primary productivity of wetlands in the Northeast China. Changchun: Northeast Institute of geography and Agroecology, Chinese Academy of Sciences. (in Chinese)
    [32] Man Xiuling, Cai Tijiu, 2005. Hydrochemical characteristics of three kinds of wetland in Gongbiela Basin. Chinese Journal of Applied Ecology, 16(7): 1335–1340. (in Chinese)
    [33] Menzel A, Sparks T H, Estella N et al., 2006. European phenological response to climate change matches the warming pattern. Global Change Biology, 12(10): 1969–1976. doi:  10.1111/j.1365-2486.2006.01193.x
    [34] Osland M J, González E, Richardson C J, 2011. Restoring diversity after cattail expansion: disturbance, resilience, and seasonality in a tropical dry wetland. Ecological Applications, 21(3): 715–728. doi:  10.1890/09-0981.1
    [35] Pan X L, Zhang D Y, Quan L, 2006. Interactive factors leading to dying-off Carex tato in Momoge wetland polluted by crude oil, Western Jilin, China. Chemosphere, 65(10): 1772–1777. doi:  10.1016/j.chemosphere.2006.04.063
    [36] Peach M, Zedler J B, 2006. How tussocks structure sedge meadow vegetation. Wetlands, 26(2): 322–335. doi:  doi:10.1672/0277-5212(2006)26[322:HTSSMV]2.0.CO;2
    [37] Pearson R G, Stanton J C, Shoemaker K T et al., 2014. Life history and spatial traits predict extinction risk due to climate change. Nature Climate Change, 4(3): 217–221. doi:  10.1038/nclimate2113
    [38] Pecl G T, Araújo M B, Bell J D et al., 2017. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science, 355(6332): eaai9214. doi:  10.1126/science.aai9214
    [39] Phillips S J, Anderson R P, Schapire R E, 2006. Maximum entropy modeling of species geographic distributions. Ecological modelling, 190(3−4): 231–259. doi:  10.1016/j.ecolmodel.2005.03.026
    [40] Piao Shilong, Zhang Xingping, Chen Anping et al., 2019. The impacts of climate extremes on the terrestrial carbon cycle: a review. Science China Earth Sciences, 62(10): 1551–1563. (in Chinese)
    [41] Qi Q, Zhang D J, Tong S Z et al., 2021. The driving mechanisms for community expansion in a restored Carex tussock wetland. Ecological Indicators, 121: 107040. doi:  10.1016/j.ecolind.2020.107040
    [42] Qin A L, Liu B, Guo, Q S et al., 2017. Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch, an extremely endangered conifer from southwestern China. Global Ecology and Conservation, 10: 139–146. doi:  10.1016/j.gecco.2017.02.004
    [43] Rabasa S G, Granda E, Benavides R et al., 2013. Disparity in elevational shifts of European trees in response to recent climate warming. Global Change Biology, 19(8): 2490–2499. doi:  10.1111/gcb.12220
    [44] Riis T, Sand-Jensen K, Larsen S E, 2001. Plant distribution and abundance in relation to physical conditions and location within Danish stream systems. Hydrobiologia, 448(1-3): 217–228. doi:  10.1023/A:1017580424029
    [45] Root T L, Price J T, Hall K R et al., 2003. Fingerprints of global warming on wild animals and plants. Nature, 421(6918): 57–60. doi:  10.1038/nature01333
    [46] Saintilan N, Wilson N C, Rogers K et al., 2014. Mangrove expansion and salt marsh decline at mangrove poleward limits. Global Change Biology, 20(1): 147–157. doi:  10.1111/gcb.12341
    [47] Shen M G, Piao S L, Dorji T et al., 2015. Plant phenological responses to climate change on the Tibetan Plateau: research status and challenges. National Science Review, 2(4): 454–467. doi:  10.1093/nsr/nwv058
    [48] Shen X J, Liu B, Xue Z S et al., 2019. Spatiotemporal variation in vegetation spring phenology and its response to climate change in freshwater marshes of Northeast China. Science of The Total Environment, 666: 1169–1177. doi:  10.1016/j.scitotenv.2019.02.265
    [49] Sun J M, Wu H X, Lu L et al., 2021. Factors associated with spatial distribution of severe fever with thrombocytopenia syndrome. Science of The Total Environment, 750: 141522. doi:  10.1016/j.scitotenv.2020.141522
    [50] Thakur D, Chawla A, 2019. Functional diversity along elevational gradients in the high altitude vegetation of the western Himalaya. Biodiversity and Conservation, 28(8–9): 1977–1996. doi:  10.1007/s10531-019-01728-5
    [51] Thomas C D, Cameron A, Green R E et al., 2004. Extinction risk from climate change. Nature,, 427(6970): 145–148. doi:  10.1038/nature02121
    [52] Thuiller W, Richardson D M, Rouget M et al., 2006. Interactions between environment, species traits, and human uses describe patterns of plant invasions. Ecology, 87(7): 1755–1769. doi:  10.1890/0012-9658(2006)87[1755:ibesta]2.0.co;2
    [53] Tilman D, Clark M., Williams D R et al., 2017. Future threats to biodiversity and pathways to their prevention. Nature, 546(7656): 73–81. doi:  10.1038/nature22900
    [54] Tong Shouzheng, Lv Xianguo, Su Liying et al., 2008. Changing process and the impact factors of wetland ecosystem in Zhaolong Wetland. Wetland Science, 6(2): 179–184. (in Chinese)
    [55] Urban M C, 2015. Accelerating extinction risk from climate change. Science, 348(6234): 571–573. doi:  10.1126/science.aaa4984
    [56] Urbina-Cardona J N, Flores-Villela O, 2010. Ecological-Niche modeling and prioritization of conservation-area networks for Mexican Herpetofauna. Conservation Biology, 24(4): 1031–1041. doi:  10.1111/j.1523-1739.2009.01432.x
    [57] Van der Putten W H, Macel M, Visser M E, 2010. Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels. Philosophical Transactions of the Royal Society B-Biological Sciences, 365(1549): 2025–2034. doi:  10.1098/rstb.2010.0037
    [58] Vetter V M S, Tjaden N B, Jaeschke A et al., 2018. Invasion of a legume ecosystem engineer in a cold biome alters plant biodiversity. Frontiers in Plant Science, 9: 715. doi:  10.3389/fpls.2018.00715
    [59] Walther G R., Post E, Convey P et al., 2002. Ecological responses to recent climate change. Nature, 416(6879): 389–395. doi:  10.1038/416389a
    [60] Wang G D, Middleton B, Jiang M, 2013. Restoration potential of sedge meadows in hand-cultivated soybean fields in Northeastern China. Restoration Ecology, 21(6): 801–808. doi:  10.1111/rec.12015
    [61] Wang G D, Wang M, Lu X G et al., 2015. Effects of farming on the soil seed banks and wetland restoration potential in Sanjiang Plain, Northeastern China. Ecological Engineering, 77: 265–274. doi:  10.1016/j.ecoleng.2015.01.039
    [62] Wang G D, Jiang M, Wang M et al., 2019a. Natural revegetation during restoration of wetlands in the Sanjiang Plain, Northeastern China. Ecological Engineering, 132: 49–55. doi:  10.1016/j.ecoleng.2019.04.001
    [63] Wang Ming, Li Xingli, Dong Yanmin et al., 2021. Plant species diversity of Carex peat mire in Changbai Mountains, China. Chinese Journal of Applied Ecology, 32(06): 2138–2146. doi:  10.13287/j.1001-9332.202106.002
    [64] Wang X H, Zhang D J, Qi Q et al., 2019b. The restoration feasibility of degraded Carex Tussock in soda-salinization area in arid region. Ecological Indicators, 98: 131–136. doi:  10.1016/j.ecolind.2018.08.066
    [65] Wang Zhiliang, Zhang Bai, Zhang Xuezhen et al., 2019c. Using MaxEnt model to guide marsh conservation in the Nenjiang River Basin, Northeast China. Chinese Geographical Science, 29(6): 962–973. doi:  10.1007/s11769-019-1082-7
    [66] Wang Zhenhai, Yin Xiuqin, Song Xueshu, 2014. Diversity of Soil Animals in Marshes of Longwan National Nature Reserve. Wetland Science, 12(05): 566–573. (in Chinese). doi:  10.13248/j.cnki.wetlandsci.2014.05.005
    [67] Wei Y Q, Zhang L, Wang J N et al., 2021. Chinese caterpillar fungus (Ophiocordyceps sinensis) in China: current distribution, trading, and futures under climate change and overexploitation. Science of The Total Environment, 755: 142548. doi:  10.1016/j.scitotenv.2020.142548
    [68] Yao Y, Bera S, Naskar K et al., 2011. A comparative study of mangrove floras in China and India. Forestry Studies in China, 13(3): 173–182. doi:  10.1007/s11632-011-0209-4
    [69] Yi Fuke, 2008. Wild Vascular Plant in Wetlands of Northeast China. Beijing: Science Press. (in Chinese)
    [70] Zhang D J, Qi Q, Tong S Z et al., 2021. Effect of hydrological fluctuation on nutrient stoichiometry and trade-offs of Carex schmidtii. Ecological Indicators, 120: 106924. doi:  10.1016/j.ecolind.2020.106924
    [71] Zhang D J, Tong S Z, Qi, Q et al., 2019a. Effects of drought and re-flooding on growth and photosynthesis of Carex schmidtii Meinsh: implication for tussock restoration. Ecological. Indicators, 103: 134–144. doi:  10.1016/j.ecolind.2019.04.005
    [72] Zhang D J, Qi Q, Tong S Z et al. , 2019b. Soil Degradation effects on plant diversity and nutrient in tussock meadow wetlands. Journal of Soil Science and Plant Nutrition. doi: 10.1007/s42729-019-00052-9
    [73] Zhang Dongjie, 2017. Distribution characteristics and ecological response of Carex tussock to water level in typical wetlands, Northeast China. Changchun: Northeast Institute of geography and Agroecology, Chinese Academy of Sciences. (in Chinese)
    [74] Zhang Dongjie, Wang Xuehong, Tong Shouzheng et al., 2016. Plant diversity in the restored riparian wetlands along the downstream of Songhua River. Wetland Science, 14(6): 883–887. (in Chinese)
    [75] Zhang Lijuan, Li Yanhong, Ren Han et al., 2020. Prediction of the suitable distribution of Cyclobalanopsis glauca and its implications for the northern boundary of subtropical zone of China. Geographical Research, 39(4): 990–1001. (in Chinese)
    [76] Zong Ming, Han Guangxuan, Li Yunzhao et al., 2017. Predicting the potential distribution of dominant species of the coastal wetland in the Yellow River Delta, China using MaxEnt model. Chinese Journal of Applied Ecology, 28(6): 1833–1842. (in Chinese)
    [77] Zong Shengwei. 2014. Mechanism Research on the Vegetation Changes of the Sub-alpine Tundra, Changbai Mountains. Changchun: Northeast Normal University. (in Chinese)
    [78] Zou Y C, Wang L Y, Xue Z S et al., 2018. Impacts of agricultural and reclamation practices on wetlands in the Amur River Basin, Northeastern China. Wetlands, 38(2): 383–389. doi:  10.1007/s13157-017-0975-4
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    [14] ZHAO Junfang YAN Xiaodong JIA Gensuo.  Simulating the net carbon budget of forest ecosystems and its response to climate change in Northeast China using the improved forest carbon budget model FORCCHN . Chinese Geographical Science, 2012, 22(1): 29-41.
    [15] ZHANG Yuehong, WU Shaohong, DAI Erfu, LIU Dengwei, YIN Yunhe.  Identification and Categorization of Climate Change Risks . Chinese Geographical Science, 2008, 18(3): 268-275. doi: 10.1007/s11769-008-0268-1
    [16] LIU Chunlan, XIE Gaodi, HUANG Heqing.  Shrinking and Drying up of Baiyangdian Lake Wetland:A Natural or Human Cause? . Chinese Geographical Science, 2006, 16(4): 314-319.
    [17] WANG Hao, XU Shiguo, SUN Leshi.  Effects of Climatic Change on Evapotranspiration in Zhalong Wetland, Northeast China . Chinese Geographical Science, 2006, 16(3): 265-269.
    [18] WANG Wei-wu, ZHU Li-zhong, WANG Ren-chao, SHI Yong-jun.  ANALYSIS ON THE SPATIAL DISTRIBUTION VARIATION CHARACTERISTIC OF URBAN HEAT ENVIRONMENTAL QUALITY AND ITS MECHANISM—A Case Study of Hangzhou City . Chinese Geographical Science, 2003, 13(1): 39-47.
    [19] 蔡运龙.  VULNERABILITY AND ADAPTATION OF CHINESE AGRICULTURE TO GLOBAL CLIMATE CHANGE . Chinese Geographical Science, 1997, 7(4): 289-301.
    [20] 叶佰生, 陈克恭.  A MODEL SIMULATING THE PROCESSES IN RESPONSES OF GLACIER AND RUNOFF TO CLIMATIC CHANGE─A Case Study of Glacier No. 1 in the ürümqi River, China . Chinese Geographical Science, 1997, 7(3): 243-250.
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出版历程
  • 收稿日期:  2021-01-18
  • 录用日期:  2021-05-12
  • 刊出日期:  2022-01-01

Evolution of Potential Spatial Distribution Patterns of Carex Tussock Wetlands Under Climate Change Scenarios, Northeast China

doi: 10.1007/s11769-022-1260-x
    基金项目:  Under the auspices of the National Natural Science Foundation of China (No. 41871101), the Science and Technology Development Project of Jilin Province (No. 20190201115JC), the ‘Strategic Priority Research Program’ of the Chinese Academy of Sciences (No. XDA23060402).
    通讯作者: TONG Shouzheng. E-mail: tongshouzheng@iga.ac.cn

English Abstract

QI Qing, ZHANG Mingye, TONG Shouzheng, LIU Yan, ZHANG Dongjie, ZHU Guanglei, LYU Xianguo, 2022. Evolution of Potential Spatial Distribution Patterns of Carex Tussock Wetlands Under Climate Change Scenarios, Northeast China. Chinese Geographical Science, 32(1): 142−154 doi:  10.1007/s11769-022-1260-x
Citation: QI Qing, ZHANG Mingye, TONG Shouzheng, LIU Yan, ZHANG Dongjie, ZHU Guanglei, LYU Xianguo, 2022. Evolution of Potential Spatial Distribution Patterns of Carex Tussock Wetlands Under Climate Change Scenarios, Northeast China. Chinese Geographical Science, 32(1): 142−154 doi:  10.1007/s11769-022-1260-x
    • The responses and feedbacks of vegetation to climate change have been brought to the fore of geography, ecology and botany (Shen et al., 2015; Piao et al., 2019). Climate change may substantially change the structure and function of the ecosystem (Thomas et al., 2004; Piao et al., 2019; Bastiaansen et al., 2020; Geng et al., 2020), and affect its stability and biodiversity. Plant spatial distribution is largely determined by climate and environmental conditions (Rabasa et al., 2013; Du et al., 2018). The differences of water, heat and their combinations alter plant growth and distribution pattern by affecting plant physiological and ecological processes (Rabasa et al., 2013; Shen et al., 2015; Du et al., 2018). Many evidences have indicated that climate warming could cause vegetation migration (Root et al., 2003), and increase the risk of species extinction (Bertrand et al., 2011; Bellard et al., 2012; Urban, 2015). Therefore, identify the spatial distribution and evolution characteristics of plant suitable habitats under climate change is of great significance for understanding the response process of ecosystem to climate change, as well as species protection.

      The species distribution models are contributed to ecological risk assessment of the species and habitats. Species distribution models, including Maximum Entropy model (Maxent), Classification and Regression Tree (CART) and Genetic Algorithm for Rule-set Prediction (GARP), etc., are mainly used to predict the potential geographical distribution (Wei et al., 2021). Maxent is statistical modeling based on maximum entropy theory and takes the environmental characteristics of presence species distribution as constraint, and then predicts the potential distribution of the target by finding the probability distribution of maximum entropy under the constraint (Phillips et al., 2006). Due to its accurate prediction, easy analyses and simple approaches, Maxent model is widely used to predict the reserve construction (Urbina-Cardona and Flores-Villela, 2010; Wang et al., 2019c), the restoration and protection of endangered species (Qin et al., 2017) and the diseases spatial distribution (Sun et al., 2021), as well as the impact of climate change on wetland species distribution (Cao et al., 2020; Hu et al., 2020).

      Carex tussock wetlands are widely distributed in floodplain wetlands and mountain valley wetlands in Northeast China (Zhang, 2017). Protruding Carex tussocks increase the surface area, roughness and micro-geomorphological heterogeneity (Lawrence and Zedler, 2011), which provides support for biodiversity (Crain and Bertness, 2005; Peach and Zedler, 2006; Johnston and Zedler, 2012). Besides, Carex tussocks have many ecological functions including pollutant adsorption, nutrient accumulation, and biological carbon sequestration (Lawrence and Zedler, 2013). Research showed that the aboveground carbon pools of Carex tussock wetland (16.5–27.5 Mg /ha, C) was significantly higher than that of other herbaceous systems (Lawrence and Zedler, 2013). In particular, the unique hummock structure formed by Carex root tillering makes a significant contribution to the formation of herbaceous peat (Bai et al., 1999).

      However, since 1961, an obviously rising trend of annual temperature and decreasing trend of precipitation was found in Northeast China (He et al., 2013). According to the survey, nearly 72% of the existing wetlands in Northeast China are threatened by different factors, resulting in a decline in ecological function (Mao et al., 2018), and the variation of precipitation fluctuation is one of the important factors leading to the high risk of regional wetlands (Fu et al., 2020). Previous study shows that alpine swamp meadow would succession to typical meadow under climate drying and warming (Li et al., 2003). Coupled with the influence of human interference (grazing, mowing, reclamation and ditching, etc.,), large area of Carex tussock wetland has been degraded or disappeared (Pan et al., 2006; Wang et al., 2019b) in Northeast China. Therefore, it is necessary to protect and restore Carex tussock wetland to exert its ecological functions. What are the spatial distribution characteristics of Carex tussock wetland in Northeast China? Where will the Carex tussock wetland be distributed in the future climate scenario? What is the stability of the potential suitable area of Carex tussock wetland? These questions need to be answered scientifically. Based on the above situation, in this study we used Maxent model to determine the evolution of potential distribution pattern of Carex tussock wetland under different climate scenarios. Our objectives are to: 1) identify the potential geographical distribution of Carex tussock wetlands under climate change, 2) explore the main environmental factors that affect the distribution of Carex tussocks, and 3) determine the patterns of habitat shifts and stability for the Carex tussock. This study provides theoretical support for the ecological conservation and targeted management of Carex tussock wetlands in Northeast China and has important reference value for the study of wetland stability.

    • This study focused on the Northeast China (38°43′N–53°33′N, 115°31′E–135°5′E), which includes Heilongjiang Province, Jilin Province, Liaoning Province and the eastern Inner Mongolia (Fig. 1). According to the topography and climatic conditions, the Northeast China include seven ecological functional regions: Eastern Inner Mongolia Plateau, Da Hinggan Mountains, Xiao Hinggan Mountains, Changbai Mountains, Sanjiang Plain, Songnen Plain and Liaohe Plain (Shen et al., 2019). And the Northeast China belongs to the temperate monsoon climate with an annual precipitation of 300−1000 mm (Li et al., 2019a). Due to the influence of complex topography, the climate in different ecological functional regions is significantly different. Wetlands in Northeast China are widely developed and of various types. The main plants include Phragmites communis, Typha orientalis, Carex lasiocarpa, etc. (Mao, 2014).

      Figure 1.  Location of study area and Carex tussock wetland distribution

    • Carex tussocks are developed in wet valley wetlands and flooded wetlands, which are the key symbols of Carex tussock wetlands. In this study, the distribution of Carex tussock was used to predict the potential spatial distribution pattern of Carex tussock wetland under future climate scenarios. The distribution information of Carex tussock was obtained according to the records of Wild Vascular Plant in Wetlands of Northeast China (Yi, 2008), the academic researches (Man and Cai, 2005; Wang et al., 2014; Wang et al., 2015; Zhang et al., 2016; Zhang, 2017; Han et al., 2018; Liu et al., 2018; Lou et al., 2018; Wang et al., 2019a; Wang et al., 2021) and the filed investigation (2016−2019). We deleted the duplicate records and ensured that there was only one distribution point in the 1 km ×1 km range, and finally screened out 68 valid samples for building the Maxent model (Fig. 1). Among them, 58 samples (85%) came from the related researches and filed investigation, and the other 10 samples (15%) were the distribution sites of tussock-forming species (Carex appendiculata, Carex schmidtii, Carex meyeriana) recorded in Wild Vascular Plant in Wetlands of Northeast China and they were also verified by historical record such as news reports, meeting reports, memoirs, expert experience and wetland park website that there were Carex tussock developed here. The Carex tussock occurrence records are derived from 1958 to 2019, and about 18% samples were recorded before 2000.

    • 28 environmental variables, including climate, topography and soil properties, which affect the distribution of Carex tussock were selected to simulate the potential spatial distribution of Carex tussock (Zong, 2014; Zhang, 2017; Wang et al., 2019b; Zhang et al., 2019b). Among them, the climate data including 19 bioclimatic factors (Bio1 to Bio19, Table 1) were downloaded from the WorldClim Database (https://www.worldclim.org/) under current (1950–2000) and future conditions (2050s and 2070s) of different climate scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) with resolution of 30'' (about 1 km). RCP is the representative concentration pathway, and RCP 2.6, 4.5 and 8.5 refer to radiative forcing reaches 2.6 W/m2, 4.5 W/m2 and 8.5 W/m2 by 2100, respectively. The current bioclimatic data is only up to the year 2000, and the future distribution under climatic conditions in 2050s (2040–2060) and 2070s (2060–2080) were modeled using environmental factors generated by BCC_CSM1–1 climate model (CMIP 5 data). The topography data were derived from Geospatial Data Cloud ASTERGDEM 30M digital elevation data (DEM) (http://www.gscloud.cn), and the slope and aspect were calculated by ArcGIS 10.2. (ESRI, USA) Soil variables (physical and chemical properties) were downloaded from the Soil Database of China for Land Surface Modeling (http://globalchange.bnu.edu.cn/research/soil2), including soil pH, bulk density (BD), soil porosity (Por), cation exchange capacity (CEC), total nitrogen (TN) and total phosphorus (TP). In order to maintain the comparability of the time series of the model and analyze the potential spatial distribution of Carex tussock wetlands under different climatic scenarios, soil properties and topography factors remain unchanged in the prediction in the future.

      Table 1.  Bioclimatic variables and its connotation included in Maxent model

      Bioclimatic variablesConnotation
      Bio1 Annual mean temperature
      Bio2 Mean diurnal range (Mean of monthly (max temp-min temp))
      Bio3 Isothermality (Bio2/Bio7) ×100
      Bio4 Temperature seasonality (standard deviation × 100)
      Bio5 Max temperature of warmest month
      Bio6 Min temperature of coldest month
      Bio7 Temperature annual range (Bio5‒Bio6)
      Bio8 Mean temperature of wettest quarter
      Bio9 Mean temperature of driest quarter
      Bio10 Mean temperature of warmest quarter
      Bio11 Mean temperature of coldest quarter
      Bio12 Annual precipitation
      Bio13 Precipitation of wettest month
      Bio14 Precipitation of driest month
      Bio15 Precipitation seasonality (Coefficient of variation)
      Bio16 Precipitation of wettest quarter
      Bio17 Precipitation of driest quarter
      Bio18 Precipitation of warmest quarter
      Bio19 Precipitation of coldest quarter

      All environmental datasets were re-projected and unified into GCS_WGS_1984 coordinate system with a spatial resolution of 30″ using ArcGIS 10.2. Moreover, in order to avoid the model over-fitting that caused by environmental variables multi-collinearity, person correlation analysis of the environmental variables was conducted by SPSS 23 (IL, USA). When the correlation coefficient is ≥ 0.8, the environmental factors with small biological significance and low contribution will be excluded (Liu et al., 2018). Finally, 16 environmental variables including Bio1, Bio3, Bio11, Bio14, Bio15, Bio18, aspect, cation exchange capacity (CEC), elevation, pH, soil porosity (SP), slope, soil total nitrogen (TN), were selected for model building.

    • The model of the potential spatial distribution of Carex tussock wetland was built in Maxent 3.4.1 (https://biodiversityinformatics.amnh.org/open_source/maxent/), 75% of the occurrence data were randomly selected as the training set to build the model, and the remaining 25% points were used as the test set for model verification. The options of ‘Create response curve’ and ‘Do jackknife to measure variable importance’ were selected to test the contribution rate of environmental variables to the distribution of Carex tussock. The accuracy of model prediction was evaluated by the area under the curve (AUC) value of the receiver operator characteristics (ROC) plot. The AUCs (0.5–1.0) with high values refers to accurate results: an AUC value in 0.9–1.0 represents excellent model performance, in 0.8–0.9 represents good performance, in 0.7–0.8 represents fair, in 0.6–0.7 represents poor and in 0.5–0.6 represents fail (Thuiller et al., 2006).

    • The occurrence probability grid map of Carex tussock was obtained by ArcGIS 10.2 software to visually analyze the simulation results. The occurrence probability (P) value is in the range of 0–1.0, and the higher P value is the higher probability of the existence of Carex tussock. The habitat suitability of Carex tussock was classified into 4 classes: unsuitable (0 < P < 0.2), low suitable (0.2 ≤ P < 0.4), moderate suitable (0.4 ≤ P <0.6) and high suitable (0.6 ≤ P <1.0). By comparing the ranges of suitable distribution regions under current and future scenarios, the shifts of distribution regions were obtained by mask extraction and then the Carex tussock habitats were divided into stable region, expanded region and lost region. Finally, we calculated the habitat areas (CTSi) by the following formula:

      $$ {CTS}_{i}=\frac{{N}_{i}}{{N}_{{\rm{total}}}}\times {S}_{{\rm{total}}} $$ (1)

      where CTSi is the habitat area of Carex tussock, i is the habitat category, Ni and Ntotal denote the pixel number of habitat i and total study area, respectively, and Stotal denotes the total area of our study.

    • Maxent model performed well at predicting the potential spatial distribution of Carex tussock wetland with AUC values of 0.861 (training data) and 0.891 (test data) respectively (Fig. 2), which indicate ‘good’ (AUC = 0.8–0.9) model performance. The current potential spatial distribution pattern (Fig. 3) indicates that the potential suitable habitats of Carex tussock are mainly distributed in the Sanjiang Plain, Songnen Plain, Changbai Mountains and Hinggan Mountains, with a total area of 49.4 × 104 km2. The high suitable region is about 5.7 × 104 km2, accounting for 4.6% of the total area of Northeast China, which concentrated in Sanjiang Plain, Changbai Mountains and along the rivers in the Songnen Plain, and sporadically distributed in Da Hinggan Mountains.

      Figure 2.  The receiver operator characteristics (ROC) curve in the Maxent model predictions for Carex tussock wetland in Northeast China. AUC is the area under the curve

      Figure 3.  Potential spatial distribution (a) and areas (b) of Carex tussock wetland in Northeast China

    • Based on the Jackknife module of Maxent model, the contribution rate of environmental variables was tested, and results showed that isothermality (Bio3), precipitation seasonality (coefficient of variation) (Bio15) and elevation had greater gains on the prediction results (Fig. 4), indicating that the distribution probability of Carex tussock was sensitive to these factors. Furthermore, the response curve of Carex tussock to main environmental factors exhibited that with the increase of Bio3 and Bio15, the presence probability of Carex tussock decreased step by step. Additionally, the influence of elevation is shown in Fig. 5, which indicates that Carex tussock would prefer lower elevation and maintain at low presence probability when the altitude was higher than 180 m. As the presence probability of Carex tussock ≥ 0.6 represents the high suitable habitat, the Bio3 ranges from 18.4 to 21.5, the Bio15 ranges from 65.7 to 93.9, and the elevation is 23.5–107.1 m.

      Figure 4.  Jackknife result of Carex tussock wetland distribution prediction in Northeast China. The Jackknife result represents the contribution of each environmental variable on the habitat spatial distribution. ‘BD’ is ‘bulk density’; ‘Bio1’ is ‘annual mean temperature’; ‘Bio11’ is ‘mean temperature of coldest quarter’; ‘Bio14’ is ‘precipitation of driest month’; ‘Bio15’ is ‘precipitation seasonality (coefficient of variation)’; ‘Bio18’ is ‘precipitation of warmest quarter’; ‘Bio3’ is ‘isothermality’; ‘Bio4’ is ‘temperature seasonality’; ‘CEC’ is ‘cation exchange capacity’; ‘Por’ represents ‘soil porosity’; ‘TN’ refers to ‘soil total nitrogen’; ‘TP’ refers to ‘soil total phosphorus’.

      Figure 5.  Response curves of main environment factors for occurrence probability of Carex tussock wetland in Northeast China. Bio3 is isothermality, Bio15 is precipitation seasonality (coefficient of variation).

    • Under different climatic scenarios, the high suitable and moderate suitable habitats of Carex tussock are generally consistent with the current distribution pattern (Fig. 6), which are also concentrated in the Sanjiang Plain, and relatively scattered in the Songnen Plain, Changbai Mountains and Da Hinggan Mountains, while the spatial distribution pattern of low suitable habitats exhibit greatly changes. Besides, it is found that the area changes of low suitable habitat are higher than that of the moderate and high suitable habitat (Fig. 7). Under RCP 2.6, the total suitable habitat of Carex tussock decreased by 18.2% and 25.6% in 2050s and 2070s, respectively. Under RCP 4.5, the total suitable habitat had a slightly increase in 2050s, while it was significantly decreased in 2070s, especially the low suitable habitat decreased by 42.7%. As for RCP 8.5 scenario, the reduction of moderate and high suitable habitats in 2050s resulted in a decrease of 2.1% of the total suitable habitat. Although the moderate and high suitable habitats decreased in 2070s, the total suitable habitat still increased due to the larger area and increase of the low suitable habitat (Fig. 7).

      Figure 6.  Potential suitable habitats distribution of Carex tussock under future climate scenarios in Northeast China, RCP 2.6, 4.5 and 8.5 refer to radiative forcing reaches 2.6 W/m2, 4.5 W/m2 and 8.5 W/m2 by 2100, respectively

      Figure 7.  Percentage change of potential suitable habitats of Carex tussock under future climate scenarios in Northeast China, RCP 2.6, 4.5 and 8.5 refer to radiative forcing reaches 2.6 W/m2, 4.5 W/m2 and 8.5 W/m2 by 2100, respectively

    • The stable suitable habitats of Carex tussock are mainly distributed in the Sanjiang Plain, Songnen Plain and Changbai Mountains in the future climate scenarios (except for RCP 2.6 in 2070s) (Fig. 8), which is generally consistent with current spatial distribution pattern. In addition, the area of stable habitat (29.98 × 104−41.75 ×104 km2) is significantly higher than that of expaned region and lost region (Table 2, P < 0.01), indicating that the potential suitable habitat of Carex tussock is relatively stable.

      Figure 8.  Habitats stability of Carex tussock under future climate scenarios in Northeast China, RCP 2.6, 4.5 and 8.5 refer to radiative forcing reaches 2.6 W/m2, 4.5 W/m2 and 8.5 W/m2 by 2100, respectively

      Table 2.  The shifts of potential suitable habitats of Carex tussock under future climate scenarios

      Climate scenariosYearArea /104 km2Proportion of area / %
      StableLostExpandedStableLostExpanded
      RCP 2.62050s36.2413.274.2529.210.73.4
      2070s32.4517.064.3626.113.73.5
      RCP4.52050s41.757.768.9733.66.27.2
      2070s29.9819.531.1024.115.70.9
      RCP 8.52050s40.778.747.7032.87.06.2
      2070s41.238.2911.8433.26.79.5
      Notes: RCP 2.6, 4.5 and 8.5 refer to radiative forcing reaches 2.6 W/m2, 4.5 W/m2 and 8.5 W/m2 by 2100, respectively

      In the future climate condition, the expanded/lost habitats (i.e. unstable habitats) of Carex tussock mainly distributed in Da Hinggan Mountains and Xiao Hinggan Mountains. Changbai Mountains also exhibits an obvious shrinking trend under RCP 2.6 in 2070s. Moreover, the largest habitat loss occurred in RCP 4.5 in 2070s, with an area loss of 19.53 × 104 km2 and wildly distributed in Northeast China. And the suitable habitat of Carex tussock in RCP 4.5 (2050s) and RCP 8.5 exhibit more obvious expansion than that in RCP 2.6, and the expanded area can reach up to 9.5% of the total area of Northeast China (Fig. 8, Table 2), which is mainly distributed in Da Hinggan Mountains and Xiao Hinggan Mountains.

    • Maxent model has great applicability in prediction of wetland species distribution (Li et al., 2019b; Hu et al., 2020; Liu 2020) and the natural reserve construction (Hunter et al., 2012; Wang et al., 2019c) with the AUC value is 0.870–0.998. In this study, the model is used to predict the potential spatial distribution of Carex tussock wetland, the AUC value is 0.8−0.9 and the prediction results are consistent with the field observation, indicating that the Maxent model provide a good performance.

    • The presence of plant composition and distribution are the results of complex interaction of physiological and ecology tolerances in response to bioclimate, soil, topography, biology, etc. (Bonin and Zedler, 2008; Van der Putten et al., 2010; Osland et al., 2011; Saintilan et al., 2014; Zhang et al., 2020). In our study, the Maxent model shows that isothermality, seasonal precipitation variation and elevation are the main environmental factors affecting the distribution of Carex tussock (Fig. 4). The presence probability of Carex tussock exhibited decreasing trend with the increase of isothermal and seasonal precipitation variation. This was consistent with the research of Yao et al. (2011) and Hu et al. (2020), who reported that the temperature affected the distribution of wetland plant the most, and rainfall could determine its succession. The vertical redistribution of water-heat conditions and the effect of altitude on water availability of wetland plants affect the distribution pattern of plants. However, in costal wetland, salt and nitrogen are the main factors affect the dominance species distribution (Zong et al., 2017). Carex is typical freshwater wetland plant and its growth and distribution are influenced by wetland water conditions (Lawrence and Zedler et al., 2011; Zhang, 2017). The function characters, physiological processes and element contents of Carex are significantly different under fluctuation or stable hydrological conditions (Zhang et al., 2019a; 2021). Compared with long-term drought, flooding and water fluctuation are beneficial to the formation of Carex tussock (Lawrence and Zedler et al., 2011). Precipitation supplied freshwater and decreased salinity (Hu et al., 2020) and was therefore improved soil water conditions, which could increase the feasibility of restoration of degraded Carex tussock wetlands in semiarid areas (Wang et al., 2019c).

      Generally, methods of plant distribution modeling mainly use climate, topography and soil variables in regional scale studies (Koncki et al., 2015; Cao et al., 2020; Hu et al., 2020). However, the distributions of wetland plant species are also affected by microtopography, water depth and distance to open water surface in wetland ecosystems (Riis et al., 2001; Gosejohan et al., 2017; Lou et al., 2018). According to the study of Lou et al. (2018), due to the different optima and niche width, wetland plant species show different distribution patterns according to the hydrological conditions.

      Water level and hydrological fluctuation period are important ecological factors affecting the formation and development of Carex tussock (Zhang et al., 2019a), however in the regional study, it is hardly to use the changes of water level (centimeter precision) as an important environmental factor in the construction of the model since the grid resolution is usually in the range of meters to kilometers. Besides, in this study, three groups of environmental factors were integrated during modeling, including bioclimate, soil properties and topography, but did not consider the influence of human disturbance on the spatial distribution of Carex tussock. In the past 30 to 50 yr, human disturbance including farmland, reclamation, grazing, cutting and burning has led to a large area of Carex tussock wetlands degraded or disappeared in Northeast China (Pan et al., 2006; Mao et al., 2018; Zou et al., 2018), and ditches and roads blocked the hydrological connectivity of wetlands, resulting in the decline of ecological functions (Tong et al., 2008). The destruction of hydrological connectivity caused by human activities is the main reason that affects the spatial distribution of Grus leucogeranus (Liu, 2020). Therefore, in the study of small-scale wetland species distribution, it would be significant to take the hydrological characteristic and human activity into consideration during modeling.

    • Climate change is expected to have a profound impact on species distribution, richness and diversity (Pecl et al., 2017; Tilman et al., 2017). The impact of global warming on species habitats is uncertain. Some of researches have indicated that with the rise of global temperature, the decreased of species suitable habitats will lead to an increase in risk of species extinction (Pearson et al., 2014; Urban, 2015). However, the study of endangered wetland species distribution in low latitude shows that species habitat shifts have the trends of increase and unstable (Cao et al., 2020). In this study, the stable habitat is generally consistent with current spatial distribution pattern (Fig. 8), and the stable habitat in the future climate is significantly larger than the lost habitat and expanded habitat (Table 2). Furthermore, the lost/expanded habitat is mainly the low suitable habitat, while the moderate and high suitable habitats fluctuate in a small range (Fig. 7), indicating that the Carex tussock habitat is relatively stable. This may result from the differences in ecological amplitude of species in different latitudes, many studies have confirmed that plant in high latitudes and high elevations are more sensitive to temperature rise (Liu et al., 2017; Thakur and Chawla, 2019), resulting in differences in response of species habitat stability to climate warming.

      Numerous evidences have suggested that species adapt to global warming by adjusting their physiological and ecological characteristic (Walther et al., 2002; Menzel et al., 2006; Shen et al., 2015) or migrating to high latitudes or high elevations (Du et al., 2018; Vetter et al., 2018), especially in alpine-plateau ecosystems and polar-subpolar ecosystems. The loss and expansion of Carex tussock wetland in high altitude and high latitude is the response of plant distribution to global warming, which is consistent with previous studies (Beckage et al., 2008; Rabasa et al., 2013; Jin et al., 2018). The Carex tussock habitats have an obviously trend of shrinks under RCP 2.6 and RCP 4.5, and the lost habitats are mainly distributed in north of Da Hinggan Mountains and Xiao Hinggan Mountains and Changbai Mountains (Fig. 8). Additionally, the expansion of Carex tussock is more prominent in medium and high emission scenarios, and expanded habitats are mainly distributed in Da Hinggan Mountains and Xiao Hinggan Mountains, which indicates that the Carex tussock wetlands in high latitude and high altitude are more sensitive to climate change.

    • In recent years, with the strengthening of wetland protection in China, the State Forest Administration, the National Development and Reform Commission and the Ministry of Finance jointly issued the ‘National Wetland Protection 13th five-year Plan’ to open up the comprehensive protection of wetlands (http://www.gov.cn/xinwen/2017-04/20/content_5187584.htm). In addition, with the improvement of public awareness of environmental protection and the strengthening of scientific research, wetlands protection and restoration are gradually deepening. At present, wetland conservation teams have developed a series of restoration techniques for Carex tussock wetland, including seed bank technology (Wang et al., 2013; 2015), hydro-regulatory techniques (Wang et al., 2019b; Zhang et al., 2019a) and rhizome clonal propagation technology (Qi et al., 2021), which is of great significance for Carex tussock habitat protection and restoration. And the guidance of national policy will help us to actively deal with the impact of climate change on wetland plant species in the future.

    • Current potential suitable habitat of Carex tussock is about 49.4 × 104 km2 in Northeast China, which is mainly distributed in the Sanjiang Plain, Songnen Plain, Changbai Mountains and Hinggan Mountains. High suitable habitat of Carex tussock is about 5.7 ×104 km2, concentrated in the Sanjiang Plain, Changbai Mountains and along the rivers in the Songnen Plain, and sporadically distributed in Da Hinggan Mountains. Under future climatic scenarios, the spatial distribution pattern is generally consistent with current, the moderate and high suitable habitats are also concentrated in the Sanjiang Plain, while the low suitable habitat shifts greatly. Under the future climate scenario, the area of high, moderate and low suitable habitats are mainly reduced. Isothermality, seasonal precipitation variability and altitude are the main environmental factors affecting the distribution of Carex tussock wetland. The area of stable habitat is obviously higher than that of the lost and expanded habitat. And the lost and expanded habitats mainly occur in Da Hinggan Mountains, Xiao Hinggan Mountains and Changbai Mountains, indicating that Carex tussock wetlands at high altitude and high latitude are more sensitive to climate change. Therefore, more attention should be invested in the habitat protection of Carex tussock at high latitude and high altitude. This study revealed the potential distribution and ecological stability under climate change, which is reference that could be applied to sustainable tussock wetland management.

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