Impacts of climate change and human activities on saline vegetation: An example of <i>Tamarix chinensis</i>

GUO Fuyin; LIU Xiaohuang; ZHAO Xiaofeng; Zulpiya·Mamat; WANG Yugang; LIN Tao; ZHAO Chuanyan; XING Liyuan; WANG Ran; ZHAO Honghui; WANG Chao; ZHOU Zhiluo; XU Yibo

doi:10.12029/gc20240407001

2025 Vol. 52, No. 4

Article Contents

Article navigation > Geology in China > 2025 > 52(4): 1425-1438

Next Article Previous Article

GUO Fuyin, LIU Xiaohuang, ZHAO Xiaofeng, Zulpiya·Mamat, WANG Yugang, LIN Tao, ZHAO Chuanyan, XING Liyuan, WANG Ran, ZHAO Honghui, WANG Chao, ZHOU Zhiluo, XU Yibo. 2025. Impacts of climate change and human activities on saline vegetation: An example of Tamarix chinensis[J]. Geology in China, 52(4): 1425-1438. doi: 10.12029/gc20240407001

Citation:

GUO Fuyin, LIU Xiaohuang, ZHAO Xiaofeng, Zulpiya·Mamat, WANG Yugang, LIN Tao, ZHAO Chuanyan, XING Liyuan, WANG Ran, ZHAO Honghui, WANG Chao, ZHOU Zhiluo, XU Yibo. 2025. Impacts of climate change and human activities on saline vegetation: An example of Tamarix chinensis[J]. Geology in China, 52(4): 1425-1438. doi: 10.12029/gc20240407001

Impacts of climate change and human activities on saline vegetation: An example of Tamarix chinensis

1.
Key Laboratory of Natural Resource Coupling Process and Effects, Beijing 100055, China
2.
College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 830017, Xinjiang, China
3.
Natural Resources Comprehensive Survey Command Center, China Geological Survey, Beijing 100055, China
4.
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
5.
Xinjiang Land Consolidation and Rehabilitation, Urumqi 830002, Xinjiang, China
6.
College of Pastoral Agriculture Science And Technology, Lanzhou University, Lanzhou 730020, Gansu, China
7.
College of Ecology, Hainan University, Haikou 570228, Hainan, China
8.
School of Economics and Management, China University of Geosciences (Beijing), Beijing 100083, China

Fund Project: Supported by the projects of China Geological Survey (No.DD20230112, No.DD20230514), the First Regional Geological Survey Team of the Xinjiang Uygur Autonomous Region Bureau of Geology, Mineral Resources Exploration and Development (No.S24-XJ001, No.LTSJ-ZFCG-2024-021), and the Science and Technology Innovation Fund Project of the Natural Resources Comprehensive Survey Command Center, China Geological Survey (No.KC20230003).

More Information

Author Bio: GUO Fuyin, male, born in 1997, master candidate, mainly engaged in the research of geographic information and ecological remote sensing; E-mail: yinfunan@163.com
Corresponding author: LIU Xiaohuang, male, born in 1972, professor lever senior engineer, mainly engaged in the research of observation and natural resources; E-mail:liuxh19972004@163.com.

Received Date: 07 April 2024

Revised Date: 26 September 2024

Publish Date: 25 July 2025

Abstract

Abstract

This paper is the result of geological survey engineering.
Objective
The widely distributed and complex genesis of saline soils in China deeply affects the sustainable development of regional ecosystems. Climate change and human activities exacerbate the ecological uncertainty of saline ecosystems in these regions. It is crucial to characterize the distribution of saline vegetation affected by climate change and human activities and explore potential distribution areas to maintain the ecological security of saline regions.
Methods
In this study, the ecological niche model of T. chinensis, a typical saline vegetation, was established using the MaxEnt model optimized by the ENMeval package. Based on 371 T. chinensis distribution data and 13 environmental variables, the potential distribution areas of T. chinensis in different climate scenarios in the present and future were simulated. The key factors constraining the potential geographic distribution of T. chinensis in the present era were assessed through the contribution of variables and the Jackknife test. Ultimately, a quantitative assessment of the potential area and size at risk of T. chinensis was conducted.
Results
(1) RM＝1.5 and FC=LQHPT are the optimal model parameters. The average value of AUC is 0.906±0.005, and the model prediction results have high credibility. The potentially suitable area of modern T. chinensis under the influence of environmental factors is 32.69×105 km², and the suitable area under the influence of human activities is 28.48×105 km². (2) The modern highly suitable area for T. chinensis is concentrated in Xinjiang, northeastern Gansu, western Inner Mongolia, northern Ningxia, southeastern Beijing, Tianjin, eastern Hebei, and northern Shandong. Mean annual temperature, coefficient of seasonal variation of temperature, alkalinity, distance from rivers, and average annual sunshine hours are the main environmental factors governing the distribution of T. chinensis. (3) Geographic range of tamarisk varies with time. The mean center of T. chinensis distribution under the future climate scenario moves towards southeastward, and Xinjiang, Gansu, western Inner Mongolia, northern Qinghai, western Hebei, and northern Shandong are stable and suitable areas for T. chinensis.
Conclusion
As global warming intensifies and human activities continue to increase, this study is relevant to the conservation of T. chinensis under various climate scenarios in the future.
- Tamarix chinensis Lour /
- MaxEnt /
- future climate /
- geographic distribution /
- potential habitat areas /
- geological survey engineering

FullText(HTML)

References

[1]	Ahmadi M, Hemami M-R, Kaboli M, Shabani F. 2023. MaxEnt brings comparable results when the input data are being completed; Model parameterization of four species distribution models[J]. Ecology and Evolution, 13(2): e9827. doi: 10.1002/ece3.9827 CrossRef Google Scholar
[2]	Boivin N L, Zeder M A, Fuller D Q, Crowther A, Larson G, Erlandson J M, Denham T, Petraglia M D. 2016. Ecological consequences of human niche construction: Examining long-term anthropogenic shaping of global species distributions[J]. Proceedings of the National Academy of Sciences, 113(23): 6388−6396. doi: 10.1073/pnas.1525200113 CrossRef Google Scholar
[3]	Chen Wudi, Liu Xiaohuang, Li Hongyu, Sun Xingli, Wang Yugang, Liu Xiaojie, Xing Liyuan, Wang Ran, Luo Xinping, Wang Chao, Zhao Honghui. 2024. Ecosystem service function and security pattern of Tianshan Mountains in Xinjiang from 1990 to 2050[J]. Geology in China, 51(5): 1644−1663 (in Chinese with English abstract). Google Scholar
[4]	Fick S E, Hijmans R J. 2017. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas[J]. International Journal of Climatology, 37(12): 4302−4315. doi: 10.1002/joc.5086 CrossRef Google Scholar
[5]	Fang Ouya, Jia Hengfeng, Qiu Hongyan, Ren Haibao. 2017. Age of arboreous Tamarix austromongolica and its growth response to environment in Tongde County of Qinghai, China[J]. Journal of Plant Ecology, 41(7): 738−748 (in Chinese with English abstract). Google Scholar
[6]	Fu Yujia, Tan Changhai, Liu Xiaohuang, Sun Xingli, Yuan Zemin, Zheng Yiwen. 2022. Definition, classification, observation and monitoring of natural resources and their application in territorial planning and governance[J]. Geology in China, 49(4): 1048−1063 (in Chinese with English abstract). Google Scholar
[7]	Han Fugui, Man Duoqing, Zheng Qingzhong, Xiao Bin, Zhang Yunian, Fu Guiquan, Du Juan. 2022. Species diversity of Tamarix Ramosissima communities in different habitats and the relationship between soil moisture and salinity in the middle and lower reaches of Shiyang River Basin[J]. Research of Soil and Water Conservation, 29(3): 49−56 (in Chinese with English abstract). Google Scholar
[8]	Guo J R, Shan C D, Zhang Y F, Wang X L, Tian H Y, Han G L, Zhang Y, Wang B S. 2022. Mechanisms of salt tolerance and molecular breeding of salt-tolerant ornamental Plant[J]. Frontiers in Plant Science, 13: 854116−854116. doi: 10.3389/fpls.2022.854116 CrossRef Google Scholar
[9]	Jones P J, Williamson G J, Bowman D M J S, Lefroy E C. 2019. Mapping Tasmania's cultural landscapes: Using habitat suitability modelling of archaeological sites as a landscape history tool[J]. Journal of Biogeography, 46(11): 2570−2582. doi: 10.1111/jbi.13684 CrossRef Google Scholar
[10]	Lepore C, Abernathey R, Henderson N, Allen J T, Tippett M K. 2021. Future global convective environments in CMIP6 Models[J]. Earth's Future, 9(12): e2021EF002277. doi: 10.1029/2021EF002277 CrossRef Google Scholar
[11]	Li S, Wang Z S, Zhu Z X, Tao Y Z, Xiang J. 2023. Predicting the potential suitable distribution area of Emeia pseudosauteri in Zhejiang Province based on the MaxEnt model[J]. Scientific Reports, 13(1): 1806. doi: 10.1038/s41598-023-29009-w CrossRef Google Scholar
[12]	Li Y C, Li M Y, Li C, Liu Z Z. 2020. Optimized maxent model predictions of climate change impacts on the suitable distribution of cunninghamia lanceolata in China[J]. Forests, 11(3): 302. doi: 10.3390/f11030302 CrossRef Google Scholar
[13]	Li Cangbai, Xiao Keyan, Li Nan, Song Xianglong, Zhang Shuai, Wang Kai, Chu Wenkai, Cao Rui. 2020. A comparative study of support vector machine, random forest and artificial neural network machine learning algorithms in geochemical anomaly information extraction[J]. Acta Geoscientica Sinica, 41(2): 309−319 (in Chinese with English abstract). Google Scholar
[14]	Liu G D, Wei M H, Yang Z, Xiao H Y, Zhang Y H, Fang N N. 2023. Relationship between spatio-temporal evolution of soil pH and geological environment/surface cover in the eastern Nenjiang River Basin of Northeast China during the past 30 years[J]. China Geology, 6(3): 369−382. Google Scholar
[15]	Merow C, Smith M J, Silander J A Jr. 2013. A practical guide to MaxEnt for modeling species' distributions: What it does, and why inputs and settings matter[J]. Ecography, 36(10): 1058−1069. doi: 10.1111/j.1600-0587.2013.07872.x CrossRef Google Scholar
[16]	Pecl G T, Araújo M B, Bell J D, Blanchard J, Bonebrake T C, Chen I C, Clark T D, Colwell R K, Danielsen F, Evengård B, Falconi L, Ferrier S, Frusher S, Garcia R A, Griffis R B, Hobday A J, Janion-Scheepers C, Jarzyna M A, Jennings S, Lenoir J, Linnetved H I, Martin V Y, Mccormack P C, Mcdonald J, Mitchell N J, Mustonen T, Pandolfi J M, Pettorelli N, Popova E, Robinson S A, Scheffers B R, Shaw J D, Sorte C J B, Strugnell J M, Sunday J M, Tuanmu M N, Vergés A, Villanueva C, Wernberg T, Wapstra E, Williams S E. 2017. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being[J]. Science, 355(6332): eaai9214. doi: 10.1126/science.aai9214 CrossRef Google Scholar
[17]	Pan Jing, Huang Cuihua, Luo Jun, Peng Fei, Xue Xian. 2018. Effects of salt stress on plant and the mechanism of arbuscular mycorrhizal fungi enhancing salt tolerance of plants[J]. Advances in Earth Science, 33(4): 361−372 (in Chinese with English abstract). Google Scholar
[18]	Duan Q X, Zhu Z H, Wang B S, Chen M. 2022. Recent progress on the salt tolerance mechanisms and application of tamarisk[J]. International Journal of Molecular Sciences, 23(6): 3325−3325. doi: 10.3390/ijms23063325 CrossRef Google Scholar
[19]	Shi F N, Liu S L, An Y, Sun Y X, Zhao S, Liu Y X, Li M Q. 2023a. Climatic factors and human disturbance influence ungulate species distribution on the Qinghai−Tibet Plateau[J]. Science of the Total Environment, 869: 161681. doi: 10.1016/j.scitotenv.2023.161681 CrossRef Google Scholar
[20]	Shi X D, Wang J W, Zhang L, Chen S X, Zhao A L, Ning X D, Fan G R, Wu N S, Zhang L, Wang Z D. 2023b. Prediction of the potentially suitable areas of Litsea cubeba in China based on future climate change using the optimized MaxEnt model[J]. Ecological Indicators, 148: 110093. doi: 10.1016/j.ecolind.2023.110093 CrossRef Google Scholar
[21]	Sillero N, Arenas-Castro S, Enriquez-Urzelai U, Vale C G, Sousa-Guedes D, Martínez-Freiría F, Real R, Barbosa A M. 2021. Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling[J]. Ecological Modelling, 456: 109671. doi: 10.1016/j.ecolmodel.2021.109671 CrossRef Google Scholar
[22]	Sun A, Yang Q H, Liu Z, Chen H, Han L, Jiang S M, Meng Y Y, Bian Y, Yang Y P. 2022. Distribution of wetlands and salt lakes in the Yadong region of Tibet based on remote sensing, and their geo-climatic environmental changes[J]. China Geology, 5(4): 637−648. Google Scholar
[23]	Xiong M Q, Li F J, Liu X H, Liu J F, Luo X P, Xing L Y, Wang R, Li H Y, Guo F Y. 2023. Characterization of ecosystem services and their trade-off and synergistic relationships under different land-use scenarios on the loess plateau[J]. Land, 12(12): 2087. doi: 10.3390/land12122087 CrossRef Google Scholar
[24]	Xu D P, Zhuo Z H, Wang R L, Ye M, Pu B. 2019. Modeling the distribution of Zanthoxylum armatum in China with MaxEnt modeling[J]. Global Ecology and Conservation, 19: e00691. doi: 10.1016/j.gecco.2019.e00691 CrossRef Google Scholar
[25]	Xu L, Fan Y, Zheng J H, Guan J Y, Lin J, Wu J G, Liu L, Wu R, Liu Y J. 2024. Impacts of climate change and human activity on the potential distribution of Aconitum leucostomum in China[J]. Science of the Total Environment, 912: 168829. doi: 10.1016/j.scitotenv.2023.168829 CrossRef Google Scholar
[26]	Yackulic C B, Chandler R, Zipkin E F, Royle J A, Nichols J D, Campbell Grant E H, Veran S. 2013. Presence-only modelling using MAXENT: When can we trust the inferences?[J]. Methods in Ecology and Evolution, 4(3): 236−243. doi: 10.1111/2041-210x.12004 CrossRef Google Scholar
[27]	Yang H J, Xia J B, Cui Q, Liu J T, Wei S C, Feng L, Dong K K. 2021. Effects of different Tamarix chinensis-grass patterns on the soil quality of coastal saline soil in the Yellow River Delta, China[J]. Science of the Total Environment, 772: 145501. doi: 10.1016/j.scitotenv.2021.145501 CrossRef Google Scholar
[28]	Yang S L, Wang H M, Tong J P, Bai Y, Alatalo J M, Liu G, Fang Z, Zhang F. 2022. Impacts of environment and human activity on grid−scale land cropping suitability and optimization of planting structure, measured based on the MaxEnt model[J]. Science of the Total Environment, 836: 155356. doi: 10.1016/j.scitotenv.2022.155356 CrossRef Google Scholar
[29]	Yang Jinsong, Yao Rongjiang, Wang Xiangping, Xie Wenping, Zhang Xin, Zhu Wei, Zhang Lu, Sun Ruijuan. 2022. Research on salt−affected soils in China: History, status quo and prospect[J]. Acta Pedologica Sinica, 59(1): 10−27 (in Chinese with English abstract). Google Scholar
[30]	Yang Weikang, Zhang Daoyuan, Yin Linke, Zhang Liyun. 2002. Distribution and cluster analysis on the similarity of the tamarix communities in Xinjiang[J]. Journal of Arid Land, (3): 6−11 (in Chinese with English abstract). Google Scholar
[31]	Ye S Y, Pei L X, He L, Xie L J, Zhao G M, Yuan H M, Ding X G, Pei S F, Yang S X, Li X, Laws E A. 2022. Wetlands in China: Evolution, carbon sequestrations and services, threats, and preservation/restoration[J]. Water, 14(7): 1152−1152. doi: 10.3390/w14071152 CrossRef Google Scholar
[32]	Ye Xingzhuang, Zhang Mingzhu, Lai Wenfeng, Yang Miaomiao, Fan Huihua, Zhang Guofang, Chen Shipin, Liu Bao. 2021. Prediction of potential suitable distribution of Phoebe bournei based on MaxEnt optimization model[J]. Acta Ecologica Sinica, 41(20): 8135−8144. (in Chinese with English abstract). Google Scholar
[33]	Yuan Jianglong, Zhao Honghui, Liu Xiaohuang, Li Hongyu, Jiang Dong, Zhao Chuanyan, Xing Liyuan, Luo Xinping, Wang Ran, Wang Chao. 2024. Driving force analysis and ecological assessment of spatiotemporal changes in vegetation cover in the Kunlun Mountains from 2000 to 2020[J]. Geology in China, 51(5): 1822−1838 (in Chinese with English abstract). Google Scholar
[34]	Zhang L, Rohr J, Cui R N, Xin Y S, Han L X, Yang X N, Gu S M, Du Y B, Liang J, Wang X Y, Wu Z J, Hao Q, Liu X. 2022. Biological invasions facilitate zoonotic disease emergences[J]. Nature Communications, 13(1): 1762. doi: 10.1038/s41467-022-29378-2 CrossRef Google Scholar
[35]	Zhao X F, Lei M, Wei C H, Guo X X. 2022. Assessing the suitable regions and the key factors for three Cd−accumulating plants (Sedum alfredii, Phytolacca americana, and Hylotelephium spectabile) in China using MaxEnt model[J]. Science of the Total Environment, 852: 158202. doi: 10.1016/j.scitotenv.2022.158202 CrossRef Google Scholar
[36]	Zhou H P, Xiao F, Zheng Y, Liu G Y, Zhuang Y F, Wang Z Y, Zhang Y Y, He J X, Fu C X, Lin H H. 2021. Pamp-induced secreted peptide 3 modulates salt tolerance through receptor-like kinase 7 in plants[J]. The Plant Cell, 34(2): 927−944. Google Scholar
[37]	Zhang Daoyuan, Yang Weikang, Pan Borong, Yin Linke. 2003. Characters of tamarix hispida willd communities and its ecological & physiological adaptation[J]. Journal of Desert Research, (4): 112−117 (in Chinese with English abstract). Google Scholar
[38]	Zhang Gengquan. 2018. Investigation and evaluation of wild tamarix chinensis resources in Golmud City of Qinghai Province[J]. Forest Inventory and Planning, 43(4): 85−88, 98 (in Chinese with English abstract). Google Scholar
[39]	Zhang Hua, Zhang Lan, Zhao Chuanyan, Peng Shouzhang, Zheng Xianglin. 2014. Biomass-based transpiration water consumption of dominant species of vegetation estimation in the oasis of lower reaches of Heihe River[J]. Geographical Science, 34(7): 876−881 (in Chinese with English abstract). Google Scholar
[40]	Zhang Yinbo, Liu Yanlan, Qin Hao, Meng Qingxin. 2019. Prediction on spatial migration of suitable distribution of elaeagnus mollis under climate change conditions in Shanxi Province, China[J]. Chinese Journal of Applied Ecology, 30(2): 496−502 (in Chinese with English abstract). Google Scholar
[41]	Zhu Shuai, Cao Jianhua, Yang Hui, Liang Jianhong, Lao Changling. 2023. A review of the interaction mechanism and law between vegetation and rock geochemical background in Karst Areas[J]. Rock and Mineral Analysis, 42(1): 59−71 (in Chinese with English abstract). Google Scholar
[42]	Zhu Xuchao, Yuan Guofu, Shao Ming'an, Yi Xiaobo, Du Tao. 2015. Spatial pattern of riparian vegetation in desert of the lower Tarim River basin[J]. Chinese Journal of Plant Ecology, 39(11): 1053−1061 (in Chinese with English abstract). doi: 10.17521/cjpe.2015.0102 CrossRef Google Scholar
[43]	陈武迪, 刘晓煌, 李洪宇, 孙兴丽, 王玉刚, 刘晓洁, 邢莉圆, 王然, 雒新萍, 王超, 赵宏慧. 2024. 新疆天山1990—2050年生态系统服务功能及安全格局[J]. 中国地质, 51(5): 1644−1663. Google Scholar
[44]	方欧娅, 贾恒锋, 邱红岩, 任海保. 2017. 青海省同德县乔木状甘蒙柽柳的年龄及其生长对环境的响应[J]. 植物生态学报, 41(7): 738−748. doi: 10.17521/cjpe.2017.0088 CrossRef Google Scholar
[45]	付宇佳, 谭昌海, 刘晓煌, 孙兴丽, 袁泽民, 郑艺文. 2022. 自然资源定义、分类, 观测监测及其在国土规划治理中的应用[J]. 中国地质, 49(4): 1048−1063. Google Scholar
[46]	韩福贵, 满多清, 郑庆钟, 肖斌, 张裕年, 付贵全, 杜娟. 2022. 石羊河中下游不同生境柽柳群落物种多样性及土壤水分、盐分关系[J]. 水土保持研究, 29(3): 49−56. Google Scholar
[47]	李苍柏, 肖克炎, 李楠, 宋相龙, 张帅, 王凯, 楚文楷, 曹瑞. 2020. 支持向量机、随机森林和人工神经网络机器学习算法在地球化学异常信息提取中的对比研究[J]. 地球学报, 41(2): 309−319. Google Scholar
[48]	潘晶, 黄翠华, 罗君, 彭飞, 薛娴. 2018. 盐胁迫对植物的影响及AMF提高植物耐盐性的机制[J]. 地球科学进展, 33(4): 361−372. Google Scholar
[49]	杨劲松, 姚荣江, 王相平, 谢文萍, 张新, 朱伟, 张璐, 孙瑞娟. 2022. 中国盐渍土研究: 历程、现状与展望[J]. 土壤学报, 59(1): 10−27. Google Scholar
[50]	杨维康, 张道远, 尹林克, 张立运. 2002. 新疆柽柳属植物(Tamarix L. )的分布与群落相似性聚类分析[J]. 干旱区研究, (3): 6−11. Google Scholar
[51]	叶兴状, 张明珠, 赖文峰, 杨淼淼, 范辉华, 张国防, 陈世品, 刘宝. 2021. 基于MaxEnt优化模型的闽楠潜在适宜分布预测[J]. 生态学报, 41(20): 8135−8144. Google Scholar
[52]	袁江龙, 赵宏慧, 刘晓煌, 李洪宇, 江东, 赵传燕, 邢莉圆, 雒新萍, 王然, 王超. 2024. 2000—2020年昆仑山植被覆盖度时空变化驱动力分析及生态评价[J]. 中国地质, 51(6): 1822−1838. Google Scholar
[53]	张道远, 杨维康, 潘伯荣, 尹林克. 2003. 刚毛柽柳群落特征及其生态、生理适应性[J]. 中国沙漠, (4): 112−117. Google Scholar
[54]	张更权. 2018. 格尔木市野生柽柳资源调查与评价[J]. 林业调查规划, 43(4): 85−88, 98. Google Scholar
[55]	张华, 张兰, 赵传燕, 彭守璋, 郑祥霖. 2014. 黑河下游绿洲植被优势种生物量空间分布及蒸腾耗水估算[J]. 地理科学, 34(7): 876−881. Google Scholar
[56]	张殷波, 刘彦岚, 秦浩, 孟庆欣. 2019. 气候变化条件下山西翅果油树适宜分布区的空间迁移预测[J]. 应用生态学报, 30(2): 496−502. Google Scholar
[57]	朱帅, 曹建华, 杨慧, 梁建宏, 劳昌玲. 2023. 岩溶区植被与岩石地球化学背景间相互作用机制研究进展[J]. 岩矿测试, 42(1): 59−71. Google Scholar
[58]	朱绪超, 袁国富, 邵明安, 易小波, 杜涛. 2015. 塔里木河下游河岸带植被的空间结构特征[J]. 植物生态学报, 39(11): 1053−1061. Google Scholar