Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (9): 1255-1269    DOI: 10.1007/s40333-024-0061-8

CSTR: 32276.14.JAL.02400618

Research article     
Potential distribution of Haloxylon ammodendron in Central Asia under climate change
CHEN Zhuo1,2, SHAO Minghao2,3, HU Zihao4, GAO Xin1,2,*(), LEI Jiaqiang1,2
1Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3National Engineering Technology Research Center for Desert-Oasis Ecological Construction, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
4University of Emergency Management, Beijing 101601, China
Download: HTML     PDF(1734KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

Understanding the spatial distribution of plant species and their dynamic changes in arid areas is crucial for addressing the challenges posed by climate change. Haloxylon ammodendron shelterbelts are essential for the protection of plant resources and the control of desertification in Central Asia. Thus far, the potential suitable habitats of H. ammodendron in Central Asia are still uncertain in the future under global climate change conditions. This study utilised the maximum entropy (MaxEnt) model to combine the current distribution data of H. ammodendron with its growth-related data to analyze the potential distribution pattern of H. ammodendron across Central Asia. The results show that there are suitable habitats of H. ammodendron in the Aralkum Desert, northern slopes of the Tianshan Mountains, and the upstream of the Tarim River and western edge of the Taklimakan Desert in the Tarim Basin under the current climate conditions. The period from 2021 to 2040 is projected to undergo significant changes in the suitable habitat area of H. ammodendron in Central Asia, with a projected 15.0% decrease in the unsuitable habitat area. Inland areas farther from the ocean, such as the Caspian Sea and Aralkum Desert, will continue to experience a decrease in the suitable habitats of H. ammodendron. Regions exhibiting frequent fluctuations in the habitat suitability levels are primarily found along the axis stretching from Astana to Kazakhskiy Melkosopochnik in Kazakhstan. These regions can transition into suitable habitats under varying climate conditions, requiring the implementation of appropriate human intervention measures to prevent desertification. Future climate conditions are expected to cause an eastward shift in the geometric centre of the potential suitable habitats of H. ammodendron, with the extent of this shift amplifying alongside more greenhouse gas emissions. This study can provide theoretical support for the spatial configuration of H. ammodendron shelterbelts and desertification control in Central Asia, emphasising the importance of proactive measures to adapt to climate change in the future.

Key words： Haloxylon ammodendron      potential suitable habitats      climate change      desertification      maximum entropy (MaxEnt) model      Central Asia      Aralkum Desert     
Received: 01 April 2024      Published: 30 September 2024
CLC:  32276.14.JAL.02400618  
Corresponding Authors: *GAO Xin (E-mail: gaoxin@ms.xjb.ac.cn)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
CHEN Zhuo
SHAO Minghao
HU Zihao
GAO Xin
LEI Jiaqiang
Cite this article:

CHEN Zhuo, SHAO Minghao, HU Zihao, GAO Xin, LEI Jiaqiang. Potential distribution of Haloxylon ammodendron in Central Asia under climate change. Journal of Arid Land, 2024, 16(9): 1255-1269.

URL:

http://jal.xjegi.com/10.1007/s40333-024-0061-8     OR     http://jal.xjegi.com/Y2024/V16/I9/1255

Fig. 1 Overview of the study area based on the digital elevation model (DEM) and spatial distribution points of Haloxylon ammodendron obtained from the Global Biodiversity Information Fund (GBIF; http://www.gbif.org) and field observations. Note that the figure is based on the standard map (GS(2016)1665) from the Standard Map Service System (http://bzdt.ch.mnr.gov.cn/index.html) marked by the Ministry of Natural Resources of the People's Republic of China, and the standard map has not been modified.
Predictor variable Abbreviation Classification of action
Annual mean temperature BIO1 PC1
Mean diurnal range BIO2 PC4, -PC5, PC6*, and -PC7
Isothermality BIO3 -PC2, PC4, PC6, and -PC7
Temperature seasonality BIO4 PC2, PC3, and PC8
Max temperature of warmest month BIO5 PC1 and PC2
Min temperature of coldest month BIO6 -PC3
Temperature annual range BIO7 PC3, PC6, and PC8
Mean temperature of wettest quarter BIO8 PC4* and -PC6
Mean temperature of driest quarter BIO9 -PC3 and -PC4
Mean temperature of warmest quarter BIO10 PC1 and PC2
Mean temperature of coldest quarter BIO11 PC1 and -PC3
Annual precipitation BIO12 -PC1
Precipitation of wettest month BIO13 PC4 and PC8
Precipitation of driest month BIO14 -PC5 and -PC8*
Precipitation seasonality BIO15 -PC2 and PC8
Precipitation of wettest quarter BIO16 PC4
Precipitation of driest quarter BIO17 -PC5, PC6, and -PC8
Precipitation of warmest quarter BIO18 -PC1* and PC7
Precipitation of coldest quarter BIO19 -PC3* and PC6
Population distribution PD -PC2*
Elevation Elev PC5* and PC7*
Topsoil sand content TSC -PC5 and PC7
Topsoil water content TWC PC5 and -PC7
Table 1 Predictor variables and their abbreviations and classifications after downscaling
Fig. 2 Potential distribution pattern of H. ammodendron under current climate conditions based on the habitat classes. Highly suitable habitats: habitat class>0.60; medium suitable habitats: 0.30<habitat class≤0.60; low suitable habitats: 0.15<habitat class≤0.30; unsuitable habitats: habitat classes≤0.15. Note that the figure is based on the standard map (GS(2016)1665) from the Standard Map Service System (http://bzdt.ch.mnr.gov.cn/ index.html) marked by the Ministry of Natural Resources of the People's Republic of China, and the standard map has not been modified.
Fig. 3 Potential suitable habitats of H. ammodendron under future climate change scenarios: SSP126 (a1-a3), SSP245 (b1-b3), SSP370 (c1-c3), and SSP585 (d1-d3) during 2021-2040, 2041-2060, and 2061-2080. SSP, shared socioeconomic pathway. Note that the Caspian Sea is not involved in this analysis; the figures are based on the standard map (GS(2016)1665) from the Standard Map Service System (http://bzdt.ch.mnr.gov.cn/index.html) marked by the Ministry of Natural Resources of the People's Republic of China, and the standard map has not been modified.
Fig. 4 Transfer of the suitable habitats of H. ammodendron with different habitat classes during 2021-2040, 2041-2060, and 2061-2080
Fig. 5 Spatial distribution of changes in the suitable habitats of H. ammodendron from the current to future climate conditions. Note that the Caspian Sea is not involved in this analysis; the figure is based on the standard map (GS(2016)1665) from the Standard Map Service System (http://bzdt.ch.mnr.gov.cn/index.html) marked by the Ministry of Natural Resources of the People's Republic of China, and the standard map has not been modified.
Fig. 6 Transformation of the geometric centre of the potential suitable habitats of H. ammodendron from the current to future climate conditions under the SSP126 (a), SSP245 (b), SSP370 (c), and SSP585 (d) scenarios. The true colour image of the base map was obtained from the Google Maps.
[1]   Abdi E, Saleh H R, Majnonian B, et al. 2019. Soil fixation and erosion control by Haloxylon persicum roots in arid lands, Iran. Journal of Arid Land, 11(1): 86-96.
[2]   Ahmadi M, Hemami M R, Kaboli M, et al. 2023. MaxEnt brings comparable results when the input data are being completed: Model parameterization of four species distribution models. Ecology and Evolution, 13(2): e9827, doi: 10.1002/ece3.9827.
[3]   Bohovic R, Dobrovolny P, Klein D. 2016. The spatial and temporal dynamics of remotely-sensed vegetation phenology in Central Asia in the 1982-2011 period. European Journal of Remote Sensing, 49(1): 279-299.
[4]   Bouchet P J, Miller D L, Roberts J J, et al. 2020. Dsmextra: Extrapolation assessment tools for density surface models. Methods in Ecology and Evolution, 11(11): 1464-1469.
[5]   Chen Z, Gao X, Lei J Q. 2022. Dust emission and transport in the Aral Sea region. Geoderma, 428: 116177, doi: 10.1016/j.geoderma.2022.116177.
[6]   Davi N K, D'Arrigo R, Jacoby G C, et al. 2015. A long-term context (931-2005 C.E.) for rapid warming over Central Asia. Quaternary Science Reviews, 121: 89-97.
[7]   Dawson T P, Jackson S T, House J I, et al. 2011. Beyond predictions: biodiversity conservation in a changing climate. Science, 332(6025): 53-58.
doi: 10.1126/science.1200303 pmid: 21454781
[8]   de Beurs K M, Henebry G M, Owsley B C, et al. 2015. Using multiple remote sensing perspectives to identify and attribute land surface dynamics in Central Asia 2001-2013. Remote Sensing of Environment, 170: 48-61.
[9]   De Marco Júnior P, Nóbrega C C. 2018. Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation. PLoS ONE, 13(9): e0202403, doi: 10.1371/journal.pone.0202403.
[10]   Dillon M E, Wang G, Huey R B. 2010. Global metabolic impacts of recent climate warming. Nature, 467(7316): 704-706.
[11]   Etherington T R. 2019. Mahalanobis distances and ecological niche modelling: correcting a chi-squared probability error. PeerJ, 7: e6678, doi: 10.7717/peerj.6678.
[12]   Fang J Q, Shi J F, Zhang P, et al. 2024. Potential distribution projections for Senegalia senegal (L.) Britton under climate change scenarios. Forests, 15(2): 379, doi: 10.3390/f15020379.
[13]   Guillera-Arroita G, Lahoz-Monfort J, Elith J. 2014. MaxEnt is not a presence-absence method: a comment on Thibaud et al. Methods in Ecology and Evolution, 5(11): 1192-1197.
[14]   Guisan A, Edwards Jr T C, Hastie T. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling, 157(2-3): 89-100.
[15]   Hirzel A H, Hausser J, Chessel D, et al. 2002. Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology, 83(7): 2027-2036.
[16]   Hu Z Y, Zhang C, Hu Q, et al. 2014. Temperature changes in Central Asia from 1979 to 2011 based on multiple datasets. Journal of Climate, 27(3): 1143-1167.
[17]   Intergovernmental Panel on Climate Change (IPCC). 2023. Climate Change 2023:Synthesi Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva: IPCC, 35-115.
[18]   Jiang L L, Jiapaer G, Bao A M, et al. 2017. Vegetation dynamics and responses to climate change and human activities in Central Asia. Science of the Total Environment, 599-600: 967-980.
[19]   Kang J F, Liu M S, Qu M K, et al. 2023. Identifying the potential soil pollution areas derived from the metal mining industry in China using MaxEnt with mine reserve scales (MaxEnt_MRS). Environmental Pollution, 329: 121687, doi: 10.1016/j.envpol.2023.121687.
[20]   Leroy B, Delsol R, Hugueny B, et al. 2018. Without quality presence-absence data, discrimination metrics such as TSS can be misleading measures of model performance. Journal of Biogeography, 45(9): 1994-2002.
[21]   Li C J, Han H, Ablimiti M, et al. 2022. Morphological and physiological responses of desert plants to drought stress in a man-made landscape of the Taklimakan Desert shelter belt. Ecological Indicators, 140: 109037, doi: 10.1016/j.ecolind.2022.109037.
[22]   Li J Y, Chang H, Liu T, et al. 2019. The potential geographical distribution of Haloxylon across Central Asia under climate change in the 21st century. Agricultural and Forest Meteorology, 275: 243-254.
[23]   Lioubimtseva E. 2015. A multi-scale assessment of human vulnerability to climate change in the Aral Sea basin. Environmental Earth Sciences, 73: 719-729.
[24]   Mannocci L, Roberts J J, Miller D L, et al. 2017. Extrapolating cetacean densities to quantitatively assess human impacts on populations in the high seas. Conservation Biology, 31(3): 601-614.
doi: 10.1111/cobi.12856 pmid: 27775847
[25]   Mannocci L, Roberts J J, Halpin P N, et al. 2018. Assessing cetacean surveys throughout the Mediterranean Sea: A gap analysis in environmental space. Scientific Reports, 8(1): 3126, doi: 10.1038/s41598-018-19842-9.
[26]   Marmion M, Parviainen M, Luoto M, et al. 2009. Evaluation of consensus methods in predictive species distribution modelling. Diversity and Distributions, 15(1): 59-69.
[27]   Mi C R, Huettmann F, Guo Y M, et al. 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: 10.7287/peerj.preprints.2517v1.
[28]   Micklin P. 2007. The Aral Sea disaster. Annual Review of Earth and Planetary Sciences, 35: 47-72.
[29]   Miller D L, Burt M L, Rexstad E A, et al. 2013. Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution, 4(11): 1001-1010.
[30]   Mohammat A, Wang X H, Xu X T, et al. 2013. Drought and spring cooling induced recent decrease in vegetation growth in Inner Asia. Agricultural and Forest Meteorology, 178-179: 21-30.
[31]   Muscarella R, Galante P J, Soley-Guardia M, et al. 2015. Enmeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for maxent ecological niche models. Methods in Ecology and Evolution, 5(11): 1198-1205.
[32]   Paolo D, Bhattachan A, Kyle F, et al. 2013. Global desertification: Drivers and feedbacks. Advances in Water Resources, 51: 326-344.
[33]   Pecchi M, Marchi M, Moriondo M, et al. 2020. Potential impact of climate change on the forest coverage and the spatial distribution of 19 key forest tree species in Italy under RCP4.5 IPCC trajectory for 2050s. Forests, 11(9): 934, doi: 10.3390/f11090934.
[34]   Phillips S J, Anderson R P, Schapire R E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4): 231-259.
[35]   Qi K, Zhu J J, Zheng X, et al. 2023. Impacts of the world's largest afforestation program (Three-North Afforestation Program) on desertification control in sandy land of China. GIScience & Remote Sensing, 60(1): 2167574, doi: 10.1080/15481603.2023.2167574.
[36]   Rengasamy P. 2006. World salinization with emphasis on Australia. Journal of Experimental Botany, 57(5): 1017-1023.
doi: 10.1093/jxb/erj108 pmid: 16510516
[37]   Ruan X, Wang Q, Chen Y, et al. 2006. Physio-ecological response of Haloxylon persicum photosynthetic shoots to drought stress. Frontiers of Forestry in China, 1: 176-181.
[38]   Shao M H, Wang L, Li B W, et al. 2022. Maxent modeling for identifying the nature reserve of Cistanche deserticola Ma under effects of the host (Haloxylon Bunge) forest and climate changes in Xinjiang, China. Forests, 13(2): 189, doi: 10.3390/f13020189.
[39]   Shao Y. 2008. Physics and Modelling of Wind Erosion. Dordrecht: Springer Netherlands, 456, doi: 10.1007/978-1-4020-8895-7.
[40]   Sillero N, Gonçalves-Seco L. 2014. Spatial structure analysis of a reptile community with airborne LiDAR data. International Journal of Geographical Information Science, 28(8): 1709-1722.
[41]   Sofaer H, Jarnevich C, Pearse I S, et al. 2019. Development and Delivery of Species Distribution Models to Inform Decision-Making. BioScience, 69(7): 544-557.
doi: 10.1093/biosci/biz045
[42]   Song Y L, Zeng K W, Jiang Y, et al. 2021. Cistanches Herba, from an endangered species to a big brand of Chinese medicine. Medicinal Research Reviews, 41(3): 1539-1577.
doi: 10.1002/med.21768 pmid: 33521978
[43]   Sorichetta A, Hornby G M, Stevens F R, et al. 2015. High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020. Scientific Data, 2: 150045, doi: 10.1038/sdata.2015.45.
[44]   Sun J J, Qiu H J, Guo J H, et al. 2020. Modeling the potential distribution of Zelkova schneideriana under different human activity intensities and climate change patterns in China. Global Ecology and Conservation, 21: e00840, doi: 10.1016/j.gecco.2019.e00840.
[45]   Tao Y, Wu G L, Zhang Y M. 2017. Dune-scale distribution pattern of herbaceous plants and their relationship with environmental factors in a saline-alkali desert in Central Asia. Science of the Total Environment, 576: 473-480.
[46]   Tarhouni M, Salem F B, Belgacem A O, et al. 2010. Acceptability of plant species along grazing gradients around watering points in Tunisian arid zone. Flora-Morphology, Distribution, Functional Ecology of Plants, 205(7): 454-461.
[47]   Tomislav H. 2018. Sand content in % (kg/kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution (v0.2)[Data set]. Zenodo. https://doi.org/10.5281/zenodo.2525662.
[48]   Tomislav H, Surya G. 2019. Soil water content (volumetric %) for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution (v0.1)[Data set]. Zenodo. https://doi.org/10.5281/zenodo.2784001.
[49]   Trampetti F, Pereira C, Rodrigues M J, et al. 2019. Exploring the halophyte Cistanche phelypaea (L.) Cout as a source of health promoting products: In vitro antioxidant and enzyme inhibitory properties, metabolomic profile and computational studies. Journal of Pharmaceutical and Biomedical Analysis, 165: 119-128.
[50]   Velasco J A, González-Salazar C. 2019. Akaike information criterion should not be a ''test'' of geographical prediction accuracy in ecological niche modelling. Ecological Informatics, 51: 25-32.
doi: 10.1016/j.ecoinf.2019.02.005
[51]   Wang L J, Zhao C Y, Li J, et al. 2009. Study on species diversity in early-spring in Haloxylon desert in north Fukang region, Xinjiang. Arid Zone Research, 26(4): 574-581. (in Chinese)
[52]   Xiao F J, Liu Q F, Qin Y. 2024. Predicting the potential distribution of Haloxylon ammodendron under climate change scenarios using machine learning of a maximum entropy model. Biology, 13(1): 3, doi: 10.3390/biology13010003.
[53]   Yang W J, Sun S X, Wang N X, et al. 2023. Dynamics of the distribution of invasive alien plants (Asteraceae) in China under climate change. Science of the Total Environment, 903: 166260, doi: 10.1016/j.scitotenv.2023.166260.
[54]   Zhang X X, Claiborn C, Lei J Q, et al. 2020. Aeolian dust in Central Asia: Spatial distribution and temporal variability. Atmospheric Environment, 238: 117734, doi: 10.1016/j.atmosenv.2020.117734.
[1] SUN Chao, BAI Xuelian, WANG Xinping, ZHAO Wenzhi, WEI Lemin. Response of vegetation variation to climate change and human activities in the Shiyang River Basin of China during 2001-2022[J]. Journal of Arid Land, 2024, 16(8): 1044-1061.
[2] YAN Yujie, CHENG Yiben, XIN Zhiming, ZHOU Junyu, ZHOU Mengyao, WANG Xiaoyu. Impacts of climate change and human activities on vegetation dynamics on the Mongolian Plateau, East Asia from 2000 to 2023[J]. Journal of Arid Land, 2024, 16(8): 1062-1079.
[3] YANG Jianhua, LI Yaqian, ZHOU Lei, ZHANG Zhenqing, ZHOU Hongkui, WU Jianjun. Effects of temperature and precipitation on drought trends in Xinjiang, China[J]. Journal of Arid Land, 2024, 16(8): 1098-1117.
[4] HAN Qifei, XU Wei, LI Chaofan. Effects of nitrogen deposition on the carbon budget and water stress in Central Asia under climate change[J]. Journal of Arid Land, 2024, 16(8): 1118-1129.
[5] WANG Tongxia, CHEN Fulong, LONG Aihua, ZHANG Zhengyong, HE Chaofei, LYU Tingbo, LIU Bo, HUANG Yanhao. Glacier area change and its impact on runoff in the Manas River Basin, Northwest China from 2000 to 2020[J]. Journal of Arid Land, 2024, 16(7): 877-894.
[6] DU Lan, TIAN Shengchuan, ZHAO Nan, ZHANG Bin, MU Xiaohan, TANG Lisong, ZHENG Xinjun, LI Yan. Climate and topography regulate the spatial pattern of soil salinization and its effects on shrub community structure in Northwest China[J]. Journal of Arid Land, 2024, 16(7): 925-942.
[7] Haq S MARIFATUL, Darwish MOHAMMED, Waheed MUHAMMAD, Kumar MANOJ, Siddiqui H MANZER, Bussmann W RAINER. Predicting potential invasion risks of Leucaena leucocephala (Lam.) de Wit in the arid area of Saudi Arabia[J]. Journal of Arid Land, 2024, 16(7): 983-999.
[8] Seyed Morteza MOUSAVI, Hossein BABAZADEH, Mahdi SARAI-TABRIZI, Amir KHOSROJERDI. Assessment of rehabilitation strategies for lakes affected by anthropogenic and climatic changes: A case study of the Urmia Lake, Iran[J]. Journal of Arid Land, 2024, 16(6): 752-767.
[9] LI Chuanhua, ZHANG Liang, WANG Hongjie, PENG Lixiao, YIN Peng, MIAO Peidong. Influence of vapor pressure deficit on vegetation growth in China[J]. Journal of Arid Land, 2024, 16(6): 779-797.
[10] LU Haitian, ZHAO Ruifeng, ZHAO Liu, LIU Jiaxin, LYU Binyang, YANG Xinyue. Impact of climate change and human activities on the spatiotemporal dynamics of surface water area in Gansu Province, China[J]. Journal of Arid Land, 2024, 16(6): 798-815.
[11] YANG Zhiwei, CHEN Rensheng, LIU Zhangwen, ZHAO Yanni, LIU Yiwen, WU Wentong. Spatiotemporal variability of rain-on-snow events in the arid region of Northwest China[J]. Journal of Arid Land, 2024, 16(4): 483-499.
[12] SHEN Jianxiang, WANG Xin, WANG Lei, WANG Jiahui, QU Wenjie, ZHANG Xue, CHANG Xuanxuan, YANG Xinguo, CHEN Lin, QIN Weichun, ZHANG Bo, NIU Jinshuai. Spatiotemporal characteristics of seed rain and soil seed bank of artificial Caragana korshinskii Kom. forest in the Tengger Desert, China[J]. Journal of Arid Land, 2024, 16(4): 550-566.
[13] BAO Anming, YU Tao, XU Wenqiang, LEI Jiaqiang, JIAPAER Guli, CHEN Xi, Tojibaev KOMILJON, Shomurodov KHABIBULLO, Xabibullaev B SAGIDULLAEVICH, Idirisov KAMALATDIN. Ecological problems and ecological restoration zoning of the Aral Sea[J]. Journal of Arid Land, 2024, 16(3): 315-330.
[14] ZHANG Mingyu, CAO Yu, ZHANG Zhengyong, ZHANG Xueying, LIU Lin, CHEN Hongjin, GAO Yu, YU Fengchen, LIU Xinyi. Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang, China[J]. Journal of Arid Land, 2024, 16(3): 373-395.
[15] WANG Baoliang, WANG Hongxiang, JIAO Xuyang, HUANG Lintong, CHEN Hao, GUO Wenxian. Runoff change in the Yellow River Basin of China from 1960 to 2020 and its driving factors[J]. Journal of Arid Land, 2024, 16(2): 168-194.