基于MaxEnt模型预测气候变化下准噶尔沙蒿（Artemisia songarica）在新疆的潜在分布

doi:10.12118/j.issn.1000-6060.2024.657

干旱区地理 ›› 2025, Vol. 48 ›› Issue (9): 1578-1588.doi: 10.12118/j.issn.1000-6060.2024.657 cstr: 32274.14.ALG2024657

基于MaxEnt模型预测气候变化下准噶尔沙蒿（Artemisia songarica）在新疆的潜在分布

李文华¹^,²^,³^,⁴(), 李生宇¹^,²^,³^,⁴(), 徐新文¹^,²^,³^,⁴, 苗佳敏¹^,²^,³^,⁴, 吕振涛¹^,²^,³^,⁴

1.中国科学院新疆生态与地理研究所国家荒漠-绿洲生态建设工程技术研究中心/干旱区生态安全与可持续发展全国重点实验室，新疆乌鲁木齐 830011
2.中国科学院新疆生态与地理研究所莫索湾沙漠研究站，新疆石河子 832000
3.中国科学院新疆生态与地理研究所塔克拉玛干沙漠研究站，新疆库尔勒 841000
4.中国科学院大学，北京 100049

收稿日期:2024-10-26 修回日期:2024-12-12 出版日期:2025-09-25 发布日期:2025-09-17
通讯作者: 李生宇（1975-），男，博士，正高级工程师，主要从事风沙地貌与风沙工程方面的研究. E-mail: oasis@ms.xjb.ac.cn
作者简介:李文华（1998-），女，硕士研究生，主要从事风沙地貌与风沙工程方面的研究. E-mail: liwenhua22@mails.ucas.ac.cn
基金资助:
第三次新疆综合科学考察课题(2021xjkk0305);新疆交投科研项目(XJJTZKX-FWCG-202401-0043)

Predicting the potential distribution of Artemisia songarica in Xinjiang under climate change based on the MaxEnt model

LI Wenhua¹^,²^,³^,⁴(), LI Shengyu¹^,²^,³^,⁴(), XU Xinwen¹^,²^,³^,⁴, MIAO Jiamin¹^,²^,³^,⁴, LYU Zhentao¹^,²^,³^,⁴

1. National Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, National Engineering Technology Research Center for Desert-0asis Ecological Construction, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
2. Mosuowan Desert Research Station, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Shihezi 832000, Xinjiang, China
3. Taklimakan Desert Research Station, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Korla 841000, Xinjiang, China
4. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2024-10-26 Revised:2024-12-12 Published:2025-09-25 Online:2025-09-17

摘要/Abstract

摘要：

作为荒漠和半荒漠地区的主要植物，准噶尔沙蒿（Artemisia songarica）在维持新疆地区生态系统平衡方面发挥着重要作用。在全球气候变化的背景下，研究其对气候变化的响应，对于区域生态系统的稳定性至关重要。基于最大熵（MaxEnt）模型，对准噶尔沙蒿在新疆地区当代及未来（2041—2060年和2081—2100年）的潜在适生区分布进行了预测，并分析了驱动其分布格局的主要环境因素。结果表明：（1）在当代气候下，准噶尔沙蒿的适生区主要集中在整个北疆区域及南疆沙漠边缘的绿洲地区，适生面积约为57.95×10⁴km²，主要的驱动因素包括人类足迹指数（hfp）、降水量季节变异系数（bio15）、最干月降水量（bio14）、等温性（bio3）和昼夜温差月均值（bio2）。（2）去除人类影响因素后，准噶尔沙蒿的潜在分布区域显著扩大、适生等级提高，且适宜生境分布更为连贯，表明人类活动对其生存发展产生了负面影响。（3）未来气候条件下，准噶尔沙蒿的潜在适宜生境与当代分布基本一致；在低辐射强迫情景（SSP126）和中等稳定情景（SSP245）下，其适生区先扩张后略有缩小，扩张区域主要位于哈密市北部边缘以及和田地区和喀什地区南部边缘，削减区域则主要位于北疆准噶尔盆地边缘和南疆的绿洲地带；而在高强迫情景（SSP585）下，其适生区则沿当代适生区的边缘向东扩张；在未来气候变化下适宜生境的分布中心向低纬度迁移，迁移距离与辐射胁迫强度成正比。

关键词: 准噶尔沙蒿, MaxEnt模型, 人类足迹指数, 潜在适生区, 气候变化

Abstract:

Artemisia songarica is a crucial species in desert and semi-desert ecosystems, significantly contributing to the maintaince of ecological balance in Xinjiang of China. Using the maximum entropy (MaxEnt) model, this study predicts the potential suitable habitat distribution of A. songarica under current and future climate scenarios (2041—2060 and 2081—2100) and analyzes the main environmental factors driving its distribution pattern. The key findings are as follow: (1) Under current climatic conditions, the potential suitable habitats of A. songarica are primarily located in northern Xinjiang and along the oasis margins of the dessert in southern Xinjiang, covering approximately 57.95×10⁴km². The main environmental determinants include the human footprint index (hfp), precipitation seasonality (bio15), precipitation of the driest month (bio14), isothermality (bio3), and the mean diurnal temperature range (bio2). (2) Excluding human influence factors, a significant expansion of the species’ potential distribution, with an increase in habitat suitability and more consistent habitat connectivity is observed. This suggests that human activities have a detrimental impact on survival and development. (3) Under future climatic scenarios, the potential distribution of suitable habitats largely overlaps with the current distributions. In low radiative forcing scenarios (SSP126) and moderately stable scenarios (SSP245), suitable habitats initially expand and then experience slight contraction. Expansion primarily occurs along the northern edge of Hami City and the southern borders of Hotan and Kashgar Prefectures, while contraction areas are concentrated in the Junggar Basin and southern oasis margins. Under a high radiative forcing scenario (SSP585), suitable habitats expand eastward along the current distribution boundary. Additionally, the centroid of potential suitable habitat is projected to shift toward lower latitudes, with migration distance positively correlated with the intensity of radiative forcing.

Key words: Artemisia songarica, MaxEnt model, human footprint, potential suitable habitat, climate change

李文华, 李生宇, 徐新文, 苗佳敏, 吕振涛. 基于MaxEnt模型预测气候变化下准噶尔沙蒿（Artemisia songarica）在新疆的潜在分布[J]. 干旱区地理, 2025, 48(9): 1578-1588.

LI Wenhua, LI Shengyu, XU Xinwen, MIAO Jiamin, LYU Zhentao. Predicting the potential distribution of Artemisia songarica in Xinjiang under climate change based on the MaxEnt model[J]. Arid Land Geography, 2025, 48(9): 1578-1588.

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参考文献 39

[1]	Yang J, Wang Y C, Xiu C L, et al. Optimizing local climate zones to mitigate urban heat island effect in human settlements[J]. Journal of Cleaner Production, 2020, 275: 123767, doi: 10.1016/j.jclepro.2020.123767.
[2]	吕佳佳, 吴建国. 气候变化对植物及植被分布的影响研究进展[J]. 环境科学与技术, 2009, 32(6): 85-95.
	[Lü Jiajia, Wu Jianguo. Advances in the effects of climate change on the distribution of plant species and vegetation in China[J]. Environmental Science and Technology, 2009, 32(6): 85-95.]
[3]	赵娟, 史雅楠, 李新平. 气候变暖背景下黄河中游晋西黄土高原的植被响应[J]. 山西林业科技, 2024, 53(2): 46-48.
	[Zhao Juan, Shi Ya’nan, Li Xinping. Vegetation response of the western Shanxi Loess Plateau in the middle reaches of the Yellow River under the background of climate warming[J]. Shanxi Forestry Science and Technology, 2024, 53(2): 46-48.]
[4]	Armstrong Mckay D I, Staal A, Abrams J F, et al. Exceeding 1.5 ℃ global warming could trigger multiple climate tipping points[J]. Science, 2022, 377(6611): 1171, doi: 10.1126/science.abn7950.
[5]	卢冬燕, 朱秀芳, 唐明秀, 等. 不同温升情景下中国旱灾风险变化评估[J]. 干旱区地理, 2024, 47(3): 369-379. doi: 10.12118/j.issn.1000-6060.2023.448
	[Lu Dongyan, Zhu Xiufang, Tang Mingxiu, et al. Assessment of drought risk changes in China under different temperature rise scenarios[J]. Arid Land Geography, 2024, 47(3): 369-379.] doi: 10.12118/j.issn.1000-6060.2023.448
[6]	Kaky E, Nolan V, Alatawi A, et al. A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants[J]. Ecological Informatics, 2020, 60: 101150, doi: 10.1016/j.ecoinf.2020.101150.
[7]	Liu X T, Yuan Q, Ni J. Research advances in modelling plant species distribution in China[J]. Chinese Journal of Plant Ecology, 2019, 43(4): 273-283.
[8]	Xing D L, Hao Z Q. The principle of maximum entropy and its applications in ecology[J]. Biodiversity Science, 2011, 19(3): 295-302.
[9]	马正平. 准噶尔西部沙漠中的主要植物[J]. 新疆农业科学, 1959(12): 500-505.
	[Ma Zhengping. The main plants in the western deserts of the Junggar Basin[J]. Xinjiang Agricultural Sciences, 1959(12): 500-505.]
[10]	王新军. 古尔班通古特沙漠固沙植被格局与水文过程的关系研究[D]. 乌鲁木齐: 新疆农业大学, 2020.
	[Wang Xinjun. Study on relationship of sand-fixating vegetation pattern and hydrological process in Gurbantonggut Desert[D]. Urumqi: Xinjiang Agricultural University, 2020.]
[11]	陶冶, 张元明. 准噶尔沙蒿群落主要物种间的关联性分析[J]. 中国沙漠, 2012, 32(5): 1308-1314.
	[Tao Ye, Zhang Yuanming. Interspecific associations among main species in Artemisia songarica communities in Junggar Basin, China[J]. Journal of Desert Research, 2012, 32(5): 1308-1314.]
[12]	Wang X M, Geng X, Liu B, et al. Desert ecosystems in China: Past, present, and future[J]. Earth-Science Reviews, 2022, 234: 104206, doi: 10.1016/j.earscirev.2022.104206.
[13]	张杰, 张旸, 赵振勇, 等. 中国飞蝗(Locusta migratoria)灾害地理分布模拟及其生物气候因子分析[J]. 干旱区地理, 2019, 42(3): 590-598.
	[Zhang Jie, Zhang Yang, Zhao Zhenyong, et al. Potential geographic distribution modeling and bioclimatic analysis of outbreak risk for the migratory locust plague in China[J]. Arid Land Geography, 2019, 42(3): 590-598.] doi: 10.12118/j.issn.1000-6060.2019.03.15
[14]	Mu H W, Li X C, Wen Y N, et al. A global record of annual terrestrial human footprint dataset from 2000 to 2018[J]. Scientific Data, 2022, 9: 176, doi: 10.1038/s41597-022-01284-8. pmid: 35440581
[15]	Zhang Y F, Chen S T, Gao S T, et al. Prediction of global potential suitable habitats of Nicotiana alata Link et Otto based on MaxEnt model[J]. Scientific Reports, 2023, 13: 4851, doi: 10.1038/s41598-023-29678-7.
[16]	张佳怡, 伦玉蕊, 刘浏, 等. CMIP6多模式在青藏高原的适应性评估及未来气候变化预估[J]. 北京师范大学学报(自然科学版), 2022, 58(1): 77-89.
	[Zhang Jiayi, Lun Yurui, Liu Liu, et al. CMIP6 evaluation and projection of climate change in Tibetan Plateua[J]. Journal of Beijing Normal University (Natural Science Edition), 2022, 58(1): 77-89.]
[17]	Fang J Q, Shi J F, Zhang P, et al. Potential distribution projections for Senegalia senegal (L.) Britton under climate change scenarios[J]. Forests, 2024, 15(2): 379, doi: 10.3390/f15020379.
[18]	Zhao Z Y, Xiao N W, Liu G H, et al. Prediction of the potential geographical distribution of five species of Scutiger in the south of Hengduan Mountains biodiversity conservation priority zone[J]. Acta Ecologica Sinica, 2022, 42(7): 2636-2647.
[19]	Shcheglovitova M, Anderson R P. Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes[J]. Ecological Modelling, 2013, 269: 9-17.
[20]	吴双梅, 周冬梅, 马静, 等. 不同气候情景下环县柠条锦鸡儿分布及土壤碳储量特征[J]. 干旱区地理, 2025, 48(5): 812-824. doi: 10.12118/j.issn.1000-6060.2024.407
	[Wu Shuangmei, Zhou Dongmei, Ma Jing, et al. Characteristics of Caragana korshinskii distribution and soilcarbon storage in Huan County under different climate scenarios[J]. Arid Land Geography, 2025, 48(5): 812-824.] doi: 10.12118/j.issn.1000-6060.2024.407
[21]	Shao M H, Wang L, Li B W, et al. 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[J]. Forests, 2022, 13(2): 189, doi: 10.3390/f13020189.
[22]	张明珠, 叶兴状, 李佳慧, 等. 气候变化情景下长序榆在中国的潜在适生区预测[J]. 生态学杂志, 2021, 40(12): 3822-3835.
	[Zhang Mingzhu, Ye Xingzhuang, Li Jiahui, et al. Prediction of potential suitable area of Ulmus elongata in China under climate change scenarios[J]. Chinese Journal of Ecology, 2021, 40(12): 3822-3835.]
[23]	孙淑霞. 气候变化下中国栎属物种及其丰富度潜在分布格局模拟预测研究[D]. 济南: 山东大学, 2022.
	[Sun Shuxia. The effect of climate change on the potential distribution pattern of oaks (Quercus L.) and its richness in China[D]. Jinan: Shandong University, 2022.]
[24]	李述. 干旱、半干旱区土地利用/覆盖变化与荒漠化的遥感综合研究[D]. 兰州: 兰州大学, 2006.
	[Li Shu. A synthetical study on land use/cover change and desertification in arid and semiarid region[D]. Lanzhou: Lanzhou University, 2006.]
[25]	Guo B, Wei C X, Yu Y, et al. The dominant influencing factors of desertification changes in the source region of Yellow River: Climate change or human activity?[J]. Science of the Total Environment, 2022, 813: 152512, doi: 10.1016/j.scitotenv.2021.152512.
[26]	高晓宇, 郝海超, 张雪琪, 等. 中国西北干旱区植被水分利用效率变化对气象要素的响应——以新疆为例[J]. 干旱区地理, 2023, 46(7): 1111-1120. doi: 10.12118/j.issn.1000-6060.2022.545
	[Gao Xiaoyu, Hao Haichao, Zhang Xueqi, et al. Responses of vegetation water use efficiency to meteorological factors in arid areas of northwest China: A case of Xinjiang[J]. Arid Land Geography, 2023, 46(7): 1111-1120.] doi: 10.12118/j.issn.1000-6060.2022.545
[27]	王方琳, 柴成武, 赵鹏, 等. 3种荒漠植物光合及叶绿素荧光对干旱胁迫的响应及抗旱性评价[J]. 西北植物学报, 2021, 41(10): 1755-1765.
	[Wang Fanglin, Chai Chengwu, Zhao Peng, et al. Photosynthetic and ChlorophyⅡ fluorescence responses of three desert species to drought stress and evaluation of drought resistance[J]. Acta Botanica Boreali-Occidentalia Sinica, 2021, 41(10): 1755-1765.]
[28]	褚建民. 干旱区植物的水分选择性利用研究[D]. 北京: 中国林业科学研究院, 2008.
	[Chu Jianmin. Study on water selective utilization by plants in arid regions[D]. Beijing: Chinese Academy of Forestry Sciences, 2008.]
[29]	陈林, 杨新国, 宋乃平, 等. 干旱半干旱地区植物叶片水分吸收性状[J]. 浙江大学学报(农业与生命科学版), 2013, 39(5): 565-574.
	[Chen Lin, Yang Xinguo, Song Naiping, et al. Leaf water uptake strategy of plants in the arid and semi-arid region of Ningxia[J]. Journal of Zhejiang University (Agriculture and Life Sciences Edition), 2013, 39(5): 565-574.]
[30]	郑新军, 李嵩, 李彦. 准噶尔盆地荒漠植物的叶片水分吸收策略[J]. 植物生态学报, 2011, 35(9): 893-905. doi: 10.3724/SP.J.1258.2011.00893
	[Zheng Xinjun, Li Song, Li Yan. Leaf water uptake strategy of desert plants in the Junggar Basin, China[J]. Chinese Journal of Plant Ecology, 2011, 35(9): 893-905.] doi: 10.3724/SP.J.1258.2011.00893
[31]	Madouh T, Quoreshi A. The function of Arbuscular mycorrhizal fungi associated with drought stress resistance in native plants of arid desert ecosystems: A review[J]. Diversity-Basel, 2023, 15(3): 391, doi: 10.3390/d15030391.
[32]	施雅风, 沈永平, 李栋梁, 等. 中国西北气候由暖干向暖湿转型的特征和趋势探讨[J]. 第四纪研究, 2003(2): 152-164.
	[Shi Yafeng, Shen Yongping, Li Dongliang, et al. Discussion on the present climate change from warm-dry to warm wet in northwest China[J]. Quaternary Sciences, 2003(2): 152-164.]
[33]	李伟光, 易雪, 侯美亭, 等. 基于标准化降水蒸散指数的中国干旱趋势研究[J]. 中国生态农业学报, 2012, 20(5): 643-649.
	[Li Weiguang, Yi Xue, Hou Meiting, et al. Standardized precipitation evapotranspiration index shows drought trends in China[J]. Journal of Eco-Agriculture in China, 2012, 20(5): 643-649.]
[34]	阿旺, 吕汪汪, 周阳, 等. 干旱降低了气候变暖对高寒草地群落的正效应[J]. 中国科学: 地球科学, 2024, 54(10): 3278-3291.
	[A Wang, Lü Wangwang, Zhou Yang, et al. Drought decreases the positive impact of warming on an alpine grassland community[J]. Scientia Sinica (Terrae), 2024, 54(10): 3278-3291.]
[35]	Lenoir J, Gegout J, Marquet P, et al. A significant upward shift in plant species optimum elevation during the 20^th century[J]. Science, 2008, 320(5884): 1768-1771. doi: 10.1126/science.1156831 pmid: 18583610
[36]	Thuiller W. Editorial commentary on “BIOMOD-optimizing predictions of species distributions and projecting potential future shifts under global change”[J]. Global Change Biology, 2014, 20(12): 3591-3592.
[37]	Shafer S, Bartlein P, Thompson R. Potential changes in the distributions of western North America tree and shrub taxa under future climate scenarios[J]. Ecosystems, 2001, 4(3): 200-215.
[38]	魏宇晨. 亚洲中高纬植被对极端气候的响应及其模拟评估[D]. 南京: 南京信息工程大学, 2023.
	[Wei Yuchen. Response of vegetation to climate extremes in middle and high latitudes of Asia and its simulation and assessment[J]. Nanjing: Nanjing University of Information Science and Technology, 2023.]
[39]	Ni M, Vellend M. Soil properties constrain predicted poleward migration of plants under climate change[J]. New Phytologist, 2024, 241(1): 131-141.

环境因子	符号	变量
气候因子	bio1	年平均气温/℃
	bio2	昼夜温差月均值/℃
	bio3	等温性
	bio4	温度季节性
	bio5	最暖月最高温/℃
	bio6	最冷月最低温/℃
	bio7	温度年度范围/℃
	bio8	最湿季度均温/℃
	bio9	最干季度均温/℃
	bio10	最暖季度均温/℃
	bio11	最冷季度均温/℃
	bio12	年平均降水量/mm
	bio13	最湿月降水量/mm
	bio14	最干月降水量/mm
	bio15	降水量季节变异系数
	bio16	最湿季度降水量/mm
	bio17	最干季度降水量/mm
	bio18	最暖季度降水量/mm
	bio19	最冷季度降水量/mm
土壤因子	clay1	0~30 cm黏土含量/%
	clay2	30~60 cm黏土含量/%
	sand1	0~30 cm砂土含量/%
	sand2	30~60 cm砂土含量/%
地形因子	elev	海拔/m
其他因子	hfp	人类足迹指数

气候条件	纬度/°N	经度/°E	长轴/km	短轴/km	面积/km²
当代	42.95	85.18	640.68	391.92	788768.13
2041—2060年（SSP126）	42.64	85.16	646.16	407.30	826740.30
2041—2060年（SSP245）	42.87	85.19	646.30	398.86	809787.00
2041—2060年（SSP585）	42.74	85.22	645.63	404.96	821310.25
2081—2100年（SSP126）	42.94	85.17	637.36	401.22	803319.60
2081—2100年（SSP245）	42.93	85.30	651.31	403.16	824873.83
2081—2100年（SSP585）	42.52	85.20	650.18	415.93	849509.55

基于MaxEnt模型预测气候变化下准噶尔沙蒿（Artemisia songarica）在新疆的潜在分布

Predicting the potential distribution of Artemisia songarica in Xinjiang under climate change based on the MaxEnt model

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[3]	陈世泷, 孟庆凯, 戴勇, 杨立强, 吴晗. 基于CMIP6未来情景的伊犁河流域地质灾害危险性评估预测[J]. 干旱区地理, 2025, 48(4): 599-611.
[4]	姚笛, 张子文, 韩卫卫. 区分由气候变化和人类活动引起的黄河流域水储量变化的不同组分[J]. 干旱区地理, 2025, 48(2): 190-201.
[5]	常文静, 丛士翔, 王融融, 丁旭东, 余海龙, 黄菊莹. 气候变化和人类活动对毛乌素沙地NDVI变化的量化分析[J]. 干旱区地理, 2025, 48(1): 63-74.
[6]	康立民, 滕心如, 车佳航, 怀保娟. 昆仑山北坡区域积雪时空变化特征[J]. 干旱区地理, 2024, 47(9): 1462-1471.
[7]	王南, 刘泽轩, 郑江华, 仲涛, 孟乘枫. 天山冰湖分布时空特征及驱动力分析[J]. 干旱区地理, 2024, 47(9): 1472-1481.
[8]	超宝, 赵媛媛, 武海岩, 李媛, 苏宁. 2000—2020年蒙古高原生态系统服务及其对气候因子的响应[J]. 干旱区地理, 2024, 47(9): 1577-1586.
[9]	夏婷婷, 薛璇, 王灏伟, 徐文哲, 盛紫怡, 汪洋. 昆仑山北坡陆地水储量变化及其驱动因素分析[J]. 干旱区地理, 2024, 47(8): 1292-1303.
[10]	朱成刚, 陈亚宁, 张明军, 车彦军, 孙美平, 赵锐锋, 汪洋, 刘玉婷. 昆仑山北坡水资源科学考察初报[J]. 干旱区地理, 2024, 47(7): 1097-1105.
[11]	张晶, 马龙, 刘廷玺, 孙柏林, 乔子戌. 基于贺兰山青海云杉（Picea crassifolia）树轮对过去202 a最低气温的重建[J]. 干旱区地理, 2024, 47(6): 909-921.
[12]	樊静, 申彦波, 常蕊. 气候变化对太阳能资源评估典型气象年选取的影响[J]. 干旱区地理, 2024, 47(6): 922-931.
[13]	利辉, 刘铁军, 王少慧, 刘东伟. 2001—2021年内蒙古荒漠草原水分利用效率时空变化特征及影响因素研究[J]. 干旱区地理, 2024, 47(6): 993-1003.
[14]	向燕芸, 王弋, 陈亚宁, 张齐飞, 张玉杰. 基于CMIP6模式的叶尔羌河流域未来水文干旱风险预估[J]. 干旱区地理, 2024, 47(5): 798-809.
[15]	赵明杰, 王宁练, 石晨烈, 侯靖琪. 2000—2020年中亚大型湖泊湖冰物候时空变化[J]. 干旱区地理, 2024, 47(4): 561-575.