气候变化下脓疮草在中国的适宜分布区预测

doi:10.13866/j.azr.2025.10.09

Abstract

Abstract:

Panzerina lanata holds significant medicinal and ecological value, contributing to both human health and ecosystem balance. In this study, to investigate the suitable habitat distribution patterns of this species and its response to future climate change, we employed the MaxEnt model to simulate and predict the species’ suitable habitats and their dynamic changes under current and future (2041-2060, 2081-2100) climate change scenarios. The analysis included 86 natural distribution points and 20 environmental variables. We assessed the importance of key environmental factors by combining comprehensive contribution rates with the jackknife method. Additionally, we simulated the dispersal pathways of P. lanata using the least-cost path method using chloroplast haplotype data from 27 populations and distribution model simulation data from different periods. The results were as follows: (1) The primary environmental factors affecting the geographical distribution of P. lanata are the maximum temperature of the warmest month, elevation, precipitation of the wettest month, and temperature seasonality. (2) Under current climate conditions, the potential highly suitable area for P. lanata in China covers approximately 21.04×10⁴ km², mainly distributed in Ulanqab, Ordos, and eastern Alxa in Inner Mongolia as well as northern Ningxia, northern Shaanxi, and parts of Gansu Province. (3) Under two typical climate scenarios based on concentration pathways (SSP1-2.6 and SSP5-8.5) in the future (2081-2100), both total suitable areas and highly suitable areas of P. lanata showed an increasing trend, with the core distribution remaining in the Inner Mongolia. The east-west corridor along the northern fringe of the Mu Us Sandy Land emerged as a crucial dispersal pathway of P. lanata during population migration, with the strongest connectivity between populations in the Alxa Left Banner and Ordos regions.

Key words: Panzerina lanata, climate change, MaxEnt model, potential suitable distribution, dispersal pathway

ZHAO Yanfen, WANG Chuncheng, PAN Borong. Predicting the suitable distribution areas of Panzerina lanata in China under climate change[J].Arid Zone Research, 2025, 42(10): 1851-1859.

Figures/Tables 8

Fig. 1

Tab. 1

Fig. 2

Tab. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

References 37

[1]	方精云, 朱江玲, 石岳. 生态系统对全球变暖的响应[J]. 科学通报, 2018, 63(2): 136-140.
	[Fang Jingyun, Zhu Jiangling, Shi Yue. The responses of ecosystems to global warming[J]. Chinese Science Bulletin, 2018, 63(2): 136-140. ]
[2]	Urban M C. Climate change extinctions[J]. Science, 2024, 386(6726): 1123-1128. doi: 10.1126/science.adp4461 pmid: 39636977
[3]	周天军, 陈梓明, 陈晓龙, 等. IPCC AR6报告解读: 未来的全球气候——基于情景的预估和近期信息[J]. 气候变化研究进展, 2021, 17(6): 652-663.
	[Zhou Tianjun, Chen Ziming, Chen Xiaolong, et al. Interpreting IPCC AR6: Future global climate based on projection under scenarios and on near-term information[J]. Climate Change Research, 2021, 17(6): 652-663. ]
[4]	孙萌, 张子龙. 药用植物对气候变化响应研究进展[J]. 生物学杂志, 2015, 32(5): 84-88.
	[Sun Meng, Zhang Zilong. Research progress in medicinal plants response to climate change[J]. Journal of Biology, 2015, 32(5): 84-88. ]
[5]	张亮, 魏彦强, 王金牛, 等. 气候变化情景下黑果枸杞的潜在地理分布[J]. 应用与环境生物学报, 2020, 26(4): 969-978.
	[Zhang Liang, Wei Yanqiang, Wang Jinniu, et al. The potential geographical distribution of Lycium ruthenicum Murr. under different climate change scenarios[J]. Chinese Journal of Applied and Environmental Biology, 2020, 26(4): 969-978. ]
[6]	查苏娜, 琪波热, 胡红霞, 等. 气候变化背景下濒危药用植物手参在中国潜在适生区预测[J]. 应用生态学报, 2024, 35(11): 3023-3030. doi: 10.13287/j.1001-9332.202411.023
	[Cha Suna, Qi Bore, Hu Hongxia, et al. Prediction of the potential distribution area of endangered medicinal plant Gymnadenia conopsea in China under the background of climate change[J]. Chinese Journal of Applied Ecology, 2024, 35(11): 3023-3030. ] doi: 10.13287/j.1001-9332.202411.023
[7]	李国庆, 刘长成, 刘玉国, 等. 物种分布模型理论研究进展[J]. 生态学报, 2013, 33(16): 4827-4835.
	[Li Guoqing, Liu Changcheng, Liu Yuguo, et al. Advances in theoretical issues of species distribution models[J]. Acta Ecologica Sinica, 2013, 33(16): 4827-4835. ]
[8]	欧阳泽怡, 欧阳硕龙, 吴际友, 等. 最大熵模型在植物适生区预测应用中的研究进展[J]. 湖南林业科技, 2022, 49(1): 83-88.
	[Ouyang Zeyi, Ouyang Shuolong, Wu Jiyou, et al. Progress of the application of prediction of potential suitable distribution of plants based on Maxent[J]. Hunan Forestry Science & Technology, 2022, 49(1): 83-88. ]
[9]	樊玉科, 任菊, 王润龙, 等. 气候变化背景下白皮松在中国潜在适宜分布预测[J]. 干旱区研究, 2024, 41(10): 1719-1730. doi: 10.13866/j.azr.2024.10.10
	[Fan Yuke, Ren Ju, Wang Runlong, et al. Prediction of potential suitable distribution area of Pinus bungeana in China under the background of climate change[J]. Arid Zone Research, 2024, 41(10): 1719-1730. ] doi: 10.13866/j.azr.2024.10.10
[10]	Yang Z B, Bai Y, Alatalo J M, et al. Spatio-temporal variation in potential habitats for rare and endangered plants and habitat conservation based on the maximum entropy model[J]. Science of the Total Environment, 2021, 784: 147080.
[11]	Rana H K, Luo D, Rana S K, et al. Geological and climatic factors affect the population genetic connectivity in Mirabilis himalaica(Nyctaginaceae): Insight from phylogeography and dispersal corridors in the Himalaya-Hengduan biodiversity hotspot[J]. Frontiers in Plant Science, 2020, 10: 1721.
[12]	瞿宇阳, 文田田, 刘佳敏, 等. 气候变化下菟丝子属3种植物在中国的潜在地理分布[J]. 干旱区研究, 2025, 42(1): 97-107. doi: 10.13866/j.azr.2025.01.09
	[Qu Yuyang, Wen Tiantian, Liu Jiamin, et al. Potential geographic distribution of the three species of Cuscuta in China under climate scenarios[J]. Arid Zone Research, 2025, 42(1): 97-107. ] doi: 10.13866/j.azr.2025.01.09
[13]	于海彬, 张镱锂, 李士成, 等. 基于GIS和物种分布模型的高山植物长花马先蒿迁移路线模拟[J]. 应用生态学报, 2014, 25(6): 1669-1673.
	[Yu Haibin, Zhang Yili, Li Shicheng, et al. Predicting the dispersal routes of alpine plant Pedicularis longiflora (Orobanchaceae) based on GIS and species distribution models[J]. Chinese Journal of Applied Ecology, 2014, 25(6): 1669-1673. ] pmid: 25223022
[14]	郭彦龙, 赵泽芳, 乔慧捷, 等. 物种分布模型面临的挑战与发展趋势[J]. 地球科学进展, 2020, 35(12): 1292-1305. doi: 10.11867/j.issn.1001-8166.2020.110
	[Guo Yanlong, Zhao Zefang, Qiao Huijie, et al. Challenges and development trend of species distribution model[J]. Advances in Earth Science, 2020, 35(12): 1292-1305. ] doi: 10.11867/j.issn.1001-8166.2020.110
[15]	赵一之, 赵利清, 曹瑞. 内蒙古植物志[M]. 呼和浩特: 内蒙古人民出版社, 2020: 220-221.
	[Zhao Yizhi, Zhao Liqing, Cao Rui. Flora Intramongolica[M]. Hohhot: Inner Mongolia People’s Press, 2020: 220-221. ]
[16]	雷啟芬, 赵湘培, 朱丹. 蒙药白益母草的化学成分研究及临床应用进展[J]. 中华中医药杂志, 2017, 32(7): 3083-3085.
	[Lei Qifen, Zhao Xiangpei, Zhu Dan. Progress of Mongolian herb Panzeria alaschanica Kupr. in chemical components and clinical application[J]. China Journal of Traditional Chinese Medicine and Pharmacy, 2017, 32(7): 3083-3085. ]
[17]	郑亚夫, 尹伟, 林莉莉, 等. 白益母草的研究进展[J]. 中草药, 2007, 38(9): 1434-1436.
	[Zheng Yafu, Yin Wei, Lin Lili, et al. Advances in studies on Panzeria alaschanica[J]. Chinese Traditional and Herbal Drugs, 2007, 38(9): 1434-1436. ]
[18]	赵一之. 内蒙古珍稀濒危植物图谱[M]. 北京: 中国农业科技出版社, 1992: 56.
	[Zhao Yizhi. Rare and Endangered Plants in Inner Mongolia[M]. Beijing: China Agricultural Science and Technology Press, 1992: 56. ]
[19]	米盈盈. 蒙药白益母草生药学及抗炎活性研究[D]. 北京: 中央民族大学, 2021.
	[Mi Yingying. Study on Pharmacognosy and Anti-inflammatory Activity of Mongolian Medicine Panzerina alaschanica[D]. Beijing: Minzu University of China, 2021. ]
[20]	Zhao Y F, Zhang H X, Pan B R, et al. Intraspecific divergences and phylogeography of Panzerina lanata (Lamiaceae) in Northwest China[J]. Peer J, 2019, 7: e6264.
[21]	杨康, 赵湘培, 张恂, 等. 基于MaxEnt生态位模型预测蒙药白益母草潜在分布研究[J]. 中药材, 2021, 44(8): 1827-1831.
	[Yang Kang, Zhao Xiangpei, Zhang Xun, et al. Prediction of potential distribution of Mongolian medicine Panzerina lanata var. alaschanica based on MaxEnt niche model[J]. Journal of Chinese Medicinal Materials, 2021, 44(8): 1827-1831. ]
[22]	王庆莉, 王茹琳, 张利平, 等. 基于MaxEnt模型的川西高原松茸气候生态适宜性与潜在分布[J]. 应用生态学报, 2021, 32(7): 2525-2533. doi: 10.13287/j.1001-9332.202107.013
	[Wang Qingli, Wang Rulin, Zhang Liping, et al. Climatic ecological suitability and potential distribution of Tricholoma matsutake in western Sichuan Plateau, China based on MaxEnt model[J]. Chinese Journal of Applied Ecology, 2021, 32(7): 2525-2533. ] doi: 10.13287/j.1001-9332.202107.013
[23]	Phillips S J, Dudík M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation[J]. Ecography, 2008, 31(2): 161-175.
[24]	陈禹光, 乐新贵, 陈宇涵, 等. 基于MaxEnt模型预测气候变化下杉木在中国的潜在地理分布[J]. 应用生态学报, 2022, 33(5):1207-1214. doi: 10.13287/j.1001-9332.202205.024
	[Chen Yuguang, Le Xingui, Chen Yuhan, et al. Identification of potential distribution area of Cunninghamia lanceolata in China under climate change based on the MaxEnt model[J]. Chinese Journal of Applied Ecology, 2022, 33(5): 1207-1214. ] doi: 10.13287/j.1001-9332.202205.024
[25]	Zhuo Z H, Xu D P, Pu B, et al. Predicting distribution of Zanthoxylum bungeanum Maxim. in China[J]. BMC Ecology, 2020, 20(1): 46.
[26]	Zhao X F, Lei M, Wei C H, et al. 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, 2022, 852: 158202.
[27]	Brown J L, Bennett J R, French C M. SDMtoolbox 2.0: The next generation python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses[J]. Peer J, 2017, 5: e4095.
[28]	Yao S R, Wei G H, Ming F J, et al. Distribution, species richness, and relative importance of different plant life forms across drylands in China[J]. Plant Diversity, 2025, 47(2): 273-281.
[29]	王思雨, 吴建国. 未来气候变化情景下中国植物物种地理分布变化趋势研究进展[J]. 广西植物, 2025, 45(3): 500-516.
	[Wang Siyu, Wu Jianguo. Research progress on the changes in geographical distributions of plant species under future climate change scenarios in China[J]. Guihaia, 2025, 45(3): 500-516. ]
[30]	Cheryomushkina V A, Astashenkov A Y. Morphological adaptation of Panzerina sojak (Lamiaceae) species to various ecological conditions[J]. Contemporary Problems of Ecology, 2014, 7(5): 520-525.
[31]	Sun Y, Sun Y, Yao S R, et al. Impact of climate change on plant species richness across drylands in China: From past to present and into the future[J]. Ecological Indicators, 2021, 132: 108288.
[32]	Qiu J S, Zhang J W, Wang Y N. PPDC: An online platform for the prediction of plant distributions in China[J]. Journal of Plant Ecology, 2024, 17(6): 1-11. doi: 10.1093/jpe/rtae094
[33]	朱飙. 西北地区气候暖湿化背景下水汽、潜在蒸散及极端温度和降水的变化特征[D]. 兰州: 兰州大学, 2023.
	[Zhu Biao. Variation Characteristics of Vapor, Potential Evapotranspiration and Extreme Temperature and Precipitation under the Background of Warming-wetting in Northwest China[D]. Lanzhou: Lanzhou University, 2023. ]
[34]	丁一汇, 柳艳菊, 徐影, 等. 全球气候变化的区域响应: 中国西北地区气候“暖湿化”趋势、成因及预估研究进展与展望[J]. 地球科学进展, 2023, 38(6): 551-562. doi: 10.11867/j.issn.1001-8166.2023.027
	[Ding Yihui, Liu Yanju, Xu Ying, et al. Regional responses to global climate change: Progress and prospects for trend, causes, and projection of climatic warming-wetting in Northwest China[J]. Advances in Earth Science, 2023, 38(6): 551-562. ] doi: 10.11867/j.issn.1001-8166.2023.027
[35]	张丹, 马松梅, 魏博, 等. 中国梭梭属植物历史分布格局及其驱动机制[J]. 生物多样性, 2022, 30(1): 42-51.
	[Zhang Dan, Ma Songmei, Wei Bo, et al. Historical distribution pattern and driving mechanism of Haloxylon in China[J]. Biodiversity Science, 2022, 30(1): 42-51. ]
[36]	Ma S M, Nie Y B, Jiang X L, et al. Genetic structure of the endangered, relict shrub Amygdalus mongolica (Rosaceae) in arid Northwest China[J]. Australian Journal of Botany, 2019, 67(2): 128-139.
[37]	孙志霞. 基于遗传和地理数据的鄱阳湖流域植物迁移通道研究[D]. 南昌: 江西农业大学, 2017.
	[Sun Zhixia. Dispersal Corridors for Plant Species in Poyang Lake Basin Identified by Integration of Genetic and Geospatial Data[D]. Nanchang: Jiangxi Agricultural University, 2017. ]

代码	环境变量	贡献率/%	置换重要值/%
Bio5	最暖月最高温	30.4	22.6
Bio13	最湿月降水量	26.7	39.2
Ele	海拔	20.5	15.5
Bio4	温度季节性变化	18.7	14.7
Bio15	降水量季节性变化	2.0	4.5
Bio2	平均日较差	1.0	3.3
Bio14	最干月降水量	0.6	0.2

时期	情景	总适生区	高适生区	中适生区	低适生区
当前		58.00	21.04	12.11	24.86
2041—2060年	SSP1-2.6	56.95	21.49	10.91	24.55
2041—2060年	SSP5-8.5	58.51	20.68	12.25	25.58
2081—2100年	SSP1-2.6	65.32	22.29	14.22	28.81
2081—2100年	SSP5-8.5	60.69	22.37	13.09	25.22

Predicting the suitable distribution areas of Panzerina lanata in China under climate change

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 37

Related Articles 15

Metrics

Comments

Recommended 0

[1]	SHANG Shujing, LIU Danhui, ZHOU Yixin, WU Jiaju, LU Ting, LI Wenjun. Prediction of the suitable distribution areas of Arnebia euchroma (Boraginaceae) in China under change climate conditions [J]. Arid Zone Research, 2025, 42(9): 1628-1639.
[2]	JIANG Anyao, CHEN Ruishan, ZHENG Lilin, GUO Xiaona, SUN Nansha, LI Yinshuai. Spatiotemporal evolution characteristics and impacts of extreme precipitation in the Three-North Shelterbelt Forest Program region [J]. Arid Zone Research, 2025, 42(8): 1357-1368.
[3]	YAN Yingcun, SUN Shujiao, YU Di, GAO Guisheng. Impact and trend estimation of climate change on vegetation greenness in the Qaidam Basin [J]. Arid Zone Research, 2025, 42(7): 1257-1268.
[4]	SHAO Junjie, TAO Tonglian, LI Zhizhong. Late Holocene climate change recorded by grain size and trace elements in sediments from the southern margin of the Gurbantunggut Desert [J]. Arid Zone Research, 2025, 42(5): 788-799.
[5]	YUE Shengru, HU Xuefei, HOU Xiaohua, MENG Fujun. Cotton production assessment in the Tarim River Basin based on CMIP6 models [J]. Arid Zone Research, 2025, 42(10): 1925-1938.
[6]	ZOU Bin, ZOU Shan, YANG Yuhui. Dynamic changes and driving factors of surface water body in Xinjiang from 1990 to 2023 [J]. Arid Zone Research, 2025, 42(1): 40-50.
[7]	QU Yuyang, WEN Tiantian, LIU Jiamin, YAN Ping. Potential geographic distribution of the three species of Cuscuta in China under climate scenarios [J]. Arid Zone Research, 2025, 42(1): 97-107.
[8]	CHEN Songqing, DONG Hongfang, YUE Yifeng, HAO Yuanyuan, LIU Xin, CAO Xianyu, MA Jun. Geographical distribution and dynamic change prediction of Hippophae rhamnoides subsp. sinensis under different climate scenarios [J]. Arid Zone Research, 2024, 41(9): 1560-1571.
[9]	LYU Zhuangzhuang, QIAO Qingqing, DONG Sunyi, WANG Dong. Paleoclimatic evolution and driving mechanisms in arid areas of inland Asia during the Middle Miocene Climatic Optimum in the context of global climate warming [J]. Arid Zone Research, 2024, 41(8): 1309-1322.
[10]	ZHOU Jie, WANG Xuhu, DU Weibo, ZHOU Xiaolei, YANG Jie, ZAHNG Xiaowei. Prediction of potential distribution area of Picea schrenkiana under the background of climate change [J]. Arid Zone Research, 2024, 41(7): 1167-1176.
[11]	LIANG Shuanghe, NIU Zuirong, JIA Ling. Analysis of runoff changes and attribution in the main stream of Zuli River in the past 65 years [J]. Arid Zone Research, 2024, 41(6): 928-939.
[12]	TANG Kexin, GUO Jianbin, HE Liang, CHEN Lin, WAN Long. Characteristics of the spatial and temporal evolution of Gross Primary Productivity and its influencing factors in China’s drylands [J]. Arid Zone Research, 2024, 41(6): 964-973.
[13]	SU Canxia, CHU Wenwen, Bahatibieke PIELIZHATI, JIANG Xiaoheng, CHEN Yanqiu, HUANG Wenpu, MA Chi, CHU Hongjun. Potential distribution changes of Castor fiber birulai under climate changes in the upper reaches of the Ulungur River，Xinjiang [J]. Arid Zone Research, 2024, 41(3): 509-520.
[14]	ZHANG Jiaqi, LIU Zhao, HAN Zhongqing, WANG Lixia, ZHANG Jinxia, YUE Jiayin, GUAN Zilong. Trend change and prediction of blue-green water in the Jinghe River Basin under climate change [J]. Arid Zone Research, 2024, 41(12): 2045-2055.
[15]	ZHANG Qian, CAO Guangchao, ZHANG Lele, ZHAO Meiliang. Spatiotemporal changes in vegetation greenness on the southern slopes of the Qilian Mountains and their responses to climate change and human activities [J]. Arid Zone Research, 2024, 41(12): 2143-2153.