基于MaxEnt模型模拟未来气候变化下金露梅在中国的潜在适生区变化 ^*

Simulating potential suitable habitat of Dasiphora fruticosa in China under future climate change with MaxEnt

摘要

金露梅（ Dasiphora fruticosa）是高寒落叶灌丛中常见且具有代表性的灌木物种，广泛分布于北半球高山和高纬度地区，在水土保持和高寒生态屏障维持方面发挥重要作用. 研究金露梅在气候变化背景下的潜在适生区变化，有助于了解未来气候变化下高寒灌丛的响应特征. 本研究以金露梅为研究对象，选择19个气候变量、3个地形变量与3个土壤变量作为环境变量，利用金露梅现代（1961-2020年）分布点建立MaxEnt模型，分析影响金露梅分布的主要环境因子，同时模拟不同共享社会经济路径（SSPs）下未来（2081-2100年）金露梅的物种分布情况. 研究结果表明：1）模型的AUC值为0.901，模拟效果较好；2）影响金露梅分布的主要环境因子为最暖季度平均温度、年降水量和土壤养分；3）不同共享社会经济路径下，2081-2100年金露梅的适生区均呈显著减少趋势，东部地区适生区几乎消失，适生区向西部高海拔地区迁移，将集中在青藏高原及周边地区. 本研究可为金露梅的优先保护区规划和气候变化适应性管理提供科学依据.

Abstract

Dasiphora fruticosa is a typical alpine deciduous shrub species widely distributed across high mountains and high-latitude regions in the Northern Hemisphere; it plays a crucial role in soil and water conservation and in maintaining alpine ecological stability. Studying potential changes in suitable habitat of D. fruticosa under climate change conditions will help characterize alpine shrubland responses to future climatic conditions. A total of 19 climatic variables, 3 topographic variables, and 3 soil variables were selected as environmental predictors. A MaxEnt model was constructed using current (1961-2020) distribution points of D. fruticosa to identify the main environmental factors influencing its distribution. Potential future (during 2081-2100) distribution of D. fruticosa under different Shared Socioeconomic Pathways (SSPs) was simulated. This model achieved an AUC value of 0.901, indicating high predictive accuracy. The mean-temperature of warmest quarter, annual precipitation, and soil nutrient were found to be the main environmental factors influencing distribution of D. fruticosa. Suitable habitat of D. fruticosa is projected to decrease significantly during 2081-2100, with suitable habitat in the eastern regions nearly disappearing and distribution shifting westward to higher elevations, mainly concentrated on the Qinghai-Xizang Plateau and surrounding regions. This study clarifies the dominant environmental mechanisms governing distribution of D. fruticosa and warns of contraction of its distribution under future climate change; this will provide some scientific basis for priority conservation area planning and climate-adaptive management of D. fruticosa.

关键词

金露梅 / 气候变化 / MaxEnt模型 / 物种分布

Key words

Dasiphora fruticosa / climate change / MaxEnt model / species distribution

参考文献

[1] 周华坤， 周立， 赵新全， 等 . 金露梅灌丛地下生物量形成规律的研究[J]. 草业学报， 2002， 11（2）： 59

[2] 王启基， 周兴民， 张堰青， 等 . 青藏高原金露梅灌丛的结构特征及其生物量[J]. 西北植物学报， 1991， 11（4）： 333

[3] 苏爱玲， 徐广平， 段吉闯， 等 . 祁连山金露梅灌丛草甸群落结构及主要种群的点格局分析[J]. 西北植物学报， 2010， 30（6）： 1231

[4] 苏培玺， 陈怀顺， 周紫鹃， 等 . 金露梅与格桑花[J]. 植物科学学报， 2020， 38（6）： 844

[5] 内蒙古植物志编委会 . 内蒙古植物志：第三卷[M]. 2版. 呼和浩特： 内蒙古人民出版社， 1989

[6] 张勇， 王一峰 . 国产委陵菜属植物资源[J]. 西北师范大学学报（自然科学版）， 1998， 34（1）： 59

[7] 安栩立， 张振华， 杨建平 . 青藏高原高寒灌木金露梅扩张态势分析[J]. 草业科学， 2024， 41（12）： 2823

[8] 苏强， 鲁娅娜， 刘矜杰 . 内蒙古贺兰山不同生境下小叶金露梅种群特征与空间格局研究[J]. 内蒙古林业， 2021（12）： 35

[9] 吴国有 . 祁连山金露梅野生资源分布与人工引种栽培[J]. 西北园艺， 2024（6）： 29

[10] 刘旻霞， 李文， 连依明 . 基于Ripley K(r)函数的金露梅与蒲公英种群空间分布格局及其关联性 [J]. 生态学杂志， 2018， 37（7）： 1925

[11] 刘旻霞 . 亚高寒草甸不同坡向金露梅种群的空间分布格局及空间关联[J]. 应用生态学报， 2017， 28（6）： 1817

[12] Duan R Y, Kong X Q, Huang M Y, et al. The predictive performance and stability of six species distribution models[J]. PLoS One, 2014, 9(11): e112764

[13] 许仲林， 彭焕华， 彭守璋 . 物种分布模型的发展及评价方法[J]. 生态学报， 2015， 35（2）： 557

[14] Anderson R P . A framework for using niche models to estimate impacts of climate change on species distributions[J]. Annals of the New York Academy of Sciences, 2013, 1297(1): 8

[15] Dyderski M K, Paź S, Frelich L E, et al. How much does climate change threaten European forest tree species distributions?[J]. Global Change Biology, 2018, 24(3): 1150

[16] Mainali K P, Warren D L, Dhileepan K, et al. Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling[J]. Global Change Biology, 2015, 21(12): 4464

[17] Guo Y L, Li X, Zhao Z F, et al. Prediction of the potential geographic distribution of the ectomycorrhizal mushroom Tricholoma matsutake under multiple climate change scenarios [J]. Scientific Reports, 2017, 7: 46221

[18] Mccune J L . Species distribution models predict rare species occurrences despite significant effects of landscape context[J]. Journal of Applied Ecology, 2016, 53(6): 1871

[19] 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

[20] Guisan A, Thuiller W . Predicting species distribution: offering more than simple habitat models[J]. Ecology Letters, 2005, 8(9): 993

[21] Elith J, Leathwick J R . Species distribution models: ecological explanation and prediction across space and time[J]. Annual Review of Ecology, Evolution, and Systematics, 2009, 40: 677

[22] Kelly A E, Goulden M L . Rapid shifts in plant distribution with recent climate change[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(33): 11823

[23] 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

[24] 中国植物志编委会 . 中国植物志[M]. 北京： 科学出版社， 1985

[25] Hijmans R J, Phillips S, Leathwick J, et al. Package ‘dismo’[J]. Circles, 2017, 9(1): 1

[26] Peng S Z, Ding Y X, Liu W Z, et al. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017[J]. Earth System Science Data, 2019, 11(4): 1931

[27] Wu T W, Lu Y X, Fang Y J, et al. The Beijing climate center climate system model (BCC-CSM): the main progress from CMIP5 to CMIP6[J]. Geoscientific Model Development, 2019, 12(4): 1573

[28] 周秀华， 何莉阳， 何洁琳， 等 . 基于BCC-CSM2-MR模式的广西未来气候变化预估[J]. 气象研究与应用， 2024， 45（4）： 75

[29] 李淑萍， 全文杰， 王正， 等 . BCC-CSM2-MR全球气候模式对东亚地区降水和气温的模拟评估[J]. 干旱气象， 2023， 41（6）： 984

[30] 辛晓歌， 吴统文， 张洁 . BCC气候系统模式开展的CMIP5试验介绍[J]. 气候变化研究进展， 2012， 8（5）： 378

[31] Amatulli G, Mcinerney D, Sethi T, et al. Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers[J]. Scientific Data, 2020, 7: 162

[32] 刘峰， 张甘霖 . 中国高分辨率国家土壤信息网格基本属性数据集（2010-2018）[DS/OL]. 国家青藏高原科学数据中心（ 2021-11-30）[2025-12-29]. https://www.tpdc.ac.cn/zh-hans/data/e1ccd22c-348f-41a2-ab46-dd1a8ac0c955/

[33] Liu F, Zhang G L, Song X D, et al. High-resolution and three-dimensional mapping of soil texture of China[J]. Geoderma, 2020, 361: 114061

[34] Saxton K E, Rawls W J, Romberger J S, et al. Estimating generalized soil-water characteristics from texture[J]. Soil Science Society of America Journal, 1986, 50(4): 1031

[35] 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]. PeerJ, 2017, 5: e4095

[36] Phillips S J, Dudík M, Schapire R E . Maxent software for modeling species niches and distributions: V3.4.1[P/OL]. 2025-12-30. http://biodiversityinformatics.amnh.org/open_source/maxent/

[37] 刘佳敏， 杜珍珠， 王玉霞， 等 . 基于Maxent模型的小叶桦中国潜在适生区预测研究[J]. 石河子大学学报（自然科学版）， 2025， 43（1）： 102

[38] Elith J, Phillips S J, Hastie T, et al. A statistical explanation of MaxEnt for ecologists[J]. Diversity and Distributions, 2011, 17(1): 43

[39] Phillips S J, Dudík M . Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation[J]. Ecography, 2008, 31(2): 161

[40] Fielding A H, Bell J F . A review of methods for the assessment of prediction errors in conservation presence/absence models[J]. Environmental Conservation, 1997, 24(1): 38

[41] Merow C, Smith M J, Silander J A Jr . A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter[J]. Ecography, 2013, 36(10): 1058

[42] 柳晓燕， 李俊生， 赵彩云， 等 . 基于MAXENT模型和ArcGIS预测豚草在中国的潜在适生区[J]. 植物保护学报， 2016， 43（6）： 1041

[43] Swets J A . Measuring the accuracy of diagnostic systems[J]. Science, 1988, 240(4857): 1285

[44] Cao C C, Su F L, Song F, et al. Distribution and disturbance dynamics of habitats suitable for Suaeda salsa [J]. Ecological Indicators, 2022, 140: 108984

[45] 李康杰， 邓海燕， 邹文涛 . 气候变化情景下枫香在中国的潜在地理分布[J]. 中南林业科技大学学报， 2025， 45（4）： 128

[46] Vasseur D A, Delong J P, Gilbert B, et al. Increased temperature variation poses a greater risk to species than climate warming[J]. Proceedings Biological Sciences, 2014, 281(1779): 20132612

[47] Körner C, Larcher W . Plant life in cold climates[C]//Long S P, Woodward F I. Plants and Temperature. Cambridge: The Company of Biologists Limited, 1988: 25

[48] Häkkinen H, Hodgson D, Early R . Plant naturalizations are constrained by temperature but released by precipitation[J]. Global Ecology and Biogeography, 2022, 31(3): 501

[49] Svoboda M D, Fuchs B A . Handbook of drought indicators and indices[M]. Geneva: World Meteorological Organization, 2016

[50] Harpole W S, Ngai J T, Cleland E E, et al. Nutrient co-limitation of primary producer communities[J]. Ecology Letters, 2011, 14(9): 852

[51] Yang G H, Zhang M M, Jin G Z . Effects of nitrogen addition on species composition and diversity of early spring herbs in a Korean pine plantation[J]. Ecology and Evolution, 2023, 13(9): e10498

[52] Zhao Z Y, Dong P, Fu B, et al. Soil organic carbon distribution and factors affecting carbon accumulation in natural and plantation forests in tropical China[J]. Ecological Indicators, 2023, 148: 110127

[53] John R, Dalling J W, Harms K E, et al. Soil nutrients influence spatial distributions of tropical tree species[J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(3): 864

[54] Condit R, Engelbrecht B M J, Pino D, et al. Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees[J]. Proceedings of the National Academy of Sciences of the United States of America, 2013, 110(13): 5064

[55] Liu M X, Li B W, Xu L, et al. Characteristics of culturable microbial community in rhizosphere/non-rhizosphere soil of Potentilla fruticosa population in alpine meadow elevation gradient [J]. Frontiers in Soil Science, 2021, 1: 741012

[56] Sherry R A, Zhou X H, Gu S L, et al. Divergence of reproductive phenology under climate warming[J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(1): 198

[57] Mazer S J, Travers S E, Cook B I, et al. Flowering date of taxonomic families predicts phenological sensitivity to temperature: implications for forecasting the effects of climate change on unstudied taxa[J]. American Journal of Botany, 2013, 100(7): 1381

[58] 邓东周， 贺丽， 鄢武先， 等 . 温度和干旱胁迫对川西北高寒沙区3种优势植物种子萌发的影响[J]. 种子， 2019， 38（3）： 15

[59] Guo Y T, Wang Z, Guan X L, et al. Proteomic analysis of Potentilla fruticosa L. leaves by iTRAQ reveals responses to heat stress [J]. PLoS One, 2017, 12(8): e0182917

[60] Farooq M, Wahid A, Kobayashi N, et al. Plant drought stress: effects, mechanisms and management[M]// Sustainable Agriculture. Dordrecht: Springer Netherlands, 2009: 153

[61] 刘颖， 贺康宁， 徐特， 等 . 水分胁迫对金露梅叶片水势、光合特性和水分利用效率的影响[J]. 中国水土保持科学， 2016， 14（1）： 106

[62] Qin Y Y, Liu W, Zhang X F, et al. Leaf stoichiometry of Potentilla fruticosa across elevations in China’s Qilian Mountains [J]. Frontiers in Plant Science, 2022, 13: 814059

[63] Shao G F, Shugart H H, Smith T M . A role-type model (rope) and its application in assessing climate change impacts on forest landscapes[J]. Vegetatio, 1995, 121(1): 135

[64] Song M H, Zhou C P, Ouyang H . Distributions of dominant tree species on the Tibetan (Qinghai-Xizang) Plateau under current and future climate scenarios[J]. Mountain Research and Development, 2004, 24(2): 166

[65] Gottfried M, Pauli H, Reiter K, et al. A fine-scaled predictive model for changes in species distribution patterns of high mountain plants induced by climate warming[J]. Diversity and Distributions, 1999, 5(6): 241

[66] Fosaa A M, Sykes M T, Lawesson J E, et al. Potential effects of climate change on plant species in the Faroe Islands[J]. Global Ecology and Biogeography, 2004, 13(5): 427

[67] 杜毅倩， 余国杰， 殷晓洁， 等 . 气候变化对冷蒿在我国潜在地理分布的影响[J]. 生态学报， 2025， 45（4）： 1950

[68] 孙红， 段霁芸， 刘雨洁， 等 . 气候变化背景下沙蓬属（ Agriophyllum）物种潜在地理分布 [J]. 中国沙漠， 2023， 43（2）： 255

[69] 吴建国 . 气候变化对我国7种植物潜在分布的影响[J]. 广西植物， 2011， 31（5）： 595

[70] 杨佳悦， 丁国玉， 田秀君 . 最大熵模型在物种生境预测中的应用研究进展[J]. 应用生态学报， 2025， 36（2）： 614

基金资助

第二次青藏高原综合科学考察研究资助项目(2019QZKK0306)