气候变化背景下白皮松在中国潜在适宜分布预测

doi:10.13866/j.azr.2024.10.10

摘要/Abstract

摘要：

研究旨在预测白皮松潜在适生区分布及气候变化对其的影响，明确其未来适宜的分布范围，为白皮松保护和其在生态工程建设中的应用提供参考。基于白皮松83个野生分布点及气候因子数据，利用MaxEnt模型和ArcGIS软件，模拟白皮松当前和未来3种（2080—2100年，低温室气体排放情景、中温室气体排放情景和高温室气体排放情景）气候变化情景（SSP126、SSP370、SSP585）下的潜在适生区分布。MaxEnt模型模拟结果AUC（受试者工作特征曲线下面积值）值均>0.973，预测结果精度较高。当前气候条件下，白皮松的潜在适生区主要分布在陕西省、山西省南部、甘肃省东南部、河南省和湖北省西北部等地，总面积约74.5×10⁴ km²，未来气候变化背景下，核心适生区面积均有不同程度的缩减，温度为白皮松潜在适生区分布的主限制因子；中、低温室气体排放情景下，白皮松潜在适生区分布的限制因子仍以温度为主；高温室气体排放情景下，全球温度升高加快，降雨量成为影响白皮松适生区分布的主要限制因子，白皮松适生区质心向东偏移，尤其在温室气体高排放浓度下更敏感，迁移距离更远。本文结合白皮松当前和未来潜在适生区的变化，提出了对白皮松保护的建议，且对利用白皮松进行生态工程建设提供参考价值。

关键词: 最大熵（MaxEnt）模型, 白皮松, 气候变化, 潜在分布区, 中国

Abstract:

This study was conducted to predict the distribution of potential suitable area and the impact of climate change and to determine the appropriate distribution range in the future, which could provide a reference for the protection of Pinus bungeana and its utilization in ecological engineering construction. Based on 83 wild distribution sites of P. bungeana and climate factor data, the MaxEnt model and ArcGIS software were used to simulate the potential suitable zone distribution of P. bungeana under the present and three climate change scenarios (SSP126, SSP370, and SSP585) (2080-2100, low-level, medium-level, and high greenhouse gas emission scenarios). In the MaxEnt model simulation, the AUC (area value under the subject operating characteristic curve) was >0.973, and the prediction results were highly accurate. Under the present climate conditions, the potential suitable areas of P. bungeana were primarily distributed in Shaanxi Province, southern Shanxi Province, southeastern Gansu Province, northwestern Henan Province, and northwestern Hubei Province, with a total area of approximately 74.5×10⁴ km², under the background of future climate change. The core suitable distribution areas were reduced to different degrees, with temperature being the primary limiting factor for the distribution of the potential growth zones of P. bungeana. Under low and medium greenhouse gas emission scenarios, temperature still remained the limiting factor for the distribution of the potential growth zones of P. bungeana. Under the high greenhouse gas emission scenario, the global temperature increased faster, and rainfall was the major limiting factor affecting the distribution of the suitable area of P. bungeana. The centroid of the suitable area of P. bungeana shifted eastward, especially being more sensitive under the high emission concentration of greenhouse gases, and the migration distance was farther. This study proposes the protection of P. bungeana, and the results provide a reference for ecological engineering construction using P. bungeana.

Key words: maximum entropy (MaxEnt) model, Pinus bungeana, climate change, potential distribution area, China

樊玉科, 任菊, 王润龙, 周栋栋, 潘自凯, 张晓玮, 周晓雷. 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.

FAN Yuke, REN Ju, WANG Runlong, ZHOU Dongdong, PAN Zikai, ZHANG Xiaowei, ZHOU Xiaolei. 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.

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参考文献 71

[1]	黄蕊, 徐利岗, 刘俊民. 中国西北干旱区气温时空变化特征[J]. 生态学报, 2013, 33(13): 4078-4089.
	[Huang Rui, Xu Ligang, Liu Junmin. Research on spatio-temporal change of temperature in the Northwest Arid Area[J]. Acta Ecologica Sinica, 2013, 33(13): 4078-4089.]
[2]	黄小燕, 李耀辉, 冯建英, 等. 中国西北地区降水量及极端干早气候变化特征[J]. 生态学报, 2015, 35(5): 1359-1370.
	[Huang Xiaoyan, Li Yaohui, Feng Jianying, et al. Climate characteristics of precipitation and extreme drought events in Northwest China[J]. Acta Ecologica Sinica, 2015, 35(5): 1359-1370.]
[3]	张雅茜, 王淋, 包福海, 等. 应用最大熵模型预测的欧李潜在适生区分布及气候变化对其的影响[J]. 东北林业大学学报, 2023, 51(11): 54-62.
	[Zhang Yaqian, Wang Lin, Bao Fuhai, et al. Distribution of potential suitable areas of Cerasus humilis by using Maxent model and effect of climate change on it[J]. Journal of Northeast Forestry University, 2023, 51(11): 54-62.]
[4]	Stocker D B, Zscheischler J, Keenan F T, et al. Drought impacts on terrestrial primary production underestimated by satellite monitoring[J]. Nature Geoscience, 2019, 12(4): 264-270.
[5]	王卓妮, 袁佳双, 庞博, 等. IPCC AR6 WGⅢ报告减缓主要结论、亮点和启示[J]. 气候变化研究进展, 2022, 18(5): 531-537.
	[Wang Zhuoni, Yuan Jiashuang, Pang Bo, et al. The interpretation and highlights on mitigation of climate change in lPCC AR6 WGШ report[J]. Advances in Climate Change Research, 2022, 18(5): 531-537.]
[6]	陈建国, 杨扬, 孙航. 高山植物对全球气候变暖的响应研究进展[J]. 应用与环境生物学报, 2011, 17(3): 435-446.
	[Chen Jianguo, Yang Yang, Sun Hang. Advances in the studies of responses of alpine plants to global warming[J]. Chinese Journal of Applied and Environmental Biology, 2011, 17(3): 435-446.]
[7]	王馨, 杨淑桂, 于芬, 等. 檫木的研究进展[J]. 南方林业科学, 2015, 43(5): 29-33, 39.
	[Wang Xin, Yang Shugui, Yu Fen, et al. Research progress of Sassafras tzumu[J]. South China Forestry Science, 2015, 43(5): 29-33, 39.]
[8]	Wiens J A, Stralberg D, Jongsomjit D, et al. Niches, models, and climate change: Assessing the assumptions and uncertainties[J]. Proceedings of the National Academy of Sciences, 2009, 106(Supplement_2): 19729-19736.
[9]	叶兴状, 张明珠, 赖文峰, 等. 基于MaxEnt优化模型的闽楠潜在适宜分布预测[J]. 生态学报, 2021, 41(20): 8135-8144.
	[Ye Xingzhuang, Zhang Mingzhu, Lai Wenfeng, et al. Prediction of potential suitable distribution of Phoebe bournei based on MaxEnt optimization model[J]. Acta Ecologica Sinica, 2021, 41(20): 8135-8144.]
[10]	Bellard C, Bertelsmeier C, Leadley P, et al. lmpacts of climate change on the future of biodiversity[J]. Ecology Letters, 2012, 15(4): 365-377. doi: 10.1111/j.1461-0248.2011.01736.x pmid: 22257223
[11]	Busby J R. BIOCLIM-a bioclimate analysis and prediction system[J]. Plant Protection Quarterly (Australia), 1991, 6(1): 8-9.
[12]	魏博, 孙芳芳, 马新, 等. 荒漠濒危植物裸果木适宜分布区对未来气候变化情景的可能响应[J]. 石河子大学学报(自然科学版), 2019, 37(4): 490-497.
	[Wei Bo, Sun Fangfang, Ma Xin, et al. Possible response of the suitable distribution areas of endangered desert plant Gymnocarpos przewalskii to future climate change scenario[J]. Journal of Shihezi University (Natural Science), 2019, 37(4): 490-497.]
[13]	Stockwell D. The GARP modelling system: Problems and solutions to automated spatial prediction[J]. International Journal of Geographical Information Science, 1999, 13(2): 143-158.
[14]	Hastie T, Tibshirani R. Generalized additive models: Some applications[J]. Journal of the American Statistical Association, 1987, 82(398): 371-386.
[15]	Nelder J A, Wedderburn R W M. Generalized linear models[J]. Journal of the Royal Statistical Society Series A: Statistics in Society, 1972, 135(3): 370-384.
[16]	Beaumont Linda J Hughes Lesley, Pitman A J. Why is the choice of future climate scenarios for species distribution modelling important?[J]. Ecology Letters, 2008, 11: 1135-1146. doi: 10.1111/j.1461-0248.2008.01231.x pmid: 18713269
[17]	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): 557-567.
	[Xu Zhonglin, Peng Huanhua, Peng Shouzhang. The development and evaluation of species distribution models[J]. Acta Ecologica Sinica, 2015, 35(2): 557-567.]
[18]	Merow C, Smith M J, Edwards Jr T C, et al. What do we gain from simplicity versus complexity in species distribution models?[J]. Ecography, 2014, 37(12): 1267-1281.
[19]	Merow C, Smith M J, Silander Jr J A. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter[J]. Ecography, 2013, 36(10): 1058-1069.
[20]	王晓帆, 段雨萱, 金露露, 等. 基于优化的最大熵模型预测中国高山栎组植物的历史、现状与未来分布变化[J]. 生态学报, 2023, 43(16): 6590-6604.
	[Wang Xiaofan, Duan Yuxuan, Jin Lulu, et al. Prediction of historical, present and future distribution of Quercus sect. Heterobalanus based on the optimized MaxEnt model in China[J]. Acta Ecologica Sinica, 2023, 43(16): 6590-6604.]
[21]	Phillips S, Anderson R, Schapire R, et al. Maximum entropy modeling of species geographic distribution[J]. Ecological Modelling, 2013, 190: 231-259.
[22]	方文培, 张泽荣. 中国植物志第五十二卷第二分册[M]. 北京: 科学出版社, 1983: 64-65.
	[Fang Wenpei, Zhang Zerong. Flora of China Volume 52, Volume 2[M]. Beijing: Science Press, 1983: 64-65.]
[23]	Moosavi S J, Budde K B, Mueller M, et al. Genetic diversity and fine-scale spatial genetic structure of the near-threatened Pinus gerardiana in Gardiz, Afghanistan[J]. Plant Ecology and Evolution, 2022, 155(3): 363-378.
[24]	彭重华, 薄楠林. 白皮松研究进展[J]. 中国农学通报, 2007, 23(11): 174-178.
	[Peng Chonghua, Bo Nanlin. Research progress in Pinus bungeana[J]. Chinese Agricultural Science Bulletin, 2007, 23(11): 174-178.]
[25]	李斌, 顾万春. 白皮松分布特点与研究进展[J]. 林业科学研究, 2003, 16(2): 225-232.
	[Li Bin, Gu Wanchun. Distribution characteristics and research progress of Pinus bungeana[J]. Forest Research, 2003, 16(2): 225-232.]
[26]	张建国, 李吉跃, 姜金璞. 京西山区人工林水分参数的研究(Ⅲ)[J]. 北京林业大学学报, 1994, 16(4): 46-54.
	[Zhang Jianguo, Li Jiyue, Jiang Jinpu. Study on water parameters of plantation in mountainous area of Western Beijing (Ⅲ)[J]. Journal of Beijing Forestry University, 1994, 16(4): 46-54.]
[27]	张力强. 白皮松群落结构与生产力研究[D]. 北京: 北京林业大学, 2007.
	[Zhang Liqiang. Study on Community Structure and Productivity of Pinus bungeana Forest[D]. Beijing: Beijing Forestry University, 2007.]
[28]	岳华峰, 吴晗彬, 樊巍, 等. 白皮松天然林更新与种群结构分析[J]. 中南林业科技大学学报, 2023, 43(3): 99-106, 154.
	[Yue Huafeng, Wu Hanbin, Fan Wei, et al. Regeneration and population structure of natural Pinus bungeana forest[J]. Journal of Central South University of Forestry-Technology, 2023, 43(3): 99-106, 154.]
[29]	赵焱, 张学忠, 王孝安. 白皮松天然林地理分布规律研究[J]. 西北植物学报, 1995, 15(2): 161-166.
	[Zhao Yan, Zhang Xuezhong, Wang Xiao’an. A Study on geographical distribution law of Pinus bungeana natural, forests in China[J]. Acta Botanica Boreali-Occidentalia Sinica, 1995, 15(2): 161-166.]
[30]	Tian Q, El-Kassaby Y A, Li W. Revealing the genetic structure and differentiation in endangered Pinus bungeana by genome-wide SNP markers[J]. Forests, 2022, 13(2): 326.
[31]	邓鑫. 基于Elman神经网络的白皮松潜在适生区估测及未来分布趋势研究[D]. 西安: 西北大学, 2013.
	[Deng Xin. The Application of Elman Artificial Neural Networks for Modelling the Distribution of Pinus bungeana in Present and Future[D]. Xi’an: Northwest University, 2013.]
[32]	唐梦, 陈静, 杨灵懿, 等. 气候变化下中国主要生物燃油树种分布与变迁[J]. 生态学报, 2023, 43(24): 10156-10170.
	[Tang Meng, Chen Jing, Yang Lingyi, et al. Distribution and change of major biofuel tree species in China under climate change[J]. Acta Ecologica Sinica, 2023, 43(24): 10156-10170.]
[33]	张华, 赵浩翔, 徐存刚. 气候变化背景下孑遗植物桫椤在中国的潜在地理分布[J]. 生态学杂志, 2021, 40(4): 968-979.
	[Zhang Hua, Zhao Haoxiang, Xu Cungang. The potential geographical distribution of Alsophila spinulosain under climate change in China[J]. Chinese Journal of Ecology, 2021, 40(4): 968-979.]
[34]	张艳芳. 基于MaxEnt模型预测花烟草全球潜在适生区的研究[D]. 泰安: 山东农业大学, 2024.
	[Zhang Yanfang. Prediction of Global Potential Suitable Habitats of Nicotiana alata Link et Otto Based on MaxEnt Model abstract[D]. Tai’an: Shandong Agricultural University, 2024.]
[35]	宋金岳. 基于生态位模型的气候变化情景下红火蚁在中国的适生区预测[D]. 兰州: 西北师范大学, 2022.
	[Song Jinyue. Prediction of Suitable habitats of Solenopsis invicta in China under Climate Change Scenarios Based on Niche Model[D]. Lanzhou: Northwest Normal University, 2022.]
[36]	Cobos M E, Peterson A T, Barve N, et al. Kuenm: An R package for detailed development of ecological niche models using MaxEnt[J]. PeerJ, 2019, 7: e6281.
[37]	曹雪萍, 王婧如, 鲁松松, 等. 气候变化情景下基于最大熵模型的青海云杉潜在分布格局模拟[J]. 生态学报, 2019, 39(14): 5232-5240.
	[Cao Xueping, Wang Jingru, Lu Songsong, et al. Simulation of the potential distribution patterns of Picea crassifolia in climate change scenarios based on the maximum entropy (Maxent) model[J]. Acta Ecologica Sinica, 2019, 39(14): 5232-5240.]
[38]	张晓玮, 王婧如, 王明浩, 等. 中国云杉属树种地理分布格局的主导气候因子[J]. 林业科学, 2020, 56(4): 1-11.
	[Zhang Xiaowei, Wang Jingru, Wang Minghao, et al. Dominant climatic factors influencing the geographical distribution pattern of Picea in China[J]. Scientia Silvae Sinicae, 2020, 56(4): 1-11.]
[39]	魏鹏, 张源, 何佳遥, 等. 基于MaxEnt模型分析气候变化下玉米褪绿斑驳病毒的潜在地理分布[J]. 植物保护学报, 2022, 49(5): 1367-1376.
	[Wei Peng, Zhang Yuan, He Jiayao, et al. Potential geographic distribution of maize chlorotic mottle virus under climate changes based on MaxEnt model[J]. Journal of Plant Protection, 2022, 49(5): 1367-1376.]
[40]	赖雨曈. CMIP6全球气候模式对中国地区干旱模拟能力评估与未来预估[D]. 北京: 中国气象科学研究院, 2023.
	[Lai Yutong. Assessment of Simulation Capability and Projection of Drought over China Based on CMIP6 Global Climate Models[D]. Beijing: Chinese Academy of Meteorological Sciences, 2023.]
[41]	Qin P, Xie Z, Jia B, et al. Characteristics of population exposure to climate extremes from regional to global 1.5 ℃ and 2.0 ℃ warming in CMIP6 models[J]. Environmental Research Letters, 2023, 19(1): 014018.
[42]	Yang M, Li Z, Anjum M N, et al. Projection of streamflow changes under CMIP6 scenarios in the Urumqi River head watershed, Tianshan Mountain, China[J]. Frontiers in Earth Science, 2022, 10: 857854.
[43]	Luo Neng, Guo Yan, Chou Jieming, et al. Added value of CMIP6 models over CMIP5 models in simulating the climatological precipitation extremes in China[J]. International Journal of Climatology, 2021, 42(2): 1148-1164.
[44]	史柠瑞, 朱珠, 王艳莉. 基于MaxEnt模型对2种青兰属植物在未来气候变化下潜在分布的预测研究[J]. 中国农学通报, 2023, 39(32): 115-123. doi: 10.11924/j.issn.1000-6850.casb2022-0874
	[Shi Ningrui, Zhu Zhu, Wang Yanli. Potential distribution prediction of two species of Dracocephalum L. under future climate change based on MaxEnt model[J]. Chinese Agricultural Science Bulletin, 2023, 39(32): 115-123.]
[45]	宦智群, 耿兴敏, 徐小蓉, 等. 基于MaxEnt模型分析不同气候变化情景下的黄心夜合(Michelia martinii)潜在地理分布[J]. 生态与农村环境学报, 2023, 39(10): 1277-1287.
	[Huan Zhiqun, Geng Xingmin, Xu Xiaorong, et al. Potential geographical Distribution of Michelia martinii under different climate change scenarios based on MaxEnt model[J]. Journal of Ecology and Rural Environment, 2023, 39(10): 1277-1287.]
[46]	李晓辰, 贡璐, 魏博, 等. 气候变化对新疆雪岭云杉潜在适宜分布及生态位分化的影响[J]. 生态学报, 2022, 42(10): 4091-4100.
	[Li Xiaochen, Gong Lu, Wei Bo, et al. Effects of climate change on potential distribution and niche differentiation of Picea schrenkiana in Xinjiang[J]. Acta Ecologica Sinica, 2022, 42(10): 4091-4100.]
[47]	赵儒楠, 何倩倩, 褚晓洁, 等. 气候变化下千金榆在我国潜在分布区预测[J]. 应用生态学报, 2019, 30(11): 3833-3843.
	[Zhao Runan, He Qianqian, Chu Xiaojie, et al. Prediction of potential distribution of Carpinus cordata in China under climate change[J]. Chinese Journal of Applied Ecology, 2019, 30(11): 3833-3843.]
[48]	熊中人, 张晓晨, 邹旭, 等. 中国天山花楸适生区预测及其对气候变化的响应[J]. 生态科学, 2019, 38(5): 44-51.
	[Xiong Zhongren, Zhang Xiaochen, Zou Xu, et al. Prediction of the suitable distribution and responses to climate change of Sorbus tianschanica in China[J]. Ecological Science, 2019, 38(5): 44-51.]
[49]	刘维, 赵儒楠, 圣倩倩, 等. 矮牡丹在中国的地理分布及潜在分布区预测[J]. 北京林业大学学报, 2021, 43(12): 83-92.
	[Liu Wei, Zhao Runan, Sheng Qianqian, et al. Geographical distribution and potential distribution area prediction of Paeonia jishanensis in China[J]. Journal of Beijing Forestry University, 2021, 43(12): 83-92.]
[50]	欧阳林男, 陈少雄, 张维耀, 等. 柠檬桉在中国的适生地理分布及其影响因子[J]. 生态学杂志, 2019, 38(2): 361-367.
	[Ouyang Linnan, Chen Shaoxiong, Zhang Weiyao, et al. The suitable geographic range for Corymbia citriodora in China and the influencing factors[J]. Chinese Journal of Ecology, 2019, 38(2): 361-367.]
[51]	钟淇涵. 基于物种分布模型的科尔沁沙地元宝枫潜在适生区研究[D]. 呼和浩特: 内蒙古农业大学, 2023.
	[Zhong Qihan. Study on Predicting Potential Suitable Area of Acer truncatum Bungein Horqin Sand Land Based on Species Distribution Models[D]. Hohhot: Inner Mongolia Agricultural University, 2023.]
[52]	Araujo M B, Pearson R C, Thuller W, Erhard M. Validation of species—climate impact models under climate change[J]. Global Change Biology, 2005, 11(9): 1504-1513.
[53]	应邦肯, 田阔, 郭浩宇, 等. 基于MaxEnt模型预测未来气候变化情境下红树秋茄(Kandelia obovata)在中国潜在适生区的变化[J]. 生态学报, 2024, 44(1): 224-234.
	[Ying Bangken, Tian Kuo, Guo Haoyu, et al. Predicting potential suitable habitats of Kandelia obovata in China under future climatic scenarios based on MaxEnt model[J]. Acta Ecologica Sinica, 2024, 44(1): 224-234.]
[54]	张丹红, 王效科, 张路, 等. 大比例尺土壤保持服务制图分级方法研究[J]. 生态学报, 2021, 41(4): 1391-1401.
	[Zhang Danhong, Wang Xiaoke, Zhang Lu, et al. Research on the classification methods of ecosystem service of soil retention for large-scale choropleth mapping[J]. Acta Ecologica Sinica, 2021, 41(4): 1391-1401.]
[55]	Zafirah N L A, Zulkifli Y, Mazlan H, et al. Predicting the habitat suitability of Melaleuca cajuputi based on the MaxEnt species distribution model[J]. Forests, 2021, 12(11): 1449.
[56]	李林霞, 何兰君, 席磊, 等. 中国南方松林地理替代分布规律及其气候主导因子研究[J]. 西南林业大学学报(自然科学), 2024, 44(1): 97-105.
	[Li Linxia, He Lanjun, Xi Lei, et al. Geographic alternative distribution pattern and its climate dominant factors of Pinus forests in Southern China[J]. Journal of Southwest Forestry University(Natural Science), 2024, 44(1): 97-105.]
[57]	张雷, 王琳琳, 刘世荣, 等. 生境概率预测值转换为二元值过程中4个阈值选择方法的比较评估——以珙桐和杉木生境预估为例[J]. 植物生态学报, 2017, 41(4): 387-395. doi: 10.17521/cjpe.2016.0184
	[Zhang Lei, Wang Linlin, Liu Shirong, et al. Comparative evaluation of four threshold selection methods in the process of converting habitat probability prediction values into binary values-taking the habitat prediction of Davidia involucrata and Cunninghamia lanceolata as an example[J]. Chinese Journal of Plant Ecology, 2017, 41(4): 387-395.]
[58]	Tian J, Zhang Z, Ahmed Z, et al. Projections of precipitation over China based on CMIP6 models[J]. Stochastic Environmental Research and Risk Assessment, 2021, 35: 831-848.
[59]	Wong M H G, Li R, Xu M, et al. An integrative approach to assessing the potential impacts of climate change on the Yunnan snub-nosed monkey[J]. Biological Conservation, 2013, 158: 401-409.
[60]	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.]
[61]	梁玉莲, 延晓冬. RCPs情景下中国21世纪气候变化预估及不确定性分析[J]. 热带气象学报, 2016, 32(2): 183-192.
	[Liang Yulian, Yan Xiaodong. Prediction and uncertainty analysis of climate change in China in the 21st century under RCPs scenario[J]. Journal of Tropical Meteorology, 2016, 32(2): 183-192.]
[62]	翟颖佳. 中国华北地区和西北东部干旱气候变化特征[D]. 兰州: 兰州大学, 2014.
	[Zhai Yingjia. The Characteristics of Arid Climate Change Over North China And Eastern Northwest China[D]. Lanzhou: Lanzhou University, 2014.]
[63]	郭聪聪, 沈永宝, 史锋厚. 白皮松种子休眠研究进展[J]. 南京林业大学学报(自然科学版), 2019, 43(2): 175-183. doi: 10.3969/j.issn.1000-2006.201710045
	[Guo Congcong, Shen Yongbao, Shi Fenghou. Research progress on seed dormancy of Pinus bungeana[J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2019, 43(2): 175-183.]
[64]	郭聪聪, 沈永宝, 史锋厚. 白皮松种子萌发过程中呼吸代谢和内源激素对温度变化的响应[J]. 中南林业科技大学学报, 2021, 41(3): 25-36.
	[Guo Congcong, Shen Yongbao, Shi Fenghou. Response of respiration and hormones during germination of Pinus bungeana seeds to temperature changes[J]. Journal of Central South University of Forestry-Technology, 2021, 41(3): 25-36.]
[65]	郭聪聪, 沈永宝, 史锋厚. 温度对白皮松种子萌发过程中储藏物质代谢及酶活性的影响[J]. 南京林业大学学报(自然科学版), 2023, 47(6): 25-34. doi: 10.12302/j.issn.1000-2006.202203069
	[Guo Congcong, Shen Yongbao, Shi Fenghou. Effects of temperature on storage substance metabolism and enzyme activity during seed germination of Pinus bungeana[J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2023, 47(6): 25-34.]
[66]	彭剑峰, 刘玉振, 王婷. 神农山白皮松不同龄组年轮—气候关系及PDSI重建[J]. 生态学报, 2014, 34(13): 3509-3518.
	[Peng Jianfeng, Liu Yuzhen, Wang Ting. Ring-climate relationship and PDSI reconstruction of different age groups of Pinus bungeana in Shennong Mountain[J]. Acta Ecologica Sinica, 2014, 34(13): 3509-3518.]
[67]	Young Derek J N, Michèle R Slaton, Alexander Koltunov. Temperature is positively associated with tree mortality in California subalpine forests containing whitebark pine[J]. Ecosphere, 2023, 14(2): e4400.
[68]	Yang X, Zhou B, Xu Y, et al. CMIP6 evaluation and projection of temperature and precipitation over China[J]. Advances in Atmospheric Sciences, 2021, 38: 817-830.
[69]	欧阳林男, 陈少雄, 刘学锋, 等. 赤桉在中国的适生地理区域及其对气候变化的响应[J]. 林业科学, 2019, 55(12): 1-11.
	[Ouyang Linnan, Chen Shaoxiong, Liu Xuefeng, et al. Suitable geographic range for Eucalyptus camaldulensis in China and its response to climate change[J]. Scientia Silvae Sinicae, 2019, 55(12): 1-11.]
[70]	李宁宁, 张爱平, 张林, 等. 气候变化下青藏高原两种云杉植物的潜在适生区预测[J]. 植物研究, 2019, 39(3): 395-406. doi: 10.7525/j.issn.1673-5102.2019.03.010
	[Li Ningning, Zhang Aiping, Zhang Lin, et al. Predicting potential distribution of two species of spruce in Qinghai-Tibet Plateau under climate change[J]. Bulletin of Botanical Research, 2019, 39(3): 395-406.] doi: 10.7525/j.issn.1673-5102.2019.03.010
[71]	张晓玮, 蒋玉梅, 毕阳, 等. 基于MaxEnt模型的中国沙棘潜在适宜分布区分析[J]. 生态学报, 2022, 42(4): 1420-1428.
	[Zhang Xiaowei, Jiang Yumei, Bi Yang, et al. Identification of potential distribution area for Hippophae rhamnoides subsp. sinensis by the MaxEnt model[J]. Acta Ecologica Sinica, 2022, 42(4): 1420-1428.]

数据图层编号	数据说明	数据图层编号	数据说明
Bio01	年平均温度/℃	Bio11	最冷季度的平均温度/℃
Bio02	每月温差的平均值/℃	Bio12	年降水量/mm
Bio03	等温性/%	Bio13	最湿月的降水量/mm
Bio04	温度季节性差异/%	Bio14	最干月的降水量/mm
Bio05	最热月的最高温度/℃	Bio15	季节性降水量变异系数
Bio06	最冷月的最低温度/℃	Bio16	最湿季度的降水量/mm
Bio07	温度年较差/℃	Bio17	最干季度的降水量/mm
Bio08	最湿季度的平均温度/℃	Bio18	最热季度的降水量/mm
Bio09	最干季度的平均温度/℃	Bio19	最冷季度的降水量/mm
Bio10	最热季度的平均温度/℃

代码	当前	2080—2100年
代码	当前	SSP126	SSP370	SSP585
Bio06	21.1%	24.5%	24.1%	18.9%
Bio03	16.4%	9.6%	10.5%	19.8%
Bio16	15.4%	7.7%	6.3%	0.1%
Bio19	11.6%	15.1%	13.7%	10.5%
Bio04	9.2%	14.3%	14.6%	12.5%
Bio12	2%	13.8%	14.9%	21.4%

适生区	当前面积 /10⁴ km²	2080—2100年SSP126		2080—2100年SSP370		2080—2100年SSP585
适生区	当前面积 /10⁴ km²	面积/10⁴ km²	面积变化比例/%	面积/10⁴ km²	面积变化比例/%	面积/10⁴ km²	面积变化比例/%
高适生区	23.23	19.38	-16.57	20.60	-11.32	20.48	-11.84
中适生区	22.40	22.37	-0.13	19.99	-10.75	22.46	0. 27
低适生区	28.95	29.15	0.69	30.17	4.21	31.97	10.43
总计	74.58	70.90	-4.93	70.76	-5.12	74.91	0.44