基于MaxEnt模拟欧亚大陆气候变化下叉子圆柏的潜在分布

doi:10.11707/j.1001-7488.20210805

Abstract

Abstract:

Objective: Juniperus sabina is an important tree species for sand-fixing and soil-holding in rocky mountain slopes, river valleys and sand-covered hilly areas in Europe, Asia and America, which is of great significance for maintaining the stability of ecological environment. The paper tries to identify the dominant environmental variables that limit the distribution of J. sabina and to predict its suitable distribution area, in order to provide a theoretical basis for the administration and restoration of the species. Method: The geographic information of 267 existing populations and environmental variables(climate, elevation), were analyzed using three models of MaxEnt, BioClim and DoMain to simulate the potential suitable distribution areas for the species. The three models were compared and analyzed with the area under curve(AUC value) of receiver operating characteristic(ROC) and Kappa value. The potential geographical distribution patterns of the Last Glacial Maximum(LGM), the Mid-Holocene(MID), Current and Future(2070) based on the MaxEnt modelling are compared and the environmental variables that restrict the geographical distribution of J. sabina are discussed. Result: 1) The percent contribution of the environmental variables, permutation importance and jackknife method showed that the geographical distribution of J. sabina was mainly affected by annual mean temperature, elevation and temperature seasonality. 2) The present suitable habitat area of J. sabina in Eurasia based on the MaxEnt model with climate variable simulation is 663.115×10³ km², concentrated in the range of 30°—50°N, with mountainous sites as the main suitable areas. 3) The suitable habitat areas of J. sabina in different geological historical periods indicated that Asia is the main distribution area, the suitable areas of Asia in the LGM, MID and Current period accounted for 86.9%, 87.0%, and 57.8%, respectively. In the Future periods, the suitable areas of Asia occupied with 84.1% under 2070(RCP2.6) and 79.2% under 2070(RCP8.5). From the Last Glacial Maximum, Current to the Future, the suitable habitat area increased first and followed by a decrease, and the distribution center tended to migration from north to south and then to further north. Conclusion: The geographical distribution of J. sabina was not only affected by climate and environmental variables(including temperature and precipitation), but also related to elevation. The distribution area is in accordance with the characteristics of Cupressaceae distribution zone. This study provides an important basis for the management and restoration, rehabilitation of the germplasm resources of J. sabina.

Key words: Juniperus sabina, Maximum Entropy model, climate change, potential geographical distribution area

CLC Number:

S717

Aijun Wang,Dongye Lu,Guosheng Zhang,Haiguang Huang,Ying Wang,Sileng Hu,Min Ao. Potential Distribution of Juniperus sabina under Climate Change in Eurasia Continent Based on MaxEnt Model[J]. Scientia Silvae Sinicae, 2021, 57(8): 43-55.

Figures/Tables 9

Table 1

Table 2

Fig.1

Table 3

Fig.2

Table 4

Table 5

Fig.3

Table 6

References 0

	陈新美, 雷渊才, 张雄清, 等. 样本量对MaxEnt模型预测物种分布精度和稳定性的影响. 林业科学, 2012, 48 (1): 53- 59.
	Chen X M , Lei Y C , Zhang X Q , et al. Effects of sample sizes on accuracy and stability of Maximum Entropy model in predicting species distribution. Scientia Silvae Sinicae, 2012, 48 (1): 53- 59.
	方精云, 朱江玲, 石岳. 生态系统对全球变暖的响应. 科学通报, 2018, 63 (2): 136- 140.
	Fang J Y , Zhu J L , Shi Y . The responses of ecosystems to global warming. Chinese Science Bulletin, 2018, 63 (2): 136- 140.
	管毕财, 陈微, 刘想, 等. 四照花物种分布格局及其冰期避难所推测. 西北植物学报, 2016, 36 (12): 2541- 2547.
	Guan B C , Chen W , Liu X , et al. Distribution pattern and glacial refugia of Cornus kousa subsp. chinensis based on MaxEnt model and GIS. Acta Botanica Boreali-Occidentalia Sinica, 2016, 36 (12): 2541- 2547.
	郭飞龙, 徐刚标, 卢孟柱, 等. 基于MaxEnt模型分析胡杨潜在适宜分布区. 林业科学, 2020, 56 (5): 184- 192.
	Guo F L , Xu G B , Lu M Z , et al. Prediction of potential suitable distribution areas for Populus euphratica using the MaxEnt model. Scientia Silvae Sinicae, 2020, 56 (5): 184- 192.
	胡菀, 张志勇, 陈陆丹, 等. 末次盛冰期以来观光木的潜在地理分布变迁. 植物生态学报, 2020, 44 (3): 1- 12.
	Hu W , Zhang Z Y , Chen L D , et al. Changes in potential geographical distribution of Tsoongiodendron odorum since the Last Glacial Maximum. Chinese Journal of Plant Ecology, 2020, 44 (3): 1- 12.
	李文庆, 徐洲锋, 史鸣明, 等. 不同情景下四子柳的亚洲潜在地理分布格局变化预测. 生态学报, 2019, 39 (9): 3224- 3234.
	Li W Q , Xu Z F , Shi M M , et al. Prediction of potential geographical distribution patterns of Salix tetrasperma Roxb. in Asia under different climate scenarios. Acta Ecologica Sinica, 2019, 39 (9): 3224- 3234.
	李璇, 李垚, 方炎明. 基于优化的MaxEnt模型预测白栎在中国的潜在分布区. 林业科学, 2018, 54 (8): 153- 164.
	Li X , Li Y , Fang Y M . Prediction of potential suitable distribution areas of Quercus fabri in China based on an optimized MaxEnt model. Scientia Silvae Sinicae, 2018, 54 (8): 153- 164.
	刘超, 霍宏亮, 田路明, 等. 基于MaxEnt模型不同气候变化情景下的豆梨潜在地理分布. 应用生态学报, 2018, 29 (11): 3696- 3704.
	Liu C , Huo H L , Tian L M , et al. Potential geographical distribution of Pyrus calleryana under different climate change scenarios based on the MaxEnt model. Chinese Journal of Applied Ecology, 2018, 29 (11): 3696- 3704.
	刘建全. "整合物种概念"和"分化路上的物种". 生物多样性, 2016, 24 (9): 1004- 1008.
	Liu J Q . "The integrative species concept" and "species on the speciation way". Biodiversity Science, 2016, 24 (9): 1004- 1008.
	路东晔, 张磊, 郝蕾, 等. 臭柏叶绿体基因组结构与系统进化分析. 西北植物学报, 2018, 38 (8): 1464- 1475.
	Lu D Y , Zhang L , Hao L , et al. Analysis of chloroplast genome structure and phylogenetic evolution of Juniperus sabina. Acta Botanica Boreali-Occidentalia Sinica, 2018, 38 (8): 1464- 1475.
	路东晔, 张国盛, 李娅翔, 等. 臭柏天然居群遗传多样性及演变历史分析. 植物科学学报, 2020, 38 (2): 151- 161.
	Lu D Y , Zhang G S , Li Y X , et al. Genetic diversity and evolutionary history analysis of natural populations of Juniperus sabina L. Plant Science Journal, 2020, 38 (2): 151- 161.
	马松梅, 张明理, 张宏祥, 等. 利用最大熵模型和规则集遗传算法模型预测孑遗植物裸果木的潜在地理分布及格局. 植物生态学报, 2010, 34 (11): 1327- 1335. doi: 10.3773/j.issn.1005-264x.2010.11.010
	Ma S M , Zhang M L , Zhang H X , et al. Predicting potential geographical distributions and patterns of the relic plant Gymnocarpos przewalskii using Maximum Entropy and Genetic Algorithm for Rule-set prediction. Chinese Journal of Plant Ecology, 2010, 34 (11): 1327- 1335. doi: 10.3773/j.issn.1005-264x.2010.11.010
	毛康珊. 2010. 广义柏科的生物地理学研究-从板块漂移理论到冰期避难所. 甘肃: 兰州大学博士学位论文, 1-124.
	Mao K S. 2010. Biogeography of Cupressaceae sensu lato: from Plate Tectonics to Glacial Refugia. Gansu: PhD thesis of Lanzhou University, 1-124. [in Chinese]
	孟和, 姜真杰, 张国盛. 内蒙古臭柏不同分布区生长与生态因子的关联分析. 浙江林学院学报, 2010, 27 (1): 51- 56.
	Meng H , Jiang Z J , Zhang G S . Using the Grey System Theory for analysis of relationship between Sabina vulgaris growth and ecological factors. Journal of Zhejiang Forestry College, 2010, 27 (1): 51- 56.
	邱浩杰, 孙杰杰, 徐达, 等. 末次盛冰期以来红豆树在不同气候变化情景下的分布动态. 生态学报, 2020, 40 (9): 1- 11. doi: 10.3969/j.issn.1673-1182.2020.09.001
	Qiu H J , Sun J J , Xu D , et al. The distribution dynamics of Ormosia hosiei under different climate change scenarios since the Last Glacial Maximum. Acta Ecologica Sinica, 2020, 40 (9): 1- 11. doi: 10.3969/j.issn.1673-1182.2020.09.001
	山中典和, 王林和, 吉川賢. 中国内蒙古毛烏素沙地における臭柏(Sabina vulgaris Ant.)更新場所の微環境. 日本緑化工学会誌, 2000, 25 (4): 437- 442.
	Yamanaka N , Wang L H , Yoshikawa K . Microenvironment of safe site for Sabina vulgaris Ant. seedlings in the Mu Us Desert, Inner Mongolia, China. Journal of the Japanese Society of Revegetation Technology, 2000, 25 (4): 437- 442.
	王林和, 党宏忠, 张国盛, 等. 中国天然臭柏群落的分布与生物量特征. 内蒙古农业大学学报, 2014, 35 (1): 37- 45.
	Wang L H , Dang H Z , Zhang G S , et al. Distribution and biomass of natural Juniperus sabina community in China. Journal of Inner Mongolia Agricultural University, 2014, 35 (1): 37- 45.
	王茹琳, 李庆, 封传红, 等. 基于MaxEnt的西藏飞蝗在中国的适生区预测. 生态学报, 2017, 37 (24): 8556- 8566.
	Wang R L , Li Q , Feng C H , et al. Predicting potential ecological distribution of Locusta migratoria tibetensis in China using MaxEnt ecological niche modeling. Acta Ecologica Sinica, 2017, 37 (24): 8556- 8566.
	王绍武. 全新世气候变化. 北京: 气象出版社, 2011, 9
	Wang S W . Holocene climate change. Beijing: Meteorological Press, 2011, 9
	王哲, 张国盛, 王林和, 等. 毛乌素沙地天然臭柏群落种子产量、种子库及幼苗更新. 干旱区资源与环境, 2005, 19 (3): 195- 200. doi: 10.3969/j.issn.1003-7578.2005.03.039
	Wang Z , Zhang G S , Wang L H , et al. Seed yield, seed bank and regeneration of natural Sabina vulgaris community in Mu Us sandland. Journal of Arid Land Resources and Environment, 2005, 19 (3): 195- 200. doi: 10.3969/j.issn.1003-7578.2005.03.039
	杨芙蓉, 张琴, 孙成忠, 等. 蒙古黄芪潜在分布区预测的多模型比较. 植物科学学报, 2019, 37 (2): 136- 143.
	Yang F R , Zhang Q , Sun C Z , et al. Comparative evaluation of multiple models for predicting the potential distribution areas of Astragalus membranaceus var. mongholicus. Plant Science Journal, 2019, 37 (2): 136- 143.
	张国盛, 王哲, 王林和, 等. 毛乌素沙地天然臭柏居群有性更新幼苗动态研究. 林业科学, 2006, 42 (5): 62- 67.
	Zhang G S , Wang Z , Wang L H , et al. Regenerative seedlings dynamics of natural Sabina vulgaris community in Mu Us sandland. Scientia Silvae Sinicae, 2006, 42 (5): 62- 67.
	赵晓冏, 巩娟霄, 赵莎莎, 等. 样本量及其空间分布对物种分布模型的影响. 兰州大学学报: 自然科学版, 2018, 54 (2): 70- 77.
	Zhao X J , Gong J X , Zhao S S , et al. Impact of sample size and spatial distribution on species distribution model. Journal of Lanzhou University: Natural Sciences, 2018, 54 (2): 70- 77.
	郑益群, 于革, 薛滨, 等. 6 ka B. P.东亚区域气候模拟及其变化机制探讨. 第四纪研究, 2004, 24 (1): 28- 38. doi: 10.3321/j.issn:1001-7410.2004.01.004
	Zheng Y Q , Yu G , Xue B , et al. Simulations of east Asian climate at 6 ka B. P. Quaternary Sciences, 2004, 24 (1): 28- 38. doi: 10.3321/j.issn:1001-7410.2004.01.004
	中国第四纪孢粉数据库小组. 中国中全新世(6 ka BP)和末次盛冰期(18 ka BP)生物群区的重建. 植物学报, 2000, 42 (11): 1201- 1209. doi: 10.3321/j.issn:1672-9072.2000.11.018
	Members of China Quaternary Pollen Data Base . Pollen-based biome reconstruction at Middle Holocene(6 ka BP) and Last Glacial Maximum(18 ka BP) in China. Acta Botanica Sinica, 2000, 42 (11): 1201- 1209. doi: 10.3321/j.issn:1672-9072.2000.11.018
	朱井丽, 高忠斯, 邹红菲, 等. 基于MAXENT模型的松嫩平原丹顶鹤秋迁期生境适宜性评价. 野生动物学报, 2018, 39 (4): 852- 857. doi: 10.3969/j.issn.1000-0127.2018.04.018
	Zhu J L , Gao Z S , Zou H F , et al. Habitat suitability assessment of the Songnen plain for Grus japonensis based on MAXENT modeling. Chinese Journal of Wildlife, 2018, 39 (4): 852- 857. doi: 10.3969/j.issn.1000-0127.2018.04.018
	朱耿平, 刘国卿, 卜文俊, 等. 生态位模型的基本原理及其在生物多样性保护中的应用. 生物多样性, 2013, 21 (1): 90- 98.
	Zhu G P , Liu G Q , Bu W J , et al. The basic principle of niche model and its application in biodiversity conservation. Biodiversity Science, 2013, 21 (1): 90- 98.
	庄鸿飞, 张殷波, 王伟, 等. 基于最大熵模型的不同尺度物种分布概率优化热点分析: 以红色木莲为例. 生物多样性, 2018, 26 (9): 931- 940.
	Zhuang H F , Zhang Y B , Wang W , et al. Optimized hot spot analysis for probability of species distribution under different spatial scales based on MaxEnt model: Manglietia insignis case. Biodiversity Science, 2018, 26 (9): 931- 940.
	Adams R P , Boratynski A , Marcysiak K , et al. Discovery of Juniperus sabina var. balkanensis R. P. Adams and A. N. Tashev in Macedonia, Bosnia-Herzegovina, Croatia and Central and Southern Italy and relictual polymorphisms found in nrDNA. Phytologia, 2018, 100 (2): 117- 127.
	Adams R P , Boratynski A , Mataraci T , et al. Discovery of Juniperus sabina var. balkanensis R. P. Adams and A. N. Tashev in western Turkey. Phytologia, 2017, 99 (1): 22- 31.
	Ahmed S E , Mcinerny G , O'Hara K , et al. Scientists and software-surveying the species distribution modeling community. Diversity and Distributions, 2015, 21 (3): 258- 267. doi: 10.1111/ddi.12305
	Alexander L, Allen S, Bindoff N L. 2013. Climate change 2013: the physical science basis-summary for policymakers. Intergovernmental Panel on Climate Change.
	Barbosa F G , Schneck F . Characteristics of the top-cited papers in species distribution predictive models. Ecological Modelling, 2015, 313, 77- 83. doi: 10.1016/j.ecolmodel.2015.06.014
	Bellard C , Bertelsmeier C , Leadley P , et al. Impacts of climate change on the future of biodiversity. Ecology Letters, 2012, 15 (4): 365- 377. doi: 10.1111/j.1461-0248.2011.01736.x
	Bertrand R , Perez V , Gegout J C . Disregarding the edaphic dimension in species distribution models leads to the omission of crucial spatial information under climate change: the case of Quercus pubescens in France. Global Change Biology, 2012, 18 (8): 2648- 2660. doi: 10.1111/j.1365-2486.2012.02679.x
	Chen I C , Hill J K , Ohlemuller R , et al. Rapid range shifts of species associated with high levels of climate warming. Science, 2011, 333 (6045): 1024- 1026. doi: 10.1126/science.1206432
	Dieleman C M , Branfireun B A , McLaughlin J W , et al. Climate change drives a shift in peat land ecosystem plant community: implications for ecosystem function and stability. Global Change Biology, 2015, 21 (1): 388- 395. doi: 10.1111/gcb.12643
	Dülgeroğlu C , Aksoy A . Assessing impacts of climate change on Campanula yaltirikii H. Duman(Campanulaceae), a critically endangered endemic species in Turkey. Turkish Journal of Botany, 2019, 43 (2): 243- 252. doi: 10.3906/bot-1809-14
	Elith J , Graham C H , Anderson R P , et al. Novel methods improve prediction of species' distributions from occurrence data. Ecography, 2006, 29 (2): 129- 151. doi: 10.1111/j.2006.0906-7590.04596.x
	Elith J , Phillips S J , Hastie T , et al. A statistical explanation of MaxEnt for ecologists. Diversity & Distributions, 2011, 17 (1): 43- 57.
	García-Cervigón A I , Linares J C , García-Hidalgo M , et al. Growth delay by winter precipitation could hinder Juniperus sabina persistence under increasing summer drought. Dendrochronologia, 2018, 51 doi: 10.1016/j.dendro.2018.07.003
	Gibson L , Lee T M , Koh L P , et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature, 2011, 478 (7369): 378- 381. doi: 10.1038/nature10425
	Graham C H , Elith J , Hijmans R J , et al. The influence of spatial errors in species occurrence data used in distribution models. Journal of Applied Ecology, 2008, 45 (1): 239- 247.
	Hewitt G M . Genetic consequences of climatic oscillations in the Quaternary. Philosophical Transactions of the Royal Society B: Biological Sciences, 2004, 359 (1442): 183- 195. doi: 10.1098/rstb.2003.1388
	Hughes G , Madden L V . Evaluating predictive models with application in regulatory policy for invasive weeds. Agricultural Systems, 2003, 76 (2): 755- 774. doi: 10.1016/S0308-521X(02)00164-6
	Kumar S , Stohlgren T J . Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. Journal of Ecology and the Natural Environment, 2009, 1 (4): 94- 98.
	Manel S , Williams H C , Ormerod S J . Evaluating presence-absence models in ecology: the need to account for prevalence. Journal of Applied Ecology, 2010, 38 (5): 921- 931.
	Otto-Bliesner B L , Marshall S J , Overpeck J T , et al. Simulating Arctic climate warmth and icefield retreat in the last interglaciation. Science, 2006, 311 (5768): 1751- 1753. doi: 10.1126/science.1120808
	Phillips S J . A brief tutorial on Maxent. Lessons in Conservation, 2010, 3, 108- 135.
	Qiao H J , Lin C T , Ji L Q , et al. mMWeb-an online platform for employing multiple ecological niche modeling algorithms. PLoS ONE, 2012, 7 (8)
	Segurado P , Araujo M B . An evaluation of methods for modelling species' distributions. Journal of Biogeography, 2004, 31 (10): 1555- 1568. doi: 10.1111/j.1365-2699.2004.01076.x
	Sekercioglu C H , Schneider S H , Fay J P , et al. Climate change, elevational range shifts, and bird extinctions. Conservation Biology, 2008, 22 (1): 140- 150. doi: 10.1111/j.1523-1739.2007.00852.x
	Soberón J , Peterson A T . Interpretation of models of fundamental ecological niches and species' distributional areas. Biodiversity Informatics, 2005, 2, 1- 10.
	Soberón J , Nakamura M . Niches and distributional areas: concepts, methods and assumptions. Proceedings of the National Academy of Sciences, 2009, 106 (17): 19644- 19650.
	Stocker T F , Qin D , Plattner G K , et al. Climate Change 2013:The Physical Science Basis. Cambridge, UK: Cambridge University Press, 2014.
	Swets J A . Measuring the accuracy of diagnostic systems. Science, 1988, 240 (4857): 1285- 1293. doi: 10.1126/science.3287615
	Van Proosdij A S , Sosef M S M , Wieringa J J , et al. Minimum required number of specimen records to develop accurate specices distribution models. Ecography, 2016, 39 (6): 542- 552. doi: 10.1111/ecog.01509
	Van Vuuren D P , Edmonds J , Kainuma M , et al. The representative concentration pathways: an overview. Climatic Change, 2011, 109 (1/2): 5- 31.
	Vaz U L , Cunha H F , Nabout J C . Trends and biases in global scientific literature about ecological niche models. Brazilian Journal of Biology, 2015, 75
	Wang J R , Hawkins C D B , Letchford T . Photosynthesis, water and nitrogen use efficiencies of four paper birch(Betula papyrifera) populations grown under different soil moisture and nutrient regimes. Forest Ecology and Management, 1998, 112 (3): 233- 244. doi: 10.1016/S0378-1127(98)00407-1
	Wang Y H , Jiang W M , Comes H P , et al. Molecular phylogeography and ecological niche modeling of a widespread herbaceous climber, Tetrastigma hemsleyanum(Vitaceae): insights into Plio-Pleistocene range dynamics of evergreen forest in subtropical China. New Phytologist, 2015, 206 (2): 852- 867. doi: 10.1111/nph.13261
	Wisz M S , Hijmans R J , Li J , et al. Effects of sample size on the performance of species distribution models. Diversity and Distributions, 2008, 14 (5): 763- 773. doi: 10.1111/j.1472-4642.2008.00482.x

	环境变量Environmental variable
Bio1	年均气温Annual mean temperature/℃
Bio2	昼夜温差月均值Monthly mean diurnal range/℃
Bio4	温度季节性变化Temperature seasonality/℃
Bio7	年温度变化范围Temperature annual range/℃
Bio8	最湿季平均温度Mean temperature of wettest quarter/℃
Bio9	最干季平均温度Mean temperature of driest quarter/℃
Bio12	年降水量Annual precipitation/mm
Bio14	最干月降水量Precipitation of driest month/mm
Bio15	降水量季节性变化Precipitation seasonality(%)
Bio17	最干季降水量Precipitation of driest quarter/mm
Bio18	最热季降水量Precipitation of warmest quarter/mm
Bio19	最冷季降水量Precipitation of coldest quarter/mm
Elev	海拔Elevation/m

模型Models	AUC	Kappa
MaxEnt	0.926±0.009	0.811±0.021
BioClim	0.777±0.026	0.622±0.079
DoMain	0.977±0.065	0.852±0.124

环境变量 Environmental variable	贡献率 Percent contribution(%)	置换重要值 Permutation importance(%)	环境变量 Environmental variable	贡献率 Percent contribution(%)	置换重要值 Permutation importance(%)
Bio1	28.80±0.86	18.70±2.12	Bio15	2.90±0.98	7.70±1.50
Elev	29.00±0.36	45.70±5.67	Bio9	2.50±0.43	6.20±1.08
Bio4	13.00±0.98	8.00±7.08	Bio12	2.10±0.59	5.20±1.51
Bio14	7.10±2.14	0.30±1.25	Bio7	0.70±2.42	2.30±0.65
Bio18	5.70±0.49	2.10±0.24	Bio2	0.70±0.28	0.80±0.40
Bio17	3.60±1.63	0.10±0.17	Bio19	0.60±0.19	0.30±0.05
Bio8	3.50±0.12	2.70±0.62

适生区Suitable growing area	LGM	MID	当前Current	未来Future RCP2.6	未来Future RCP8.5
亚洲Asia	63.37×10³	62.85×10³	383.03×10³	56.47×10³	43.87×10³
欧洲Europe	9.52×10³	9.40×10³	280.09×10³	10.65×10³	11.49×10³
总面积Total area	72.89×10³	72.25×10³	663.12×10³	67.12×10³	55.36×10³

气候时期 Climatic periods	面积Area/(×10³ km²)
气候时期 Climatic periods	增加 Increase	不变 Unchanged	减少 Decrease
LGM—MID	27.32	39.99	31.08
MID—当前Current	559.71	49.92	17.39
当前Current—未来Future RCP2.6	22.80	39.91	569.72
当前Current—未来Future RCP8.5	22.75	26.81	582.81

Potential Distribution of Juniperus sabina under Climate Change in Eurasia Continent Based on MaxEnt Model

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 0

Related Articles 15

Recommended Articles

Metrics

Comments

时期 Period	年均气温(Bio1) Annual average temperature/℃		海拔(Elev) Elevation/m		温度季节性变化(Bio4) Seasonal variation of temperature/℃		年降水量(Bio12) Annual precipitation/mm
时期 Period	范围Range	均值Average	范围Range	均值Average	范围Range	均值Average	范围Range	均值Average
LGM	-14.5~2.2	-1.42±4.78	110~3 100	1 254±824	4.1~15.9	9.58±3.50	90~2 000	552.95±389.24
MID	-9.0~7.6	4.69±4.45	800~3 000	1 254±824	6.2~16.4	10.07±3.03	150~1 700	611.56±343.50
当前Current	-8.0~13.0	5.31±4.39	100~2 900	1 254±824	5.5~15.5	9.16±2.76	120~1 700	597.85±328.66
未来Future RCP2.6	-6.5~7.4	6.89±4.22	140~2 900	1 254±824	5.7~15.6	9.18±2.71	150~1 800	601.11±325.82
未来Future RCP8.5	-3.8~12.3	9.12±4.01	900~2 900	1 254±824	6.0~16.4	9.37±2.51	150~1 400	585.33±309.56

[1]	Rui Bai,Ning Li,Shaojun Liu,Xiaomin Chen,Haiping Zou,Run Lü. Risk Analysis of White Root Disease on Rubber Trees in China under the Background of Future Climate Change [J]. Scientia Silvae Sinicae, 2021, 57(6): 37-45.
[2]	Guanghua Zhao,Xinyue Cui,Zhi Wang,Hongli Jing,Baoguo Fan. Prediction of Potential Distribution of Ziziphus jujuba var. spinosa in China under Context of Climate Change [J]. Scientia Silvae Sinicae, 2021, 57(6): 158-168.
[3]	Danni Zhang,Xiya Chen,Chuanfu Zang. Potential for Afforestation in the Three-North Region Where Implements Shelter-Belt Forest Program [J]. Scientia Silvae Sinicae, 2021, 57(5): 184-194.
[4]	Jian Yu,Jiajia Chen,Guang Zhou,Guohua Liu,Yongping Wang,Junqing Li,Qijing Liu. Response of Radial Growth of Abies forrestii and Picea likiangensis to Climate Factors in the Central Hengduan Mountains, Southwest China [J]. Scientia Silvae Sinicae, 2020, 56(12): 28-38.
[5]	Tiantian Pan,Yan Li,Zhongyuan Wang,Shitong Lu,Linfeng Ye,Sen Chen,Jiangbo Xie. Relationship between the Hydraulic Function and the Anatomical Structure of Branch and Root Xylem in Three Taxodiaceae Species in Humid Area [J]. Scientia Silvae Sinicae, 2020, 56(12): 49-59.
[6]	Li Yacang, Feng Zhongke. Developing a System Climate Sensitive Biomass Compatible Equations for Masson Pine [J]. Scientia Silvae Sinicae, 2019, 55(5): 65-73.
[7]	Wang Xiaowei, Ren Xueyan, Liang Yingmei. MaxEnt-Based Prediction of Potential Geographic Distribution and Habitat Suitability Analysis for Dothistroma pini in China [J]. Scientia Silvae Sinicae, 2019, 55(4): 160-170.
[8]	Lü Zhengang, Li Wenbo, Huang Xuanrui, Zhang Zhidong. Predicting Suitable Distribution Area of Three Dominant Tree Species under Climate Change Scenarios in Hebei Province [J]. Scientia Silvae Sinicae, 2019, 55(3): 13-21.
[9]	Zhengang Lü,Wenbo Li,Xuanrui Huang,Zhidong Zhang. Larix principis-rupprechtii Growth Suitability Based on Potential NPP under Climate Change Scenarios in Hebei Province [J]. Scientia Silvae Sinicae, 2019, 55(11): 37-44.
[10]	Liu Dan, Li Yutang, Hong Lingxia, Guo Hong, Xie Yangsheng, Zhang Zhuoli, Lei Xiangdong, Tang Shouzheng. The Suitability of Potential Geographic Distribution of Natural Forest Types in Jilin Province Based on Maximum Entropy Models [J]. Scientia Silvae Sinicae, 2018, 54(7): 1-15.
[11]	Wang Jingru, Wang Minghao, Zhang Xiaowei, Sun Shan, Zhao Changming. The Ecological Divergence and Projection of Future Potential Distribution of Homoploid Hybrid Species Picea purpurea [J]. Scientia Silvae Sinicae, 2018, 54(6): 63-72.
[12]	Lu Weiwei, Yu Xinxiao, Jia Guodong, Li Hanzhi, Liu Ziqiang. Response of Stable Carbon Isotope of Tree-Ring to Temperature and Precipitation Changes in Pinus tabulaeformis in Miyun Mountain Area [J]. Scientia Silvae Sinicae, 2018, 54(3): 1-7.
[13]	Yuan Ran, Li Xinyue, Wu Shaoping, Cao Chuanwang. Effects of Elevated CO₂ Concentration on Growth and Development of Lymantria dispar (Lepidoptera: Lymantridae), as well as on Activities of Detoxifying Enzymes and Protective Enzymes in the Body [J]. Scientia Silvae Sinicae, 2018, 54(12): 102-109.
[14]	Qian Yang, Sun Honggang, Dong Ruxiang, Jiang Jingming. Research Progress of Carbohydrates Allocation in Conifers [J]. Scientia Silvae Sinicae, 2018, 54(1): 141-153.
[15]	Tian Xiaorui, Shu Lifu, Zhao Fengjun, Wang Mingyu. Impacts of Climate Change on Forest Fire Danger in China [J]. Scientia Silvae Sinicae, 2017, 53(7): 159-169.