Haloxylon ammodendron's Potential Distribution under Climate Change in Arid Areas of Northwest China

doi:10.7522/j.issn.1000-694X.2018.00048

Journal of Desert Research ›› 2019, Vol. 39 ›› Issue (1): 110-118.DOI: 10.7522/j.issn.1000-694X.2018.00048

Previous Articles Next Articles

Haloxylon ammodendron's Potential Distribution under Climate Change in Arid Areas of Northwest China

Chang Hong¹, Liu Tong¹, Wang Dawei², Ji Xiaoru²

1. College of Life Science, Shihezi University, Shihezi 832000, Xinjiang, China;
2. College of Resource and Environment, Linyi University, Linyi 276000, Shandong, China

Received:2018-02-12 Revised:2018-04-19 Published:2019-02-14

Abstract

Abstract: Climate change will lead to changes of species distribution and even species extinction. As a kind of third-class protection plant in China, Haloxylon ammodendron grows only in desert. It plays a key role in the prevention of wind and sand in desert areas and in maintaining the balance of desert ecosystems. In order to explore the impact of climate change on the potential distribution of Haloxylon ammodendron, we evaluated the performance of GARP (Genetic Algorithm for Rule-set Production), ENFA (Ecological Niche Factor analysis) and Maxent (maximum entropy model), and then we selected the optimal model and 10 major environmental factors. We used the optimal model and 17 general circulation models (GCMs) to simulate the future potential distribution of the Haloxylon ammodendron in two periods (2041-2060 and 2061-2080), under two climate change scenarios (RCP4.5 and RCP8.5) in the Arid Area of Northwest China. The simulation results of 17 CCMs were assembled and averaged, and then were divided into 3 grades (low-suitable area, middle-suitable area and high-suitable area). The results show that MAXENT is the best model (AUC=0.973). The total contribution of factors related to precipitation is 60.5%, while the total contribution of factors related to temperature is 14.8%. Precipitation of wettest quarter (39%), soil type (22.7%) and standard deviation of temperature seasonality (9.1%) are most important factors. As time goes on, the low-suitable areas will reduce (9.36%-20.44%). In most cases, the middle-suitable areas will increase (3.50%-60.05%). While it will reduce by 4.29% in 2061-2080 period under RCP8.5 scenarios. The high-suitable areas will increase by 11.01%-33.80%, and the total suitable areas will increase by 7.47%-9.07% in study area. The increase of suitable area mainly due to the increase of the high-suitable area. The high-suitable area of the Tarim basin increase largest (127%-669%), while there is a slight reduction in the Qaidam basin (4%-9%).

Key words: ecological niche models, Haloxylon ammodendron, climate change

CLC Number:

Q145

Chang Hong, Liu Tong, Wang Dawei, Ji Xiaoru. Haloxylon ammodendron's Potential Distribution under Climate Change in Arid Areas of Northwest China[J]. Journal of Desert Research, 2019, 39(1): 110-118.

References

[1] 马海波,包根晓,马微东,等.内蒙古梭梭荒漠草地资源及其保护利用[J].草业科学,2000(4):1-5.
[2] 张立运.新疆荒漠中的梭梭和白梭梭(下)[J].植物杂志,2002(5):4-5.
[3] 郭泉水,王春玲,郭志华,等.我国现存梭梭荒漠植被地理分布及其斑块特征[J].林业科学,2005(5):2-7.
[4] 曾晓玲,刘彤,张卫宾,等.古尔班通古特沙漠西部地下水位和水质变化对植被的影响[J].生态学报,2012,32(5):1490-1501.
[5] 张荣,刘彤.古尔班通古特沙漠南部植物多样性及群落分类[J].生态学报,2012,32(19):6056-6066.
[6] Root T L,Price J T,Hall K R,et al.Fingerprints of global warming on wild animals and plants[J].Nature,2003,421(6918):57-60.
[7] Klamt M,Thompson R,Davis J.Early response of the platypus to climate warming.Global Change[J].Biology,2011,17:3011-3018.
[8] Yu D,Chen M,Zhou Z C,et al.Global climate change will severely decrease potential distribution of the East Asian coldwater fish Rhynchocypris oxycephalus (Actinopterygii,Cyprinidae)[J].Hydrobiologia,2012,700(1):23-32.
[9] Taylor K E,Stouffer R J,Meehl G A.An overview of CMIP5 and the experiment design[J].Bulletin of the American Meteorological Society,2012,93(4):485-498.
[10] 张艳武,张莉,徐影.CMIP5模式对中国地区气温模拟能力评估与预估[J].气候变化研究进展,2016,12(1):10-19.
[11] 陈晓晨,徐影,许崇海,等.CMIP5全球气候模式对中国地区降水模拟能力的评估[J].气候变化研究进展,2014,10(3):217-225.
[12] 张冰.CMIP5全球气候模式对中国极端温度事件模拟能力检验与未来预估[D].成都:成都信息工程大学,2015.
[13] Goberville E,Beaugrand G,Hautekèete N C,et al.Uncertainties in the projection of species distributions related to general circulation models[J].Ecology and Evolution,2015,5(5):1100-1116.
[14] Araújo M B,Whittaker R J,Ladle R J,et al.Reducing uncertainty in projections of extinction risk from climate change[J].Global Ecology and Biogeography,2005,14(6):529-538.
[15] Barry S,Elith J.Error and uncertainty in habitat models[J].Journal of Applied Ecology,2006,43(3):413-423.
[16] 刘芳,李晟,李迪强.利用分布有/无数据预测物种空间分布的研究方法综述[J].生态学报,2013,33(22):7047-7057.
[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.
[18] Elith J,Phillips S J,Hastie T,et al.A statistical explanation of MaxEnt for ecologists[J].Diversity and Distributions,2011,17(1):43-57.
[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):4827-4835.
[20] Elith J,Graham C H,Anderson R P,et al.Novel methods improve prediction of species' distributions from occurrence data[J].Ecography,2006,29(2):129-151.
[21] 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.
[22] Hirzel A H,Hausser J,Chessel D,et al.Ecological-niche factor analysis:how to compute habitat-suitable maps without absence data[J].Ecology,2002,83(7):2036-2070.
[23] Zhang L,Liu S R,Sun P S,et al.Comparative evaluation of multiple models of the effects of climate change on the potential distribution of Pinus massoniana[J].Chinese Journal of Plant Ecology,2011,35(11):1091-1105.
[24] Hartley S,Harris R,Lester P J.Quantifying uncertainty in the potential distribution of an invasive species:climate and the Argentine ant[J].Ecological Letters,2006,9(9):1068-1079.
[25] Buisson L,Thuiller W,Casajus N,et al.Uncertainty in ensemble forecasting of species distribution[J].Global Change Biology,2010,16:1145-1157.
[26] Synes N W,Osborne P E.Choice of predictor variables as a source of uncertainty in continental-scale species distribution modelling under climate change[J].Global Ecology and Biogeography,2011,20(6):904-914.
[27] 邓兴耀,刘洋,刘志辉,等.中国西北干旱区蒸散发时空动态特征[J].生态学报,2017,37(9):2994-3008.
[28] 赵哈林,赵学勇,张铜会,等.我国西北干旱区的荒漠化过程及其空间分异规律[J].中国沙漠,2011,31(1):1-8.
[29] 张鸿铎.准噶尔盆地梭梭林型及其特点[J].中国沙漠,1990,10(1):44-52.
[30] 张景波,王葆芳,郝玉光,等.我国梭梭林地理分布和适应环境及种源变异[J].干旱区资源与环境,2010,24(5):166
[31] 王继和,马全林.民勤绿洲人工梭梭林退化现状、特征与恢复对策[J].西北植物学报,2003(12):2107-2112.
[32] 郑颖,赵文智,张格非.基于V-Hegyi竞争指数的绿洲边缘人工固沙植被梭梭(Haloxylon ammodendron)的种群竞争[J].中国沙漠,2017,37(6):1127-1134.
[33] 何芳兰,金红喜,郭春秀,等.民勤绿洲边缘人工梭梭(Haloxylon ammodendron)林衰败过程中植被组成动态及群落相似性[J].中国沙漠,2017,37(6):1135-1141.
[34] 高志娟,谢双全,吕新华,等.梭梭(Haloxylon ammodendron)居群间系统发育关系[J].中国沙漠,2017,37(3):462-468.
[35] 张超,陈磊,田呈明,等.基于GARP和MaxEnt的云杉矮槲寄生分布区的预测[J].北京林业大学学报,2016,38(5):23-32.
[36] 郭乃嘉.基于生态位模型的中间香型烤烟生态适宜区潜在分布预测[D].重庆:西南大学,2013.
[37] 杨瑞.苹果蠹蛾(Cydia pomonella L.)在中国的适生性研究[D].陕西杨凌:西北农林科技大学,2008.
[38] 潘红伟.松材线虫(Bursaphelenchus xylophilus)在我国的潜在分布区研究[D].北京:中国林业科学研究院,2009.
[39] 孙瑜,史明昌,彭欢,等.基于MAXENT模型的黑龙江大兴安岭森林雷击火火险预测[J].应用生态学报,2014,25(4):1100-1106.
[40] Phillips S J,Dudik M.Modeling of species distributions with Maxent:new extensions and a comprehensive evaluation[J].Ecography,2008,31(2):161-175.
[41] 王运生,谢丙炎,万方浩,等.ROC曲线分析在评价入侵物种分布模型中的应用[J].生物多样性,2007(4):365-372.
[42] 应凌霄,刘晔,陈绍田,等.气候变化情景下基于最大熵模型的中国西南地区清香木潜在分布格局模拟[J].生物多样性,2016,24(4):453-461.
[43] 曹向锋.外来入侵植物黄顶菊在中国潜在适生区预测及其风险评估[D].南京:南京农业大学,2010.
[44] 邹婷,李彦,许皓,等.不同生境梭梭对降水变化的生理响应及形态调节[J].中国沙漠,2011,31(2):428-435.
[45] 田媛,李建贵,赵岩.梭梭幼苗死亡与土壤和大气干旱的关系研究[J].中国沙漠,2010,30(4):878-884.
[46] 黄培祐,李启剑,袁勤芬.准噶尔盆地南缘梭梭群落对气候变化的响应[J].生态学报,2008,28(12):6051-6059.
[47] 任朝霞,杨达源.近50a西北干旱区气候变化趋势及对荒漠化的影响[J].干旱区资源与环境,2008,22(4):91-95.
[48] 徐利岗,周宏飞,杜历,等.1951-2008年中国西北干旱区降水时空变化及其趋势[J].中国沙漠,2015,35(3):724-732.
[49] 李林,申红艳,李红梅,等.柴达木盆地气候变化的区域显著性及其成因研究[J].自然资源学报,2015,30(4):641-650.
[50] 王发科,祁贵明,苟日多杰,等.柴达木盆地气候变化对生态环境的影响[J].青海环境,2008(2):70-75.
[51] 尚忠慧.基于MaxEnt的物种空间分布预测不确定性分析[D].西安:陕西师范大学,2016.
[52] 马松梅,魏博,李晓辰,等.气候变化对梭梭适宜分布的影响[J].生态学杂志,2017,36(5):1240-1250.
[53] Peterson A T,Nakazawa Y.Environmental data sets matter in ecological niche modelling:an example with Solenopsis invicta and Solenopsis richteri[J].Global Ecology and Biogeography,2008,17:135-144.
[54] Beaumont L J,Hughes L,Poulsen M.Predicting species distributions:use of climatic parameters in BIOCLIM and its impact on predictions of species' current and future distributions[J].Ecological Modelling,2005,186:250-269.
[55] Williams P,Hannah L,Andelman S,et al.Planning for climate change:identifying minimum-dispersal corridors for the Cape Proteaceae[J].Conservation Biology,2005,19(4):1063-1074.
[56] Broennimann O,Thuiller W,Hughes G,et al.Do geographic distribution,niche property and life form explain plants' vulnerability to global change?[J].Global Change Biology,2006,12(6):1079-1093.

Haloxylon ammodendron's Potential Distribution under Climate Change in Arid Areas of Northwest China

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Yuanzheng He, Wenda Huang, Xin Zhao, Peng Lv, Huaihai Wang. Review on the impact of climate change on plant diversity [J]. Journal of Desert Research, 2021, 41(1): 59-66.
[2]	Taotao Zhang, Jinglong Fan, Shiming Wang, Xinwen Xu, Zhaohui Chai. Photosynthetic characteristics of Haloxylon ammodendron under high salinity water irrigation [J]. Journal of Desert Research, 2020, 40(5): 112-119.
[3]	Di Deng, Zebin Zhao, Yuan Ma. Modeling of species distribution with GIS in arid regions： take Caragana korshinskii for example [J]. Journal of Desert Research, 2020, 40(5): 74-80.
[4]	Han Chao, Xiao Shengchun, Ding Aijun, Teng Zeyu. Radial growth climatic response characteristics and tree ring climate records of conifer species at south margin of Tengger Desert, China [J]. Journal of Desert Research, 2020, 40(2): 50-58.
[5]	Li Xiang, Su Zhizhu, Ma Yijuan, Zhang Caixia, Liu Miaomiao. Holocene climatic instability record in the southeastern margin of Mu Us Desert [J]. Journal of Desert Research, 2020, 40(2): 109-117.
[6]	Zhao Zefang, Wei Haiyan, Guo Yanlong, Luan Wenfei, Zhao Zebin. Impact of climate change on the suitable habitatdistribution of Gymnocarpos przewalskii, a relict plant [J]. Journal of Desert Research, 2020, 40(2): 125-133.
[7]	Chang Xi, Lu Huayu, Lü Nana, Cui Mengchun, Li Haiyu. Variation of desert and sandy field in China on the basis of remote sensing analysis and the relationship with climate change during 1992-2015 [J]. Journal of Desert Research, 2020, 40(1): 57-63.
[8]	Ma Qimin, Jia Xiaopeng, Wang Haibing, Li Yongshan, Li Shaoning. Recent Advances in Driving Mechanisms of Climate and Anthropogenic Factors on Vegetation Change [J]. Journal of Desert Research, 2019, 39(6): 48-55.
[9]	Liu Liyun, Lu Ruijie, Liu Xiaokang. Climate Change in the Mu Us Desert since Holocene Based on Soil Chromaticity [J]. Journal of Desert Research, 2019, 39(6): 83-89.
[10]	Lei Chen, Pang Jiangli, Huang Chunchang, Zha Xiaochun, Zhou Yali, Wen Ruiyan, Chui Yuda. Composition Characteristics and Significance of Rb, Sr and Ba of Fanjiacheng Loess-paleosol Profile in the Upper Reaches of the Weihe River [J]. Journal of Desert Research, 2019, 39(6): 90-98.
[11]	Ma Pengli, Han Lanying, Zhang Xudong, Liu Weiping. Regional Characteristics of Drought in China under the Background of Climate Warming [J]. Journal of Desert Research, 2019, 39(6): 209-215.
[12]	Han Lanying, Zhang Qiang, Jia Jianying, Wang Youheng, Huang Tao. Drought Severity, Frequency, Duration and Regional Differences in China [J]. Journal of Desert Research, 2019, 39(5): 1-10.
[13]	Han Rui, Su Zhizhu, Li Xiang, Liu Miaomiao, Ma Yijuan. Holocene Climate Change Revealed by Grain Size and Magnetic Susceptibility in the Eastern Mu Us Sandy Land [J]. Journal of Desert Research, 2019, 39(2): 105-114.
[14]	Bai Zhuangzhuang, Cui Jianxin. Desertification and Its Causes in Mu Us Desert in Recent 2 000 Years [J]. Journal of Desert Research, 2019, 39(2): 177-185.
[15]	Zhang Fuping, Li Xiaojuan, Feng Qi, Wang Huwei, Wei Yongfen, Bai Hao. Spatial and Temporal Variation of Water Conservation in the Upper Reaches of Heihe River Basin Based on InVEST Model [J]. Journal of Desert Research, 2018, 38(6): 1321-1329.