Scientia Silvae Sinicae ›› 2020, Vol. 56 ›› Issue (5): 184-192.doi: 10.11707/j.1001-7488.20200521
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Feilong Guo1,Gangbiao Xu1,*,Mengzhu Lu1,2,Yihong Meng1,Chengzhi Yuan1,Kaiqi Guo1
Received:
2019-04-04
Online:
2020-05-01
Published:
2020-06-13
Contact:
Gangbiao Xu
CLC Number:
Feilong Guo,Gangbiao Xu,Mengzhu Lu,Yihong Meng,Chengzhi Yuan,Kaiqi Guo. Prediction of Potential Suitable Distribution Areas for Populus euphratica Using the MaxEnt Model[J]. Scientia Silvae Sinicae, 2020, 56(5): 184-192.
Table 1
Description of environmental variables used in MaxEnt"
类型Type | 变量Variable | 代码Code |
地形Topographical | 海拔Elevation | Ele |
气候Climate | 昼夜温差月均值Mean diurnal temperature range (Mean of monthly) | Bio2 |
等温性Isothermality | Bio3 | |
气温年较差Temperature annual range | Bio7 | |
最湿季节平均温度Mean temperature of the wettest quarter | Bio8 | |
最热季节平均温度Mean temperature of the warmest quarter | Bio10 | |
最湿月份降水量Precipitation of the wettest month | Bio13 | |
最干月份降水量Precipitation of the driest month | Bio14 | |
降水量变异系数Precipitation seasonality (coefficient of variation) | Bio15 | |
最热季节降水量Precipitation of the warmest quarter | Bio18 | |
最冷季节降水量Precipitation of the coldest quarter | Bio19 | |
土壤Soil | 养分可利用性Nutrient availability | SP1 |
生根条件Rooting conditions | SP3 | |
根部氧气供应Oxygen availability to roots | SP4 | |
过量盐分Excess salts | SP5 | |
毒性Toxicity | SP6 | |
水文Hydrological | 0~10 cm土壤含水量Soil moisture content (0-10 cm underground) | SMC1 |
10~40 cm土壤含水量Soil moisture content (10-40 cm underground) | SMC2 | |
40~100 cm土壤含水量Soil moisture content (40-100 cm underground) | SMC3 | |
根部土壤湿度Root zone soil moisture | RSM | |
地下径流Baseflow-groundwater runoff | BGR | |
土壤水分蒸发量Evapotranspiration | Eva |
Table 2
Percent contribution of each environmental variable"
代码 Code | 4类环境变量Four types of environmental variables | 代码 Code | 单一气候变量Single climate variable | |||
贡献率Percent contribution(%) | 置换重要值Permutation importance(%) | 贡献率Percent contribution(%) | 置换重要值Permutation importance(%) | |||
Bio14 | 22.15±3.16 | 4.69±2.97 | Bio14 | 43.42±3.31 | 7.35±5.74 | |
Bio18 | 17.53±3.17 | 17.28±7.01 | Bio18 | 22.03±5.44 | 53.89±9.74 | |
SMC2 | 14.61±4.60 | 9.88±6.09 | Bio3 | 11.44±1.78 | 5.11±3.11 | |
RSM | 7.45±2.03 | 4.13±1.55 | Bio19 | 6.94±5.19 | 7.92±1.71 | |
Bio13 | 5.80±1.50 | 9.60±3.96 | Bio13 | 5.47±1.10 | 12.29±6.53 | |
Bio10 | 4.87±2.04 | 1.89±0.69 | Bio7 | 4.13±1.08 | 1.08±0.52 | |
Bio2 | 3.60±1.70 | 2.31±0.93 | Bio8 | 3.34±1.00 | 0.49±0.55 | |
Bio7 | 3.45±1.44 | 3.81±0.98 | Bio15 | 2.06±0.72 | 9.90±1.57 | |
Eva | 3.38±1.46 | 17.32±4.99 | Bio2 | 1.15±0.49 | 2.01±2.15 | |
Bio8 | 3.30±0.97 | 1.83±0.63 | ||||
Bio19 | 2.44±1.66 | 4.11±2.07 | ||||
SP1 | 2.31±1.71 | 3.42±2.33 | ||||
SMC1 | 2.16±1.28 | 4.05±2.00 | ||||
SMC3 | 1.78±1.11 | 1.32±0.72 | ||||
Bio15 | 1.50±0.36 | 6.92±2.52 | ||||
BGR | 1.12±0.58 | 2.67±1.05 | ||||
SP3 | 0.75±0.50 | 1.99±1.47 | ||||
Ele | 0.64±0.21 | 0.60±0.25 | ||||
SP5 | 0.51±0.24 | 0.65±0.36 | ||||
SP6 | 0.37±0.19 | 0.72±0.57 | ||||
SP4 | 0.23±0.11 | 0.82±0.58 |
Table 3
The suitable distribution area of P. euphratica"
地区Area | 模拟面积The simulated area/km2 | |
4类环境变量Four types of environmental variables | 单一气候变量Single climate variable | |
亚洲Asia | 418.00×103 | 1 996.00×103 |
非洲Africa | 43.00×103 | 45.00×103 |
欧洲Europe | 1.00×103 | 0.03×103 |
南美洲South America | 9.00×103 | 0.20×103 |
全球Global | 471.00×103 | 2 041.23×103 |
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. | |
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. | |
刘洪霞, 管文轲, 扎依达·斯迪克, 等. 塔里木胡杨国家自然保护区湿地面积在生态输水工程前后的变化. 林业科学, 2018. 54 (9): 1- 8. | |
Liu H X , Guan W K , Zayida S , et al. Changes of wetland area before and after ecological water supplement project in the national nature reserve of Populus euphratica in Tarim. Scientia Silvae Sinicae, 2018. 54 (9): 1- 8. | |
唐书培, 穆丽光, 王晓玲, 等. 基于MaxEnt模型的赛罕乌拉国家级自然保护区斑羚生境适宜性评价. 北京林业大学学报, 2019. 41 (1): 102- 108. | |
Tang S P , Mu L G , Wang X L , et al. Habitat suitability assessment based on MaxEnt modeling of Chinese goral in Saihanwula National Nature Reserve, Inner Mongolia of northern China. Journal of Beijing Forestry University, 2019. 41 (1): 102- 108. | |
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. | |
王世绩. 全球胡杨林的现状及保护和恢复对策. 世界林业研究, 1996. (6): 38- 45. | |
Wang S J . The status, conservation and recovery of global resources of Populus euphratica. World Forestry Research, 1996. (6): 38- 45. | |
张宁, 李宝富, 徐彤彤, 等. 1960-2012年全球胡杨分布区干旱指数时空变化特征. 干旱区资源与环境, 2017. 31 (7): 121- 126. | |
Zhang N , Li B F , Xu T T , et al. Spatiotemporal variations of drought index in Populus euphratica global distribution area during the past 50 years (1960-2012). Journal of Arid Land Resources and Environment, 2017. 31 (7): 121- 126. | |
张晓芹. 西北旱区典型生态经济树种地理分布与气候适宜性研究. 杨凌:中国科学院大学(中国科学院教育部水土保持与生态环境研究中心)博士学位论文, 2018. | |
Zhang X Q . Geographical distribution and climatic suitability of typical eco-economical tree species in the dryland of northwest China. Yangling:PhD thesis of University of Chinese Academy of Sciences (Research Center of Soil and Water Conservation and Ecological Environment), 2018. | |
赵佳强, 石娟. 基于新型最大熵模型预测刺槐叶瘿蚊(双翅目:瘿蚊科)在中国的适生区. 林业科学, 2019. 55 (2): 118- 127. | |
Zhao J Q , Shi J . Prediction of the potential geographical distribution of Obolodiplosis robiniae (Diptera:Cecidomyiidae) in China based on a novel Maximum Entropy model. Scientia Silvae Sinicae, 2019. 55 (2): 118- 127. | |
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. | |
中国绿化基金会. 2018. "一带一路"胡杨林生态修复计划.北京:中国林业出版社. | |
China Green Foundation. 2018. Ecological restoration plan of Populus euphratica forest along the belt and road. Beijing:China Forestry Publishing House.[in Chinese] | |
朱耿平, 刘强, 高玉葆. 提高生态位模型转移能力来模拟入侵物种的潜在分布. 生物多样性, 2014. 22 (2): 223- 230. | |
Zhu G P , Liu Q , Gao Y B . Improving ecological niche model transferability to predict the potential distribution of invasive exotic species. Biodiversity Science, 2014. 22 (2): 223- 230. | |
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. | |
Ahmed S E , McInerny G , O'Hara K , et al. Scientists and software-surveying the species distribution modelling community. Diversity and Distributions, 2015. 21 (3): 258- 267. | |
Bellard C , Bertelsmeier C , Leadley P , et al. Impacts of climate change on the future of biodiversity. Ecology Letters, 2012. 15 (4): 365- 377. | |
Borcard D , Legendre P , Drapeau P . Partialling out the spatial component of ecological variation. Ecology, 1992. 73 (3): 1045- 1055. | |
Chen I C , Hill J K , Ohlemüller R , et al. Rapid range shifts of species associated with high levels of climate warming. Science, 2011. 333 (6045): 1024- 1026. | |
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. | |
Elith J , Phillips S J , Hastie T , et al. A statistical explanation of MaxEnt for ecologists. Diversity & Distributions, 2011. 17 (1): 43- 57. | |
Farashi A , Kaboli M , Karami M . Predicting range expansion of invasive raccoons in northern Iran using ENFA model at two different scales. Ecological Informatics, 2013. 15 (2): 96- 102. | |
Gibson L , Lee T M , Koh L P , et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature, 2011. 478, 378- 381. | |
Guo Y L , Li X , Zhao Z F , et al. Modeling the distribution of Populus euphratica in the Heihe River Basin, an inland river basin in an arid region of China. Science China:Earth Sciences, 2018. 61 (11): 1669- 1684. | |
He C Y , Zheng S X , Zhang J G , et al. Clonal reproduction and natural variation of Populus canescens patches. Tree Physiology, 2010. 30 (11): 1383- 1390. | |
Huang J H , Li G Q , Li J , et al. Projecting the range shifts in climatically suitable habitat for Chinese sea buckthorn under climate change scenarios. Forests, 2017. 9 (1): 1- 12. | |
Hughes G , Madden L V . Evaluating predictive models with application in regulatory policy for invasive weeds. Agricultural Systems, 2003. 76 (2): 755- 774. | |
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. | |
Merow C , Smith M J , Silander J A . A practical guide to MaxEnt for modeling species' distributions:what it does, and why inputs and settings matter. Ecography, 2013. 36 (10): 1058- 1069. | |
Narouei-Khandan H A , Harmon C L , Harmon P , et al. Potential global and regional geographic distribution of Phomopsis vaccinii on Vaccinium species projected by two species distribution models. European Journal of Plant Pathology, 2017. 148 (4): 919- 930. | |
Phillips S J , Anderson R P , Schapire R E . Maximum entropy modeling of species geographic distributions. Ecological Modelling, 2006. 190 (3/4): 231- 259. | |
Phillips S J , Dudík M . Modeling of species distributions with MaxEnt:new extensions and a comprehensive evaluation. Ecography, 2008. 31 (2): 161- 175. | |
Phillips S J , Anderson R P , Dudík M , et al. Opening the black box:an open-source release of Maxent. Ecography, 2017. 40 (7): 887- 893. | |
Sillero N . What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecological Modelling, 2011. 222 (8): 1343- 1346. | |
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. | |
Verbruggen H , Tyberghein L , Belton G S , et al. Improving transferability of introduced species' distribution models:new tools to forecast the spread of a highly invasive seaweed. PLoS One, 2013. 8 (6): e68337. | |
Wang D D , Yu Z T , Peng G , et al. Water use strategies of Populus euphratica seedlings under groundwater fluctuation in the Tarim River Basin of Central Asia. Catena, 2018. 166, 89- 97. | |
Wang J , Wu Y X , Ren G P , et al. Genetic differentiation and delimitation between ecologically diverged Populus euphratica and P. pruinosa. PLoS One, 2011. 6 (10): e26530. | |
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. | |
Zhang K L , Yao L J , Meng J S , et al. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Science of the Total Environment, 2018. 634, 1326- 1334. |
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