基于Biomod2组合模型的我国山杨潜在分布区研究

doi:10.12302/j.issn.1000-2006.202205022

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南京林业大学学报（自然科学版） ›› 2024, Vol. 48 ›› Issue (2): 247-255.doi: 10.12302/j.issn.1000-2006.202205022

基于Biomod2组合模型的我国山杨潜在分布区研究

高明龙¹(), 铁牛², 张晨¹, 李凤滋¹, 乌雅瀚¹, 罗奇辉¹, 王子瑞¹, 刘磊¹, 萨如拉¹^,^*()

1.内蒙古农业大学林学院,内蒙古呼和浩特 010019
2.内蒙古自治区林业和草原局,内蒙古呼和浩特 010020

收稿日期:2022-05-13 修回日期:2022-08-05 出版日期:2024-03-30 发布日期:2024-04-08
通讯作者: *萨如拉(sarula213@163.com),教授。
作者简介:高明龙(gml9652@foxmail.com)。
基金资助:
内蒙古自治区科技计划项目(2020GG0067)

Modelling the potential distribution area of Populus davidiana in China based on the Biomod2

GAO Minglong¹(), TIE Niu², ZHANG Chen¹, LI Fengzi¹, WU Yahan¹, LUO Qihui¹, WANG Zirui¹, LIU Lei¹, SA Rula¹^,^*()

1. Forestry College of Inner Mongolia Agricultural University, Hohhot 010019, China
2. Forestry and Grassland Bureau of Inner Mongolia Autonomous Region, Hohhot 010020, China

Received:2022-05-13 Revised:2022-08-05 Online:2024-03-30 Published:2024-04-08

摘要/Abstract

摘要：

【目的】通过探究环境变化对山杨(Populus davidiana)分布的影响,为山杨资源的保护和开发提供理论支撑。【方法】根据山杨的134条地理分布数据,结合18个气候、土壤及地形因子,基于Biomod2软件包构建组合模型,模拟我国山杨潜在分布区在未来3种气候条件模式下的空间分布格局变化,并确定影响山杨分布的主要环境变量。【结果】我国山杨当前潜在适生分布区主要位于400 mm等降水线两侧较高纬度或较高海拔地区,总面积约为1 560 340.9 km²,约占我国陆地面积的16.2%,其中大兴安岭、长白山、太行山、秦岭、祁连山南麓、横断山、云贵高原等地区为山杨高度适生区;在未来气候条件下,山杨适生区整体呈向西南方向收缩趋势,生境适宜度总体呈下降趋势;影响山杨分布主要环境变量为最热月最高气温、年降水量和海拔;基于5个最优单一模型构建的组合模型比单一模型对山杨适生区预测结果更好,训练集平均受试者工作特征曲线下面积和真实技巧统计值分布为 0.91和0.73,预测准确度较高。【结论】我国山杨空间分布格局主要受水热条件影响,海拔也是影响山杨分布的重要因素。在未来气候条件下,山杨分布区面积将随气候变暖的程度逐渐减少。以山杨作为用材林和生态公益林树种进行造林时,造林地点应选择未来生境适宜度变化不大的地区,以降低未来由于气候变化造成的损失。

关键词: 山杨, Biomod2软件包, 组合模型, 潜在分布区, 气候变暖

Abstract:

【Objective】 This study aims to investigate the effects of changes in environmental factors on the distribution of Populus davidiana, and to provide theoretical support for the conservation and development of P. davidiana resources. 【Method】 This study applied Biomod2 to simulate changes in the spatial distribution pattern of P. davidiana in China's potential distribution areas under three future climatic conditions based on 134 geographical distribution data points of P. davidiana in China, combined with 18 climatic, soil and topographic factors. Then a combinatorial model based on the Biomod2 package was consturcted and identified the main environmental variables affecting the distribution of P. davidiana were identified. 【Result】 The current potential distribution areas of P. davidiana in China were mainly located at higher latitudes or higher altitudes on both sides of the 400 mm precipitation line, with a total area of about 1 560 340.9 km², of which the Greater Khingan Mountains, Changbai Mountains, Taihang Mountains, Qinling Mountains, southern foot of Qilian Mountains, Hengduan Mountains, Yunnan-Guizhou Plateau and other areas are the highest suitable areas for P. davidiana. Under future climatic conditions, the overall trend of suitable areas for P. davidiana will shrink to southwest China, and the overall trend of suitable areas was decreasing. The ensemble model constructed based on the five optimal single models had better prediction results for suitable areas for P. davidiana compared to the single model, and the area under the receiver operating characteristic curve and true skill statistics were distributed as 0.91 and 0.73, with higher prediction accuracy. 【Conclusion】 The spatial distribution pattern of P. davidiana in China was mainly influenced by water and heat conditions, while altitude was also an important factor affecting its distribution. Under future climatic conditions, the area of P. davidiana distribution will gradually decrease based on the degree of climate warming. When planting P. davidiana for timber forests and as an ecological public welfare forest species, planting sites should be selected in areas where habitat suitability will not change significantly in the future, to reduce future losses because of climate change.

Key words: Populus davidiana, Biomod2, ensemble model, potential distribution area, global warming

中图分类号:

S792.114

高明龙,铁牛,张晨,等. 基于Biomod2组合模型的我国山杨潜在分布区研究[J]. 南京林业大学学报（自然科学版）, 2024, 48(2): 247-255.

GAO Minglong, TIE Niu, ZHANG Chen, LI Fengzi, WU Yahan, LUO Qihui, WANG Zirui, LIU Lei, SA Rula. Modelling the potential distribution area of Populus davidiana in China based on the Biomod2[J].Journal of Nanjing Forestry University (Natural Science Edition), 2024, 48(2): 247-255.DOI: 10.12302/j.issn.1000-2006.202205022.

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参考文献 35

[1]	曾建平, 代峰. 气候伦理是否可能[J]. 中国人民大学学报, 2011, 25(3):90-96.
	ZENG J P, DAI F. The possibility of a climatic ethics[J]. J Renmin Univ China, 2011, 25(3):90-96.
[2]	BEZENG B S, MORALES-CASTILLA I, VAN DER BANK M, et al. Climate change may reduce the spread of non-native species[J]. Ecosphere, 2017, 8(3):e01694.DOI: 10.1002/ecs2.1694.
[3]	BELLARD C, BERTELSMEIER C, LEADLEY P, et al. Impacts of climate change on the future of biodiversity[J]. Ecol Lett, 2012, 15(4):365-377.DOI: 10.1111/j.1461-0248.2011.01736.x.
[4]	VILLÉN-PERÉZ S, HEIKKINEN J, SALEMAA M, et al. Global warming will affect the maximum potential abundance of boreal plant species[J]. Ecography, 2020, 43(6):801-811.DOI: 10.1111/ecog.04720.
[5]	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.DOI: 10.1111/j.2006.0906-7590.04596.x.
[6]	刘晓彤, 袁泉, 倪健. 中国植物分布模拟研究现状[J]. 植物生态学报, 2019, 43(4):273-283.
	LIU X T, YUAN Q, NI J. Research advances in modelling plant species distribution in China[J]. Chin J Plant Ecol, 2019, 43(4):273-283.DOI: 10.17521/cjpe.2018.0237.
[7]	ZHAO G H, CUI X Y, SUN J J, et al. Analysis of the distribution pattern of Chinese Ziziphus jujuba under climate change based on optimized biomod2 and MaxEnt models[J]. Ecol Indic, 2021, 132:108256.DOI: 10.1016/j.ecolind.2021.108256.
[8]	TANG J H, LI J H, LU H, et al. Potential distribution of an invasive pest,Euplatypus parallelus,in China as predicted by Maxent[J]. Pest Manag Sci, 2019, 75(6):1630-1637.DOI: 10.1002/ps.5280.
[9]	赵光华, 崔馨月, 王智, 等. 气候变化背景下我国酸枣潜在适生区预测[J]. 林业科学, 2021, 57(6):158-168.
	ZHAO G H, CUI X Y, WANG Z, et al. Prediction of potential distribution of Ziziphus jujuba var.spinosa in China under context of climate change[J]. Sci Silvae Sin, 2021, 57(6):158-168.DOI: 10.11707/j.1001-7488.20210618.
[10]	LI Y C, LI M Y, LI C, et al. Optimized maxent model predictions of climate change impacts on the suitable distribution of Cunninghamia lanceolata in China[J]. Forests, 2020, 11(3):302.DOI: 10.3390/f11030302.
[11]	吴晓萌, 叶冬梅, 白玉娥, 等. 基于MaxEnt模型的中国白杄分布格局及未来变化[J]. 西北植物学报, 2022, 42(1):162-172.
	WU X M, YE D M, BAI Y E, et al. Distribution pattern and future change of Picea meyeri in China based on MaxEnt model[J]. Acta Bot Boreali Occidentalia Sin, 2022, 42(1):162-172.DOI: 10.7606/j.issn.1000-4025.2022.01.0162.
[12]	GONG Y F, HU X K, HAO Y W, et al. Projecting the proliferation risk of Oncomelania hupensis in China driven by SSPs:a multi-scenario comparison and integrated modeling study[J]. Adv Clim Change Res, 2022, 13(2):258-265.DOI: 10.1016/j.accre.2022.02.004.http://dx.doi.org/10.1016/j.accre.2022.02.004
[13]	HAO T X, ELITH J, GUILLERA-ARROITA G, et al. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD[J]. Divers Distrib, 2019, 25(5):839-852.DOI: 10.1111/ddi.12892.
[14]	卫尊征, 郭丽琴, 张金凤, 等. 利用trnL-F序列分析杨属树种的系统发育关系[J]. 北京林业大学学报, 2010, 32(2):27-33.
	WEI Z Z, GUO L Q, ZHANG J F, et al. Phylogenetic relationship of Populus by trnL-F sequence analysis[J]. J Beijing For Univ, 2010, 32(2):27-33.DOI: 10.13332/j.1000-1522.2010.02.031.
[15]	中国科学院中国植物志编辑委员会. 中国植物志-第二十卷, 第二分册[M]. 北京: 科学出版社,1984.
	Delectis Florae Reipublicae Popularis Sinicae, Agendae Academiae Sinicae Edita. Flora reipublicae popularis sinicae tomus 20(2)[M]. Beijing: Science Press,1984.
[16]	翁宇威, 蔡闻佳, 王灿. 共享社会经济路径(SSPs)的应用与展望[J]. 气候变化研究进展, 2020, 16(2):215-222.
	WENG Y W, CAI W J, WANG C. The application and future directions of the Shared Socioeconomic Pathways(SSPs)[J]. Clim Change Res, 2020, 16(2):215-222.DOI: 10.12006/j.issn.1673-1719.2019.078.
[17]	YANG X Q, KUSHWAHA S P S, SARAN S, et al. MaxEnt modeling for predicting the potential distribution of medicinal plant,Justicia adhatoda L.in Lesser Himalayan foothills[J]. Ecol Eng, 2013, 51:83-87.DOI: 10.1016/j.ecoleng.2012.12.004.
[18]	张天蛟, 刘刚. 提高生态位模型时间转移能力的方法研究[J]. 中国农业大学学报, 2017, 22(2):98-105.
	ZHANG T J, LIU G. Study of methods to improve the temporal transferability of niche model[J]. J China Agric Univ, 2017, 22(2):98-105.DOI: 10.11841/j.issn.1007-4333.2017.02.12.
[19]	DORMANN C F, ELITH J, BACHER S, et al. Collinearity:a review of methods to deal with it and a simulation study evaluating their performance[J]. Ecography, 2013, 36(1):27-46.DOI: 10.1111/j.1600-0587.2012.07348.x.
[20]	ALLOUCHE O, TSOAR A, KADMON R. Assessing the accuracy of species distribution models:prevalence,Kappa and the true skill statistic (TSS)[J]. J Appl Ecol, 2006, 43(6):1223-1232.DOI: 10.1111/j.1365-2664.2006.01214.x.
[21]	吴艺楠, 马育军, 刘文玲, 等. 基于BIOMOD的青海湖流域高原鼠兔分布模拟[J]. 动物学杂志, 2017, 52(3):390-402.
	WU Y N, MA Y J, LIU W L, et al. Modelling the distribution of plateau pika(Ochotona curzoniae) in Qinghai Lake basin using BIOMOD[J]. Chin J Zool, 2017, 52(3):390-402.DOI: 10.13859/j.cjz.201703004.
[22]	郭恺琦, 姜小龙, 徐刚标. 薄片青冈潜在适生区及气候变化对其分布的影响[J]. 生态学杂志, 2021, 40(8):2563-2574.
	GUO K Q, JIANG X L, XU G B. Potential suitable distribution area of Quercus lamellosa and the influence of climate change[J]. Chin J Ecol, 2021, 40(8):2563-2574.DOI: 10.13292/j.1000-4890.202108.023.
[23]	ZHAN P, WANG F Y, XIA P G, et al. Assessment of suitable cultivation region for Panax notoginseng under different climatic conditions using MaxEnt model and high-performance liquid chromatography in China[J]. Ind Crops Prod, 2022, 176:114416.DOI: 10.1016/j.indcrop.2021.114416.
[24]	李易. 中国山杨群体历史动态初步研究[D]. 北京: 中国林业科学研究院, 2020.
	LI Y. A preliminary study on demographic history of Populus davidiana[D]. Beijing: Chinese Academy of Forestry, 2020.
[25]	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):1292-1305.
	GUO Y L, ZHAO Z F, QIAO H J, et al. Challenges and development trend of species distribution model[J]. Adv Earth Sci, 2020, 35(12):1292-1305.DOI: 10.11867/j.issn.1001-8166.2020.110.
[26]	袁喆, 严登华, 杨志勇, 等. 1961—2010年中国400 mm和800 mm等雨量线时空变化[J]. 水科学进展, 2014, 25(4):494-502.
	YUAN Z, YAN D H, YANG Z Y, et al. Research on temporal and spatial change of 400 mm and 800 mm rainfall contours of China in 1961 to 2000[J]. Adv Water Sci, 2014, 25(4):494-502.DOI: 10.14042/j.cnki.32.1309.2014.04.002.
[27]	贺敏, 魏江生, 石亮, 等. 大兴安岭南段山杨径向生长和死亡对区域气候变化的响应[J]. 生态学杂志, 2018, 37(11):3237-3244.
	HE M, WEI J S, SHI L, et al. The response of radial growth and death of Populus davidiana to regional climate change in southern Greater Xing'an Mountains[J]. Chin J Ecol, 2018, 37(11):3237-3244.DOI: 10.13292/j.1000-4890.201811.026.
[28]	潘春芳, 赵秀海, 夏富才, 等. 长白山山杨种群的性比格局及其空间分布[J]. 生态学报, 2011, 31(2):297-305.
	PAN C F, ZHAO X H, XIA F C, et al. Sex ratio and spatial pattern in Populus davidiana in Changbai Mountain[J]. Acta Ecol Sin, 2011, 31(2):297-305.
[29]	DIAS J M A, BOSKI T, RODRIGUES A, et al. Coast line evolution in Portugal since the Last Glacial Maximum until present: a synthesis[J]. Mar Geol, 2000, 170(1/2):177-186.DOI: 10.1016/S0025-3227(00)00073-6.
[30]	TARASOV P, BEZRUKOVA E, KARABANOV E, et al. Vegetation and climate dynamics during the Holocene and Eemian interglacials derived from Lake Baikal pollen records[J]. Palaeogeogr Palaeoclimatol Palaeoecol, 2007, 252(3/4):440-457.DOI: 10.1016/j.palaeo.2007.05.002.
[31]	白伟宁, 张大勇. 植物亲缘地理学的研究现状与发展趋势[J]. 生命科学, 2014, 26(2):125-137.
	BAI W N, ZHANG D Y. Current status and future directions in plant phylogeography[J]. Chin Bull Life Sci, 2014, 26(2):125-137.DOI: 10.13376/j.cbls/2014020.
[32]	HOU Z, LI A, ZHANG J G. Genetic architecture,demographic history,and genomic differentiation of Populus davidiana revealed by whole-genome resequencing[J]. Evol Appl, 2020, 13(10):2582-2596.DOI: 10.1111/eva.13046.
[33]	CHEN I C, HILL J K, OHLEMÜLLER R, et al. Rapid range shifts of species associated with high levels of climate warming[J]. Science, 2011, 333(6045):1024-1026.DOI: 10.1126/science.1206432.
[34]	SUON S, LI Y, POM L, et al. Spatiotemporal analysis of soil moisture drought over China during 2008-2016[J]. J Water Resour Prot, 11,700-712.DOI: 10.4236/jwarp.2019.116041.
[35]	胡忠俊, 张镱锂, 刘林山, 等. 生物避难所及其识别方法评述[J]. 生态学杂志, 2013, 32(12):3397-3406.
	HU Z J, ZHANG Y L, LIU L S, et al. Refugia and their identification methods:a review[J]. Chin J Ecol, 2013, 32(12):3397-3406.DOI: 10.13292/j.1000-4890.2013.0518.

类型 type	变量 variable	描述 description	类型 type	变量 variable	描述 description
气候因子 bioclimatic variable	bio1	年平均气温,×10 ℃	土壤因子 top soil variable	T-GRAVE	顶层土砾石含量,%
	bio2	平均气温日较差, ×10 ℃		T-OC	顶层有机碳含量,%
	bio3	等温性		T-PH-H₂O	顶层土壤pH(H₂O), -Log(H⁺)
	bio5	最热月最高气温, ×10 ℃		T-BS	顶层基本饱和度,%
	bio6	最冷月最低气温, ×10 ℃		T-CEC-CLAY	黏性成分阳离子交换能力, cmol/kg
	bio12	年降水量,mm		T-CEC	顶层土壤的阳离子交换能力, cmol/kg
	bio19	最冷季度降水量,mm		T-ESP	顶层可交换钠盐,%
地形因子 terrain variable				T-SILT	顶层粉沙粒含量,%
	Elev	海拔,m		T-TEB	顶层交换性盐基, cmol/kg
				T-USDA-CLASS	顶层USDA土壤质地分类

模型 model	受试者工作特征曲线下面积(AUC) area under the receiver operating characteristic curve			真实技巧统计值(TSS) true skill statistics
模型 model	平均值 mean	标准差 SD	变异系数 CV	平均值 mean	标准差 SD	变异系数 CV
人工神经网络ANN	0.793	0.046	0.058	0.536	0.052	0.099
分类与回归树模型CTA	0.754	0.033	0.044	0.523	0.057	0.109
柔性判别分析FDA	0.833	0.025	0.030	0.581	0.044	0.076
推进式回归树模型GBM	0.852	0.035	0.041	0.595	0.056	0.094
广义线性模型GLM	0.832	0.042	0.050	0.563	0.078	0.138
最大熵值模型MaxEnt	0.813	0.047	0.057	0.533	0.090	0.169
随机森林RF	0.854	0.031	0.036	0.604	0.069	0.113
表面分布区分室模型SRE	0.687	0.045	0.065	0.373	0.090	0.240
组合模型ensemble model	0.909	0.005	0.007	0.730	0.019	0.027

气候情景 climate scenario	总适生区/ km² total suitable area	高度适生区/ km² most suitable area	丧失区/ km² contraction area	增加区/ km² expansion area	保留区/ km² unchanged area	丧失率/% contraction rate	增加率/% expansion rate	保留率/% unchanged rate
末次冰盛期LGM	1 317 833.8	967 029.8	399 224.2	1 409 104.1	923 865.4	32.3	106.9	70.1
全新世中期MH	2 327 602.8	935 409.7	1 165 915.9	388 228.2	1 169 468.6	50.1	16.7	50.2
当前current	1 560 340.9	553 489.5	—	—	—	—	—	—
SSP126-2050s	1 165 734.2	381 675.3	505 082.6	110 475.9	1 055 258.3	32.3	7.1	67.6
SSP126-2090s	1 200 171.5	355 901.7	126 874.4	161 311.7	1 038 859.8	10.9	13.8	89.1
SSP245-2050s	1 135 828.7	341 538.9	551 329.5	126 817.3	1 009 011.4	35.3	8.1	64.7
SSP245-2090s	1 017 186.2	275 645.4	235 563.0	116 920.5	900 265.7	20.7	10.3	79.3
SSP585-2050s	1 091 248.7	309 337.0	636 250.9	167 158.7	924 090.0	40.8	10.7	59.2
SSP585-2090s	608 885.2	107 181.9	533 131.9	50 768.41	558 116.8	51.1	4.7	51.1

气候情景 climate scenario	年降水量/mm annual precipitation			最热月最高气温/℃ max air temperature			海拔/m elevation			物种生境适宜度 suitability of species habitat
气候情景 climate scenario	当前 current	2050s	2090s	当前 current	2050s	2090s	当前 current	2050s	2090s	当前 current	2050s	2090s
当前	667.99			24.76			1 316.82			0.64
SSP126		676.45	726.17		27.14	27.09		1 317.82	1 320.82		0.46	0.46
SSP245		708.70	724.45		27.59	28.60		1 318.82	1 321.82		0.44	0.40
SSP585		717.41	782.54		28.15	31.06		1 319.82	1 322.82		0.42	0.31

基于Biomod2组合模型的我国山杨潜在分布区研究

Modelling the potential distribution area of Populus davidiana in China based on the Biomod2

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[1]	方静, 张书曼, 严善春, 武帅, 赵佳齐, 孟昭军. 两种丛枝菌根真菌复合接种对青山杨叶片抗美国白蛾的影响[J]. 南京林业大学学报（自然科学版）, 2024, 48(2): 144-154.
[2]	罗楚滢, 佘济云, 唐子朝. 基于SSPs气候场景的濒危植物银杉潜在分布区预测[J]. 南京林业大学学报（自然科学版）, 2024, 48(1): 161-168.
[3]	谢立红, 黄庆阳, 曹宏杰, 杨帆, 王继丰, 倪红伟. 气候变暖对黑龙江省五大连池兴安落叶松径向生长的影响[J]. 南京林业大学学报（自然科学版）, 2022, 46(2): 150-158.
[4]	缪菁, 王勇, 王璐, 许晓岗. 基于MaxEnt模型的苦槠潜在地理分布格局变迁预测[J]. 南京林业大学学报（自然科学版）, 2021, 45(3): 193-198.
[5]	吴显坤,南程慧,汤庚国,李垚,毛丽君,张志成. 气候变化对浙江楠潜在分布范围及空间格局的影响[J]. 南京林业大学学报（自然科学版）, 2016, 40(06): 85-91.
[6]	彭冶,王焱,顾慧,叶建仁. 外来观赏植物大花金鸡菊在中国的潜在地理分布预测[J]. 南京林业大学学报（自然科学版）, 2016, 40(01): 53-58.
[7]	魏强,凌雷,张广忠,柴春山,王多锋,陶继新,薛睿. 兴隆山森林群落不同演替阶段优势乔木种群结构特征[J]. 南京林业大学学报（自然科学版）, 2015, 39(05): 59-66.
[8]	李勤文,李永和,刘建宏,杨松,马明友,佘光辉. 基于Maxent模型的细梢小卷蛾云南潜在分布区预测[J]. 南京林业大学学报（自然科学版）, 2013, 37(06): 37-40.
[9]	闫绍鹏,杨瑞华,王秋玉. 低温胁迫对欧美杂种山杨无性系的生理影响[J]. 南京林业大学学报（自然科学版）, 2013, 37(06): 161-164.