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

doi:10.12302/j.issn.1000-2006.202205022

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (2): 247-255.doi: 10.12302/j.issn.1000-2006.202205022

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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

Abstract

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

CLC Number:

S792.114

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, 2024, 48(2): 247-255.

Figures/Tables 9

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References 35

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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

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

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