气候变化背景下我国长柄扁桃潜在适生区预测

doi:10.11707/j.1001-7488.LYKX20210840

Abstract

Abstract:

Objective: Amygdalus pedunculata is one of the important oil tree species in China, with extremely high economic value and ecological benefits. This study aims to predict the suitable areas of A. pedunculata in China and its response to climate change, which would provide scientific basis for the protection and artificial planting of A. pedunculata resources. Method: A total of 148 A. pedunculata distribution data and 34 environmental factors were filtered out by R language and ArcGis. The ENMeval software package was used to optimize the maximum entropy niche model (MaxEnt) parameters, and complete the filtering of environmental factors required for modeling which was based on Pearson correlation analysis and VIF variance expansion factor analysis. Jackknife was used to evaluate the dominant environmental factors in the suitable area of A. pedunculata. The optimized model was used to analyze the geographical distribution of the currently suitable distribution areas of A. pedunculata, to speculate the potential distribution in the Last Inter Glacial, the Last Glacial Maximum and the Mid Holocene, and analyze their potential distribution. According to the sixth climate model of IPCC, the changing trend of A. pedunculata in the future distribution area under different climatic scenarios could be predicted. Result: The results of model optimization showed that when the model feature combinations were linear, quadratic, fragmented, product and threshold features, and the regulation radio was 1.5, the training omission rate and low complexity of MaxEnt model were low, and the fitting was the best. The AUC value of the receiver-operating characteristic curve was 0.967, showing that the model prediction results were accurate and high reliability. According to the results of the Jackknife, the precipitation of the warmest season, the precipitation seasonality, the temperature seasonality, the annual mean temperature, and the topsoil base saturation were the dominant environmental factors affecting the distribution of A. pedunculata. The prediction results of the model showed that at present suitable areas of A. pedunculata in China were mainly distributed in the Inner Mongolia Plateau and Loess Plateau. The analysis of historical, current and future adaptation areas showed that A. pedunculata was sensitive to climate change. Under different climate change scenarios in the future, the suitable area of A. pedunculata would shrink and tend to migrate to middle and high latitude, and high altitude areas, especially under high greenhouse gas emission concentration, the migration distance would be longer. Conclusion: The optimized model can accurately predict the potential geographical distribution area of A. pedunculata. Temperature and precipitation are the most likely environmental factors causing the migration of A. pedunculata distribution areas. In the future, climate warming will cause the migration of the distribution area of A. pedunculata. Under the background of future climate warming, suitable areas of A. pedunculata tend to decrease. The reduction areas are mainly in low latitudes and low altitudes, while newly suitable areas appear in medium and high latitudes, and high altitudes (Hohhot, Ordos, Xilingol, Yan'an).

Key words: Amygdalus pedunculata, MaxEnt model, suitable area, climate change, environmental factors

CLC Number:

S718.45

Shuning Zhang,Junxing Chen,Dun Ao,Mei Hong,Yaqian Zhang,Fuhai Bao,Lin Wang,Tana Wuyun,Yu’e Bai,Wenquan Bao. Prediction of Potential Suitable Areas of Amygdalus pedunculata in China under Climate Change[J]. Scientia Silvae Sinicae, 2023, 59(12): 25-36.

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类型 Type	因子 Variables	描述 Description	单位 Units
气候生物因子 Bio climatic variables	Bio1	年平均气温Annual mean temperature	℃
	Bio4	气温变异系数Temperature seasonality	CV
	Bio5	最热月最高温Max temperature of warmest month	℃
	Bio6	最冷月最低温Min temperature of coldest month	℃
	Bio7	气温年较差Temperature annual range	℃
	Bio12	年降水量Annual precipitation	mm
	Bio15	降水量变异系数Variation coefficient of precipitation	%
	Bio18	最暖季度降水量Precipitation of warmest quarter	mm
土壤因子 Top soil variables	T-SAND	表层土壤沙砾百分比Percentage sand in the topsoil	%wt
	T-OC	土壤有机碳百分比Percentage of organic carbon in topsoil	%wt
	T-PH-H₂O	表层土壤pH值pH-value of topsoil（H₂O）	log (H⁺)
	T-BS	表层土壤盐基饱和度The base saturation in topsoil	%
地形因子Terrain	ELEV	海拔Elevation	m

气候变化情景 Climate change scenarios		高度适生区 Most suitable area/hm²	一般适生区 Suitable area/hm²	较不适生区 Marginally unsuitable area/hm²	最不适生区 Unsuitable area/hm²	总适生区 Total suitable area (%)
末次间冰期 Last inter glacial		620 431.9	655 532.6	2179 945	6 154 269	1 275 964.6
末次盛冰期Last glacial maximum		295 434	158 611.1	498 993.1	8 656 753	454 045.1
全新中世纪Mid holocene		98 385.4	168 535.4	814 250	8 529 008	266 920.8
当前Current		219 166.7	228 107.6	779 600.7	8 051 493	447 274.3
SSP126	2050S	278 333.3	357 621.5	886 406.3	6 154 269	635 954.8
SSP126	2090S	191 579.9	247 829.9	866 284.7	8 656 753	439 409.8
SSP245	2050S	197 534.7	360 451.4	846 319.4	8 529 008	197 571.2
SSP245	2090S	150 225.7	195 607.6	989 652.8	8 051 493	345 833.3
SSP585	2050S	187 256.9	253 576.4	1 078 611	7 756 007	440 833.3
SSP585	2090S	176 388.9	269 878.5	1 044 514	7 787 587	446 267.4

项目 Item	末次间冰期 Last inter glacial	末次盛冰期 Last glacial maximum	全新世中期 Mid holocene	2050s			2090s
项目 Item	末次间冰期 Last inter glacial	末次盛冰期 Last glacial maximum	全新世中期 Mid holocene	SSP126	SSP245	SSP585	SSP126	SSP245	SSP585
保留率 Unchange rate (%)	69.19	20.01	41.37	79.63	70.78	63.65	89.25	72.67	71.00
丧失率 Contraction rate (%)	215.27	81.44	18.16	20.56	29.69	36.38	53.83	52.60	27.54
新增率 Expansion rate (%)	33.16	39.41	58.68	63.32	53.83	34.50	9.15	4.33	28.17

时期 Period	末次间冰期 Last inter glacial	末次盛冰期 Last glacial maximum	全新世中期 Mid holocene	当前 Current	2050s			2090s
时期 Period	末次间冰期 Last inter glacial	末次盛冰期 Last glacial maximum	全新世中期 Mid holocene	当前 Current	SSP126	SSP245	SSP585	SSP126	SSP245	SSP585
末次盛冰期 Last glacial maximum	693 888
全新世中期Mid holocene	281 946	547 357
当前Current	687 602	305 587	440 539
SSP126-2050s	723 126	382 763	460 460	78 638
SSP245-2050s	703 742	423 268	433 321	118 648	53 311
SSP585-2050s	405 111	486 170	174 783	295 097	297 781	264 004
SSP126-2090s	609 924	412 027	339 118	142 269	126 410	94 336	171 905
SSP245-2090s	600 745	392 373	333 615	131 199	127 372	103 526	171 422	20 985
SSP585-2090s	444 933	535 516	163 687	340 468	336 565	298 300	50 016	210 246	213 056	0

因子 Variables	贡献率 Percentage contribution (%)	置换重要值 Permutation importance
最暖季度降水量Precipitation of warmest quarter	22.7	22.3
降水量变异系数Variation coefficient of precipitation	20.3	24.1
气温变异系数Temperature seasonality	16.3	20
年均气温Annual mean temperature	11.2	3.6
表层土壤盐基饱和度The base saturation in topsoil	8.4	4.9
年降水量Annual precipitation	5	4.4
气温年较差Temperature annual range	4	7.9
表层土壤沙砾百分比Percentage sand in the topsoil	2.6	1.6
最热月最高温Max temperature of warmest month	2.1	9.1
最冷月最低温Min temperature of coldest month	2	1.1
土壤有机碳百分比Percentage of organic carbon in topsoil	0.4	0.6
表层土壤pH值pH-value of topsoil（H₂O）	0.1	0.2

Prediction of Potential Suitable Areas of Amygdalus pedunculata in China under Climate Change

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