气候变化背景下的天山云杉潜在分布区预测

doi:10.13866/j.azr.2024.07.08

Abstract

Abstract:

Picea schrenkiana, one of the most important tree species in the Tianshan Mountains, plays an important role in soil and water conservation in this region. In this study, the potential distribution and dominant climatic factors of current and three climate change scenarios (i.e., low, the medium, and the high greenhouse gas emission scenarios; SSP1-2.6, SSP3-7.0 and SSP5-8.5) in two future time periods (2020-2040 and 2040-2060) were modeled using the maximum entropy model (MaxEnt). The results were: (1) AUC values of the MaxEnt model were all greater than 0.99, indicating that the model had high reliability for predicting the distribution region of P. schrenkiana. The results from the jackknife test and climate factor response curves revealed that isotherm, seasonal temperature variation, annual mean temperature, precipitation in the coldest quarter, precipitation in the wettest month, and precipitation in the driest quarter were the main factors affecting the potential distribution of P. schrenkiana. Overall, temperature is key factor affecting the potential distribution at present, and precipitation, especially precipitation in the coldest quarter, will be the key factor in the future. (2) At present, the potential distribution of P. schrenkiana is mainly in the mountainous regions of the Xinjiang, Qinghai, Inner Mongolia, Xizang, Gansu, Ningxia, Shanxi, and Sichuan provinces. The total potential area of P. schrenkiana is 299.17×10⁴ km² at present, and the area of highest suitability is 49.45×10⁴ km². The potential distribution of P. schrenkiana in future scenarios is still dominated by its currently simulated distribution regions, meaning that the simulated potential area of P. schrenkiana does not significantly change under the different future scenarios. However, the most suitable regions tended to be larger than currently. The potential distribution of P. schrenkiana tended to expand to the west in all scenarios, apart from a migration to the southeast that was predicted under the SSP5-8.5 scenario from 2020 to 2040.

Key words: maximum entropy (MaxEnt) model, climate change, potential distribution area, Picea schrenkiana

ZHOU Jie, WANG Xuhu, DU Weibo, ZHOU Xiaolei, YANG Jie, ZAHNG Xiaowei. Prediction of potential distribution area of Picea schrenkiana under the background of climate change[J].Arid Zone Research, 2024, 41(7): 1167-1176.

Figures/Tables 9

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数据图层编号	数据说明	数据图层编号	数据说明	数据图层编号	数据说明
bio1	年平均温度/℃	bio8	最湿季度的平均温度/℃	bio14	最干月的降水量/mm
bio2	每月温差的平均值/℃	bio9	最干季度的平均温度/℃	bio15	季节性降水量变异系数/%
bio3	等温性/%	bio10	最热季度的平均温度/℃	bio16	最湿季度的降水量/mm
bio4	季节性温度变异	bio11	最冷季度的平均温度/℃	bio17	最干季度的降水量/mm
bio5	最热月的最高温度/℃	bio12	年降水量/mm	bio18	最热季度的降水量/mm
bio6	最冷月的最低温度/℃	bio13	最湿月的降水量/mm	Bio19	最冷季度的降水量/mm
bio7	温度年较差/℃

时期	情景	训练集AUC
当前（2000—2020年）	-	0.991
2020—2040年	SSP1-2.6	0.990
	SSP3-7.0	0.992
	SSP5-8.5	0.992
2040—2060年	SSP1-2.6	0.992
	SSP3-7.0	0.991
	SSP5-8.5	0.991

变量	描述	贡献率/％	变量	描述	贡献率/％
bio19	最冷季度的降水量	17.3	bio11	最冷季度的平均温度	2.8
bio3	等温性	15.8	bio2	每月温差的平均值	1.4
bio4	季节性温度变异	12.6	bio12	年降水量	0.4
bio1	年平均温度	12.0	bio7	温度年较差	0.3
bio13	最湿月的降水量	10.6	bio6	最冷月的最低温度	0.2
bio17	最干季度的降水量	7.6	bio5	最热月的最高温度	0.1
bio18	最热季度的降水量	7.1	bio16	最湿季度的降水量	0
bio9	最干季度的平均温度	4.6	bio8	最湿季度的平均温度	0
bio14	最干月的降水量	3.7	bio10	最热季度的平均温度	0
bio15	季节性降水量变异系数	3.3

情景	时期	高适生区	面积变化比例%	中适生区	面积变化比例%	低适生区	面积变化比例%	总适生区	面积变化比例%
当前	-	49.45	-	85.48	-	164.24	-	299.17	-
SSP1-2.6	2020—2040年	63.43	28.27	78.78	-7.84	165.29	0.64	307.50	2.78
SSP1-2.6	2040—2060年	62.61	26.61	74.30	-13.08	145.70	-11.29	282.61	-5.54
SSP3-7.0	2020—2040年	60.73	22.81	75.90	-11.21	152.62	-7.08	289.25	-3.32
SSP3-7.0	2040—2060年	65.86	33.19	86.09	0.71	153.92	-6.28	305.87	2.24
SSP5-8.5	2020—2040年	64.06	29.54	82.63	-3.33	141.94	-13.58	288.63	-3.52
SSP5-8.5	2040—2060年	64.56	30.56	87.24	2.06	154.53	-5.91	306.33	2.39

Prediction of potential distribution area of Picea schrenkiana under the background of climate change

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