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

摘要/Abstract

摘要：

天山云杉（Picea schrenkiana）是天山地区最主要的树种之一，为天山山地的水土保持和水源涵养发挥着重要的作用。本文基于气候相似性原理以最大熵（MaxEnt）模型为基础结合ArcGIS预测当前（2000—2020年）与2020—2040年、2040—2060年两个时段3种气候情景低温室气体排放条件（SSP1-2.6）、中温室气体排放条件（SSP3-7.0）和高温室气体排放条件（SSP5-8.5）下天山云杉的潜在分布范围，并分析影响天山云杉分布的主要环境因子。结果表明：（1） MaxEnt模型对天山云杉的分布区预测可信度高，所有模型AUC值均大于0.99。温度（等温性、季节性温度变异、年平均温度）与降水（最冷季度的降水量、最湿月的降水量、最干季度的降水量）是影响天山云杉潜在分布的主导因子；其中温度为当前主要的影响因子，最冷季度的降水量为未来的主要影响因子。（2）当前时期天山云杉的适生区主要分布在新疆、青海、内蒙古、西藏、甘肃、宁夏、陕西、四川等地区的山区，总适生区面积为299.17×10⁴ km²，高适区面积为49.45×10⁴ km²。未来各情景下天山云杉的潜在适生区面积变化不大且分布仍以这些地区为主，但高适生区较当前均表现增加。除2020—2040年SSP5-8.5情景下天山云杉的适宜分布向东南方向迁移外，其他情景天山云杉的适宜分布有向西扩展的趋势。

关键词: 最大熵（MaxEnt）模型, 气候变化, 潜在分布区, 天山云杉

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

周杰, 王旭虎, 杜维波, 周晓雷, 杨洁, 张晓玮. 气候变化背景下的天山云杉潜在分布区预测[J]. 干旱区研究, 2024, 41(7): 1167-1176.

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.

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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/℃

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