华中樱桃适生区模拟和生态特征分析

doi:10.12302/j.issn.1000-2006.202101024

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南京林业大学学报（自然科学版） ›› 2022, Vol. 46 ›› Issue (3): 213-221.doi: 10.12302/j.issn.1000-2006.202101024

华中樱桃适生区模拟和生态特征分析

董京京(), 陈洁, 杨宏, 李蒙, 王贤荣, 伊贤贵^*()

南京林业大学,南方现代林业协同创新中心,南京林业大学生物与环境学院, 南京林业大学樱花研究中心,江苏南京 210037

收稿日期:2021-01-19 接受日期:2021-04-12 出版日期:2022-05-30 发布日期:2022-06-10
通讯作者: 伊贤贵
基金资助:
江苏省现代农业面上项目(BE2017037);江苏林业科技创新与推广项目(LYKJ[2017]14);江苏林业科技创新与推广项目(LYKJ[2018]29);江苏林业科技创新与推广项目(LYKJ[2018]43)

Simulation and analyses of ecological characteristics of Cerasus conradinae adaptability area

DONG Jingjing(), CHEN Jie, YANG Hong, LI Meng, WANG Xianrong, YI Xiangui^*()

Co-Innovation Center for Sustainable Forestry in Southern China,College of Biology and Environment, Cerasus Research Center, Nanjing Forestry University, Nanjing 210037,China.

Received:2021-01-19 Accepted:2021-04-12 Online:2022-05-30 Published:2022-06-10
Contact: YI Xiangui

摘要/Abstract

摘要：

【目的】华中樱桃[Cerasus conradinae (Koehne) Yü et Li]为中国特有樱属植物资源,具有极高的观赏价值和利用前景,分析当前和未来(2050s,2070s)气候变化对其地理分布的影响,以期为华中樱桃的保护和利用提供理论依据。【方法】基于华中樱桃的201个实际地理分布点数据,结合气候特征,采用MaxEnt模型和Arc-GIS软件对其在中国的潜在适生区进行预测,构建预测模型,检验其精确度,确定环境因子贡献率,并使用主成分分析、相关性分析等方法对其主导环境因子进行定性定量的分析。【结果】MaxEnt模型精度检测极为可靠,训练集的AUC为0.945,测试集的为0.949,当前华中樱桃潜在适生区主要在西南、华中、华东地区,两大核心适生区主要集中于重庆市及以西至四川盆地、东至武陵山、南至大娄山、北至大巴山脉一带等呈不规则块状区域,以及云贵高原东部、苗岭山脉以及雪峰山脉西部边缘地区长条状区域。影响其分布的主要环境因子是最湿季降水量(bio 16)、年降水量(bio 12)、温度季节变化方差(bio 4)、温度年较差(bio 7)。在2050s未来气候(CCSM4)情境变化下,总适宜区相较于当代面积呈减少状态,但在2070s未来气候情境变化下总适宜面积增加,且极高适宜区有向高纬度和东北方向扩散,高适宜区有向低纬度方向扩散的趋势。【结论】MaxEnt模型可以准确预测华中樱桃的潜在适生区,华中樱桃适生分布区连续且较为广泛,西南、华中与华东地区为华中樱桃种质资源保护与利用的核心区域。这可为华中樱桃种质保护、亲缘地理与资源利用的后续研究奠定重要基础。

关键词: 华中樱桃, MaxEnt模型, 地理分布, 主导环境因子

Abstract:

【Objective】Cerasus conradinae is a endemic plant resource in China, with high ornamental value and utilization prospects. The impacts of climate change on its geographical distribution in the present and future (2050s, 2070s) were analyzed to provide a theoretical basis for the protection and utilization of C. conradinae.【Method】Based on data from 201 geographical distribution points for C. conradinae combined with climate characteristics, the MaxEnt model and ArcGIS software were used to predict its potential adaptive area in China. The contribution rate, knife cutting test, response curve drawing, principal component analysis and correlation analysis were used to make qualitative and quantitative analyses of the leading environmental factors.【Result】 The MaxEnt model precision detection is very reliable, with a regularized training gain of 0.945, and a test gain of 0.949, The potential adaptive area of C. conradinae is mainly in southwest, central and east China. The two core adaptive areas are concentrated in Chongqing and the area from the Sichuan Basin to the west, Wuling Mountain to the east, the Dalou Mountains to the south and Daba Mountain to the north, as well as the eastern Yunnan-Guizhou Plateau, the Miao Mountain range and the western edge of the Xuefeng Mountain range long strip area. The main environmental factors affecting its distributions were the wettest quarterly precipitation (bio 16), annual precipitation (bio 12), seasonal variation in temperature (bio 4) and the annual temperature difference (bio 7). Under the change of the 2050s future climate (CCSM4) scenario, the total adaptive area decreased compared with the contemporary area, but increased in the 2070s, and the extremely adaptive area spreading to the high latitude in a northeasterly direction, while the highly adaptive area spread in the low latitude direction.【Conclusion】The MaxEnt model can accurately predict the potential adaptive area of C. conradinae. The normal distribution area of C. conradinae is continuous and widespread, and the core area of germplasm resources protection and use is southwest, central and east China. This paper has laid an important foundation for further research on germplasm protection, phylogeography and resource use of Cerasus conradinae.

Key words: Cerasus conradinae, MaxEnt, geographical distribution, dominant environmental factor

中图分类号:

S717

董京京,陈洁,杨宏,等. 华中樱桃适生区模拟和生态特征分析[J]. 南京林业大学学报（自然科学版）, 2022, 46(3): 213-221.

DONG Jingjing, CHEN Jie, YANG Hong, LI Meng, WANG Xianrong, YI Xiangui. Simulation and analyses of ecological characteristics of Cerasus conradinae adaptability area[J].Journal of Nanjing Forestry University (Natural Science Edition), 2022, 46(3): 213-221.DOI: 10.12302/j.issn.1000-2006.202101024.

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变量序号 variable number	变量描述 description of variables	贡献率/% percent contribution	置换重要值/% permutation importance	变量序号 variable number	变量描述 description of variables	贡献率/% percent contribution	置换重要值/% permutation importance
bio 12	年均降水量annual mean precipitation	50.4	31.6	bio 3	等温性isothermality	0.7	2.0
bio 7	温度年较差 temperature annual range	14.7	0.5	bio 19	最冷季降水量precipitation in the coldest season	0.6	5.4
bio 4	温度季节变化方差variance of temperature seasonal change	8.0	28.4	bio 14	最干月降水量driest monthly precipitation	0.4	0.2
bio 17	最干季降水量precipitation in the driest season	6.1	2.3	bio 15	季节降水量变异系数variation coefficient of seasonal precipitation	0.2	1.1
bio 6	极端最低温extreme minimum temperature	5.0	1.8	bio 16	最湿季降水量precipitation in the wettest season	0.2	1.2
bio 1	年均温annual mean temperature	3.8	1.3	bio 8	最湿季均温mean temperature in the wettest season	0.2	0.8
bio 9	最干季均温mean temperature in the driest season	3.2	13.1	bio 5	极端最高温extreme maximum temperature	0.2	5.1
bio 2	平均日温差mean temperature diurnal range	2.4	0.3	bio 18	最暖季降水量precipitation in the warmest season	0.1	1.8
bio 10	最暖季均温mean temperature in the warmest season	2.1	1.1	bio 13	最湿月降水量wettest monthly precipitation	0.1	1.7
bio 11	最冷季均温mean temperature in the coldest season	1.8	0.2

主成分 principal component	特征值 eigenvalue	方差贡献率/% variance contribution rate	累计贡献率/% cumulative contribution rate
1	4.52	45.22	45.22
2	3.07	30.68	75.90
3	1.64	16.38	92.27

气候因子 climatic factors	主成分principal component
气候因子 climatic factors	PC1	PC2	PC3
bio 4	0.02	0.83	0.40
bio 6	0.73	-0.33	0.55
bio 7	-0.33	0.82	0.29
bio 9	0.80	-0.36	0.44
bio 11	0.70	-0.49	0.50
bio 12	0.83	0.46	-0.26
bio 16	0.85	0.03	-0.46
bio 17	0.67	0.69	-0.01
bio 18	0.63	-0.33	-0.60
bio 19	0.70	0.66	-0.05

参数 parameter	bio 7	bio 16	bio 4	bio 12	海拔 altitude	经度 longitude
bio 16	-0.331^**	-
bio 4	0.806^**	-0.112	-
bio 12	0.007	0.820^**	0.315^**	-
海拔altitude	-0.211^**	-0.259^**	-0.601^**	-0.467^**	-
经度longitude	0.460^**	0.377^**	0.685^**	0.756^**	-0.588^**	-
纬度latitude	0.601^**	-0.516^**	0.455^**	-0.383^**	0.115	-0.041

时期 period	气候情境 climatic scenario	适生区面积/万km² permissive area				面积合计/万km² total area
时期 period	气候情境 climatic scenario	低low	中middle	高high	极高extremely high	面积合计/万km² total area
当代contemporary		74.79	65.32	28.43	1.46	170.00
2050s	RCP 2.6	83.29	53.57	28.49	2.85	168.20
	RCP 4.5	71.53	60.26	38.97	4.68	175.44
	RCP 6.0	76.79	50.55	36.90	4.45	168.69
	RCP 8.5	74.15	55.71	35.18	3.33	168.37
2070s	RCP 2.6	78.19	66.16	32.50	2.41	179.26
	RCP 4.5	77.78	57.44	29.76	2.02	167.00
	RCP 6.0	84.30	54.86	34.59	6.28	180.03
	RCP 8.5	68.82	63.03	40.89	4.70	177.44

华中樱桃适生区模拟和生态特征分析

Simulation and analyses of ecological characteristics of Cerasus conradinae adaptability area

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