基于MaxEnt模型的苦槠潜在地理分布格局变迁预测

doi:10.12302/j.issn.1000-2006.202008029

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南京林业大学学报（自然科学版） ›› 2021, Vol. 45 ›› Issue (3): 193-198.doi: 10.12302/j.issn.1000-2006.202008029

基于MaxEnt模型的苦槠潜在地理分布格局变迁预测

缪菁¹(), 王勇², 王璐¹, 许晓岗¹

1.南京林业大学生物与环境学院,江苏南京 210037
2.南京市绿化园林局,江苏南京 210019

收稿日期:2020-08-19 修回日期:2021-02-22 出版日期:2021-05-30 发布日期:2021-05-31
基金资助:
江苏省林业科技创新与推广项目(LYKJ[2018]13)

Prediction of potential geographical distribution pattern change for Castanopsis sclerophylla on MaxEnt

MIAO Jing¹(), WANG Yong², WANG Lu¹, XU Xiaogang¹

1. College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
2. Nanjing Bureau of Greening and Landscaping, Nanjing 210019, China

Received:2020-08-19 Revised:2021-02-22 Online:2021-05-30 Published:2021-05-31

摘要/Abstract

摘要：

【目的】苦槠(Castanopsis sclerophylla)为壳斗科栲属常绿阔叶树种,广布于中国长江以南五岭以北各省区,被视为我国南北方气候的“分界树”。研究苦槠潜在地理分布格局变迁及其对气候变化的响应对于保护其野生资源具有重要意义。【方法】运用MaxEnt模型,模拟和预测苦槠在末次盛冰期(last glacial maximum, LGM)、现代和2070年3个时期的潜在分布区,评估了气候因子变化对其潜在地理分布的影响。【结果】MaxEnt模型模拟现代分布区准确度极高,受试者工作特征曲线下的面积(AUC值)达0.971;苦槠现代高度适宜区主要是皖南山区、大别山区、天目山至北雁荡山地区;中度适宜区扩展到了苏南山区、江西及福建沿海地区;末次盛冰期退缩分布在我国华南北部地区,以及东海大陆架东部。2070年,苦槠的分布受气候影响预测适生区会急剧缩小,生境出现破碎化,高、中度适宜区残存于高海拔地区。Jackknife检验表明,最干季降雨量、最干季均温是影响苦槠地理分布的最主要气候因子。【结论】探讨不同气候因子对苦槠潜在地理分布的影响,利用其生物属性监测亚热带最北缘范围动态变化,可为苦槠的种源保护、生境恢复、繁育驯化等提供科学依据。

关键词: 苦槠, 潜在分布区, MaxEnt模型, 气候变化, 植物地理

Abstract:

【Objective】 Castanopsis sclerophylla is an evergreen broad-leaved tree species of the Fagaceae family. It is widely distributed in the provinces and regions south of the Yangtze River and north of the five mountains in China. It is regarded as the “Dividing tree” of the climate in the south and north of China. It is important to study the changes in the potential geographical distribution of Castanopsis and its response to climate change for the protection and utilization of its wild resources. 【Method】 In this study, the MaxEnt model was used to simulate and predict the potential distribution areas of Castanopsis during the last glacial maximum, in modern times, and in 2070, and to evaluate the impacts of different climatic factors on its potential geographical distribution. 【Result】 ① The accuracy of the MaxEnt model in simulating the modern distribution area is very high, and the area under the working characteristic curve (AUC value) of the subjects was 0.971. ② The modern day highly suitable area for Castanopsis is mainly the mountainous area of south Anhui, namely the Dabie Mountainous areas, Tianmu Mountain and the hinterland of north Yandang Mountain. The moderately suitable area is located in the mountainous area of southern Jiangsu, namely the coastal area of Jiangxi and Fujian. The distribution during the last glacial maximum was mainly in the south and north of China and east of the continental shelf of the East Sea. It was predicted that in 2070, the distribution of Castanopsis will be affected by climate and the habitat will be fragmented. The highly and moderately suitable areas were predicted to remain in the high-altitude areas. ③ The Jackknife test showed that rainfall and average temperature in the driest season are the most important climatic factors affecting the geographical distribution of Castanopsis sclerophylla. 【Conclusion】 The effects of different climatic factors on the potential geographical distribution of Castanopsis sclerophylla can provide a scientific basis and guidance for provenance protection, habitat restoration, breeding, and domestication of Castanopsis.

Key words: Castanopsis sclerophylla, potential distribution area, MaxEnt model, climate change, phytogeography

中图分类号:

Q948.13

缪菁,王勇,王璐,等. 基于MaxEnt模型的苦槠潜在地理分布格局变迁预测[J]. 南京林业大学学报（自然科学版）, 2021, 45(3): 193-198.

MIAO Jing, WANG Yong, WANG Lu, XU Xiaogang. Prediction of potential geographical distribution pattern change for Castanopsis sclerophylla on MaxEnt[J].Journal of Nanjing Forestry University (Natural Science Edition), 2021, 45(3): 193-198.DOI: 10.12302/j.issn.1000-2006.202008029.

图/表 3

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编号 No.	环境变量 environmental variable	贡献率/% contribution rate	置换重要值/% replace important value
bio1	年均气温 mean annual air temperature	3.8	1.0
bio2	昼夜温差 mean diurnal range[mean of monthly (max-min temperature)]	10.6	1.8
bio3	等温性 isothermality	0.2	1.1
bio4	温度季节变化方差 SD of temperature seasonality	6.2	21.9
bio5	极端最高气温 extreme maximum air temperature	0.1	0.2
bio8	最湿季度平均气温 mean air temperature of the wettest quarter	0.3	3.1
bio9	最干季平均气温 mean air temperature of the driest quarter	2.4	12.2
bio12	年均降水量 annual precipitation	0.3	2.4
bio15	降水变异系数 coefficient of variation of precipitation seasonality	1.3	2.8
bio17	最干季降水量 precipitation of the driest quarter	74.8	53.4

基于MaxEnt模型的苦槠潜在地理分布格局变迁预测

Prediction of potential geographical distribution pattern change for Castanopsis sclerophylla on MaxEnt

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